Ubiquitous and Pervasive Computing: Concepts, Methodologies, Tools, and Applications [1 ed.] 1605669601, 9781605669601

Table of contents :
Title
......Page 2
Editorial Advisory Board......Page 4
List of Contributors......Page 6
Contents......Page 13
Preface......Page 28
Ubiquitous and Pervasive Computing......Page 33
About the Editor......Page 57
Section I
Fundamental Concepts
and Theories......Page 59
Introduction to Ubiquitous
Computing......Page 60
Ubiquitous Computing History,
Development, and Scenarios......Page 79
The Ubiquitous Portal......Page 87
The Ubiquitous Grid......Page 94
RFID Technologies
and Applications......Page 103
Understanding RFID (Radio
Frequency Identification)......Page 113
Radio Frequency Identification
History and Development......Page 124
Automated Data Capture
Technologies......Page 141
Contactless Payment with RFID
and NFC......Page 171
Ambient Intelligence......Page 180
Ambient Intelligence
in Perspective......Page 188
Ambient Intelligence
Environments......Page 196
On Ambient Information Systems......Page 204
Basics of Ubiquitous Networking......Page 215
Networked Appliances
and Home Networking......Page 230
Section II
Development and Design
Methodologies......Page 240
Ubiquitous and Pervasive
Application Design......Page 241
When Ubiquitous Computing
Meets Experience Design......Page 250
Designing Ubiquitous Content
for Daily Lifestyle......Page 265
Designing Pervasive and
Multimodal Interactive Systems......Page 271
Pervasive Computing......Page 293
Developing User Interfaces for
Community-Oriented Workflow
Information Systems......Page 312
An Adaptable Context
Management Framework for
Pervasive Computing......Page 335
Incorporating Human Factors in
the Development of
Context-Aware Personalized
Applications......Page 368
Deploying Ubiquitous
Computing Applications on
Heterogeneous Next Generation
Networks......Page 389
Convergence Broadcast and
Telecommunication Services......Page 412
Designing a Ubiquitous
Audio-Based Memory Aid......Page 429
A Navigational Aid for Blind
Pedestrians Designed with
User- and Activity-Centered
Approaches......Page 448
Model-Driven Development for
Pervasive Information Systems......Page 467
The Design Space of Ubiquitous
Mobile Input......Page 498
A Methodology for the Design,
Development and Validation of
Adaptive and Context-Aware
Mobile Services......Page 521
Context-Aware Services for
Ambient Environments......Page 547
Section III
Tools and Technologies......Page 561
Deploying Pervasive
Technologies......Page 562
Embedding Ubiquitous
Technologies......Page 570
Ubiquitous Computing
Technologies in Education......Page 579
Mobile and Pervasive
Technology in Education
and Training......Page 583
A SCORM Compliant
Courseware Authoring Tool for
Supporting Pervasive Learning......Page 616
Realizing the Promise of RFID......Page 640
The Little Chip
That Could......Page 653
Web & RFId Technology......Page 682
Smart Antennas for Automatic
Radio Frequency Identification
Readers......Page 707
Getting to Know Social
Television......Page 737
Pervasive iTV and Creative
Networked Multimedia Systems......Page 766
Ambient Media and Home
Entertainment......Page 776
TeleTables and Window Seat......Page 789
Interactive Tables......Page 800
Section IV
Utilization and Application......Page 822
Pervasive Healthcare......Page 823
Intelligent Agent Framework for
Secure Patient-Doctor Profiling
and Profile Matching......Page 841
Using RFID to Track and Trace
High Value Products......Page 861
Implementing RFID Technology
in Hospital Environments......Page 874
RFID as the Critical Factor for
Superior Healthcare Delivery......Page 882
An Ambient Intelligence
Based Multi-Agent System for
Alzheimer Health Care......Page 892
Ubiquitous Healthcare......Page 904
Ubiquitous Risk Analysis of
Physiological Data......Page 912
RFID in Healthcare......Page 926
Tapping into Digital Literacy......Page 945
Internet-Enabled User
Interfaces for Distance Learning......Page 964
Handhelds for Digital Libraries......Page 990
South Korea......Page 1000
Ubiquitous Computing for
Microbial Forensics and
Bioterrorism......Page 1016
Section V
Organizational and
Social Implications......Page 1032
Cultural Dimension in the
Future of Pervasive Computing......Page 1033
Outline of the Human Factor
Elements Evident with Pervasive
Computers......Page 1052
Adapting to the User......Page 1065
How Research can Help to
Create Commercially Successful
Ubiquitous Services......Page 1080
Knowledge Sharing and
Pervasive Computing......Page 1098
Ubiquitous Communication......Page 1111
Identity Management for
Wireless Service Access......Page 1126
Inscribing Interpretive
Flexibility of Context Data in
Ubiquitous Computing
Environments......Page 1138
Consumer Attitudes toward
RFID Usage......Page 1157
Determinants of User
Acceptance for RFID
Ticketing Systems......Page 1165
An Empirical Study of Factors
Affecting RFID’s Adoption in
Taiwan......Page 1181
Impact of RFID Technology on
Health Care Organizations......Page 1203
Learning by Pervasive Gaming......Page 1215
Using Mobile and Pervasive
Technologies to Engage
Formal and Informal Learners
in Scientific Debate......Page 1238
Pervasive Business
Infrastructure......Page 1257
Decision Analysis for Business
to Adopt RFID......Page 1277
Intelligent Supply Chain
Management with Automatic
Identification Technology......Page 1286
When Does RFID Make
Business Sense for Managing
Supply Chains?......Page 1308
RFID and Supply Chain
Visibility......Page 1342
Security and Reliability of RFID
Technology in Supply Chain
Management......Page 1351
Recognizing RFID as a
Disruptive Technology......Page 1359
Ubiquitous Connectivity &
Work-Related Stress......Page 1373
Bridging the Gap between
Employee Surveillance
and Privacy Protection......Page 1389
Section VII
Critical Issues......Page 1407
The Ethical Debate
Surrounding RFID......Page 1408
Privacy Issues of Applying
RFID in Retail Industry......Page 1416
An Evaluation of the RFID
Security Benefits of the APF
System......Page 1432
Security and Privacy in RFID
Based Wireless Networks......Page 1444
Humans and Emerging RFID
Systems......Page 1454
Privacy Factors for Successful
Ubiquitous Computing......Page 1466
Privacy Threats in Emerging
Ubicomp Applications......Page 1483
Deciphering Pervasive
Computing......Page 1508
Privacy Control Requirements
for Context-Aware Mobile
Services......Page 1523
Access Control in Mobile and
Ubiquitous Environments......Page 1539
Warranting High Perceived
Quality of Experience (PQoE) in
Pervasive Interactive Multimedia
Systems......Page 1556
Pervasive and Ubiquitous
Computing Databases......Page 1575
Adaptive Resource
and Service Management in a
Mobile-Enabled Environment......Page 1585
Service-Oriented Architectures
for Context-Aware Information
Retrieval and Access......Page 1607
Section VIII
Emerging Trends......Page 1619
Ambient Learning......Page 1620
Plastic Interfaces for
Ubiquitous Learning......Page 1640
u-City......Page 1659
Planning for Knowledge
Cities in Ubiquitous
Technology Spaces......Page 1671
Emotional Ambient Media......Page 1684
Leveraging Semantic
Technologies towards Social
Ambient Intelligence......Page 1701
From E to U......Page 1727
Ubiquitous Services and
Business Processes......Page 1746
Ambient Intelligence on the
Dance Floor......Page 1778
Adaptive Narration in
Multiplayer Ubiquitous Games......Page 1796
Concept of Symbiotic
Computing and its Agent-Based
Application to a Ubiquitous
Care-Support Service......Page 1820
Adaptive Awareness of
Hospital Patient Information
through Multiple
Sentient Displays......Page 1844
Index......Page 1857

Citation preview

Ubiquitous and Pervasive Computing: Concepts, Methodologies, Tools, and Applications Judith Symonds Auckland University of Technology, New Zealand

Volume I

InformatIon scIence reference Hershey • New York

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Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanbookstore.com Copyright © 2010 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Ubiquitous and pervasive computing : concepts, methodologies, tools, and applications / Judith Symonds, editor. p. cm. Includes bibliographical references and index. Summary: "This publication covers the latest innovative research findings involved with the incorporation of technologies into everyday aspects of life"--Provided by publisher. ISBN 978-1-60566-960-1 (hardcover) -- ISBN 978-1-60566-961-8 (ebook) 1. Ubiquitous computing. I. Symonds, Judith, 1972QA76.5915.U2565 2009 004--dc22 2009031090

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Editor-in-Chief Mehdi Khosrow-Pour, DBA Editor-in-Chief Contemporary Research in Information Science and Technology, Book Series

Associate Editors Steve Clarke University of Hull, UK Murray E. Jennex San Diego State University, USA Annie Becker Florida Institute of Technology USA Ari-Veikko Anttiroiko University of Tampere, Finland

Editorial Advisory Board Sherif Kamel American University in Cairo, Egypt In Lee Western Illinois University, USA Jerzy Kisielnicki Warsaw University, Poland Keng Siau University of Nebraska-Lincoln, USA Amar Gupta Arizona University, USA Craig van Slyke University of Central Florida, USA John Wang Montclair State University, USA Vishanth Weerakkody Brunel University, UK

Additional Research Collections found in the “Contemporary Research in Information Science and Technology” Book Series Data Mining and Warehousing: Concepts, Methodologies, Tools, and Applications John Wang, Montclair University, USA • 6-volume set • ISBN 978-1-60566-056-1 Electronic Business: Concepts, Methodologies, Tools, and Applications In Lee, Western Illinois University • 4-volume set • ISBN 978-1-59904-943-4 Electronic Commerce: Concepts, Methodologies, Tools, and Applications S. Ann Becker, Florida Institute of Technology, USA • 4-volume set • ISBN 978-1-59904-943-4 Electronic Government: Concepts, Methodologies, Tools, and Applications Ari-Veikko Anttiroiko, University of Tampere, Finland • 6-volume set • ISBN 978-1-59904-947-2 Knowledge Management: Concepts, Methodologies, Tools, and Applications Murray E. Jennex, San Diego State University, USA • 6-volume set • ISBN 978-1-59904-933-5 Information Communication Technologies: Concepts, Methodologies, Tools, and Applications Craig Van Slyke, University of Central Florida, USA • 6-volume set • ISBN 978-1-59904-949-6 Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications Vijayan Sugumaran, Oakland University, USA • 4-volume set • ISBN 978-1-59904-941-0 Information Security and Ethics: Concepts, Methodologies, Tools, and Applications Hamid Nemati, The University of North Carolina at Greensboro, USA • 6-volume set • ISBN 978-1-59904-937-3 Medical Informatics: Concepts, Methodologies, Tools, and Applications Joseph Tan, Wayne State University, USA • 4-volume set • ISBN 978-1-60566-050-9 Mobile Computing: Concepts, Methodologies, Tools, and Applications David Taniar, Monash University, Australia • 6-volume set • ISBN 978-1-60566-054-7 Multimedia Technologies: Concepts, Methodologies, Tools, and Applications Syed Mahbubur Rahman, Minnesota State University, Mankato, USA • 3-volume set • ISBN 978-1-60566-054-7 Virtual Technologies: Concepts, Methodologies, Tools, and Applications Jerzy Kisielnicki, Warsaw University, Poland • 3-volume set • ISBN 978-1-59904-955-7

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List of Contributors

Abowd, Gregory D. \ Georgia Institute of Technology, USA ............................................................ 370 Abuelma’atti, Omar \ Liverpool John Moores University, UK ........................................................ 171 Ahn, David \ Nyack College, USA ................................................................................................... 1358 Ahonen, Pasi \ VTT Technical Research Centre of Finland, Finland .............................................. 1425 Alahuhta, Petteri \ VTT Technical Research Centre of Finland, Finland ....................................... 1425 Andersson, Magnus \ Viktoria Institute, Sweden ............................................................................ 1079 Angelopoulos, Spyros P. \ Technical University of Crete, Greece .................................................. 1669 Aparajita, Upali \ Utkal University, India......................................................................................... 974 Apiletti, Daniele \ Politecnico di Torino, Italy .................................................................................. 853 Ayoade, John \ American University of Nigeria, Nigeria................................................................ 1374 Babulak, Eduard \ Fairleigh Dickinson University, Canada ......................................................... 1669 Bakhouya, M. \ The George Washington University, Washington DC, USA..................................... 182 Ballagas, Rafael \ RWTH Aachen University, Germany .................................................................... 439 Bang, Jounghae \ Penn State University Mont Alto, USA ................................................................. 941 Baralis, Elena \ Politecnico di Torino, Italy ...................................................................................... 853 Barricelli, Barbara R. \ Università degli Studi di Milano, Italy ....................................................... 212 Barros, Alistair \ SAP Research, Australia ...................................................................................... 1688 Bataille, Fabien \ Alcatel-Lucent Bell Labs, France ....................................................................... 1643 Berbegal, Nídia \ Universitat Pompeu Fabra, Spain......................................................................... 353 Berzunza, Gustavo \ CICESE, Mexico............................................................................................ 1786 Billinghurst, Mark \ Human Interface Technology Laboratory New Zealand– University of Canterbury, New Zealand .......................................................................................... 741 Borchers, Jan \ RWTH Aachen University, Germany........................................................................ 439 Boslau, Madlen \ Georg-August-Universität Göttingen, Germany ........................................... 44, 1098 Boye, Niels \ University of Aalborg, Denmark ................................................................................... 764 Briffault, Xavier \ CESAMES UMR 8136, Université René-Descartes Paris V, France .................. 389 Briggs, Pam \ Northumbria University, UK..................................................................................... 1408 Brook, Phillip W J \ University of Western Sydney, Australia ........................................................ 1039 Bruno, Giulia \ Politecnico di Torino, Italy ....................................................................................... 853 Butcher, T. \ University of Hull Logistics Institute (UHLI), UK ........................................................ 823 Buyurgan, Nebil \ University of Arkansas, USA ............................................................................... 867 Byrne, Caroline \ Institute of Technology Carlow, Ireland ............................................................... 129 Calafate, Carlos Tavares \ Technical University of Valencia, Spain ................................................ 503 Calleros, Juan Manuel González \ Université catholique de Louvain, Louvain School of Management (LSM), Belgium ............................................................................ 253

Camarata, Ken \ KDF Architecture, USA ......................................................................................... 730 Cano, Jose \ Technical University of Valencia, Spain ........................................................................ 503 Cano, Juan-Carlos \ Technical University of Valencia, Spain .......................................................... 503 Carvalho, João Álvaro \ University of Minho, Portugal .................................................................. 408 Cerquitelli, Tania \ Politecnico di Torino, Italy ................................................................................ 853 Chamberlain, Alan \ University of Nottingham, UK....................................................................... 1179 Chang, Elizabeth \ Curtin University of Technology, Australia .......................................................... 82 Chang, Flora Chia-I \ Tamkang University, China ........................................................................... 557 Chang, She-I \ National Chung Cheng University, Taiwan ............................................................. 1122 Chen, Yen-Jung \ National University of Tainan, Taiwan ................................................................. 520 Cheok, Adrian David \ National University of Singapore, Singapore .............................................. 905 Chiu, Yuh-Wen \ National Yunlin University of Science & Technology, Taiwan ............................ 1122 Choi, Inyoung \ Georgetown University, USA................................................................................... 941 Choi, Yongsoon \ National University of Singapore, Singapore........................................................ 905 Chong, Jimmy \ Nanyang Technological University, Singapore......................................................... 20 Chorianopoulos, Konstantinos \ Bauhaus University of Weimar, Germany .................................... 717 Chowdhury, Mohammad M. R. \ University Graduate Center – UniK, Norway ......................... 1067 Clarke, Dave \ GXS, USA .................................................................................................................. 581 Corchado, Juan M. \ Universidad de Salamanca, Spain .................................................................. 833 Coyle, Lorcan \ University College Dublin, Ireland ......................................................................... 145 Crellin, David \ Abingtom Partners, UK ......................................................................................... 1179 Cuozzo, Félix \ ENSICAEN, France .................................................................................................. 112 Dillon, Teresa \ Polar Produce, UK ................................................................................................. 1179 Do, Ellen Yi-Luen \ Georgia Institute of Technology, USA ............................................................... 730 Dorsch, Tillmann \ Tampere University of Technology, Finland .................................................... 1626 Duh, Henry B. L. \ Nanyang Technological University, Singapore .................................................... 20 Dwivedi, A. \ University of Hull, UK ................................................................................................. 823 Edegger, Francika \ evolaris Privatstiftung, Austria ...................................................................... 1156 Eikerling, Heinz-Josef \ Siemens AG SIS C-LAB, Germany ............................................................. 462 Eldin, Amr Ali \ Accenture BV, The Netherlands............................................................................. 1465 El-Nasr, Magy Seif \ Penn State University, USA ........................................................................... 1720 Elwood, Susan A. \ Texas A&M University, Corpus Christi, USA .................................................... 511 Etter, Stephanie \ Mount Aloysius College, USA ............................................................................ 1350 Favela, Jesus \ CICESE, Mexico...................................................................................................... 1786 Fergus, Paul \ Liverpool John Moores University, UK ..................................................................... 171 Fernandes, José Eduardo \ Bragança Polytechnic Institute, Portugal ............................................ 408 Fraser, Danaë Stanton \ University of Bath, UK ............................................................................ 1179 Friedewald, Michael \ Fraunhofer Institute Systems and Innovation Research, Germany ............ 1425 Gaber, J. \ Université de Technologie de Belfort-Montbéliard, France ............................................ 182 Galambosi, Agnes \ The University of North Carolina at Charlotte, USA...................................... 1250 García, Josefina Guerrero \ Université catholique de Louvain, Louvain School of Management (LSM), Belgium ............................................................................ 253 Garg, Miti \ The Logistics Institute – Asia Pacific, Singapore ........................................................ 1284 Gaunet, Florence \ Laboratoire Eco-Anthropologie et Ethnobiologie UMR 5145, CNRS, France .. 389 Germanakos, Panagiotis \ National & Kapodistrian University of Athens, Greece ........................ 309 Glambedakis, Antony \ University of Western Sydney, Australia ..................................................... 993

Glancy, Maxine \ BBC Research & Innovation, UK ....................................................................... 1179 Godara, Varuna \ University of Western Sydney, Australia .................................................... 234, 1199 Goh, Mark \ NUS Business School, The Logistics Institute – Asia Pacific, Singapore ................... 1284 Gomez, Laurent \ SAP Research, France ....................................................................................... 1481 Gosain, Sanjay \ The Capital Group Companies, USA ..................................................................... 581 Gower, Amanda \ BT Innovate, UK................................................................................................. 1179 Gower, Andrew \ BT Innovate, UK.................................................................................................. 1179 Gross, Mark D. \ Carnegie Mellon University, USA ......................................................................... 730 Gupta, Sumeet \ Shri Sankaracarya Institute of Management and Technology, India ................... 1284 Gurevych, Iryna \ Technische Universität Darmstadt, Germany ......................................................... 1 Gutiérrez, Jairo A. \ University of Auckland, New Zealand ................................................... 156, 1301 Hagenhoff, Svenja \ Georg-August-Universität Göttingen, Germany ................................................ 44 Hair, M. \ University of the West of Scotland, UK ........................................................................... 1315 Haller, Michael \ Upper Austria University of Applied Sciences–Digital Media, Austria ................ 741 Hallikas, Jukka \ Lappeenranta University of Technology, Finland............................................... 1052 Harboe, Gunnar \ Motorola, USA ..................................................................................................... 678 Hardgrave, Bill C. \ University of Arkansas, USA ............................................................................ 867 Hattori, Fumio \ Ritsumeikan University, Japan ............................................................................. 1762 Hayes, Gillian R. \ Georgia Institute of Technology, USA................................................................. 370 Hazlewood, William R. \ Indiana University Bloomington, USA ..................................................... 145 Huang, Elaine \ Motorola, USA ......................................................................................................... 678 Humanes, Pabo Roman \ Tampere University of Technology, Finland .......................................... 1626 Hung, Patrick C. K. \ University of Ontario Institute of Technology, Canada ............................... 1358 Hwang, Gwo-Jen \ National University of Tainan, Taiwan .............................................................. 520 Hwang, Jong-Sung \ National Information Society Agency, Korea ................................................ 1601 Iachello, Giovanni \ Georgia Institute of Technology, USA .............................................................. 370 Inakage, Masa \ Keio University, Japan............................................................................................ 206 Jentzsch, Ric \ Compucat Research Pty Limited, Australia .............................................................. 782 Johnson, Stephen \ Mobility Research Centre, UK ........................................................................... 707 Joly, Adrien \ Alcatel-Lucent Bell Labs, France Alcatel-Lucent Bell Labs, France & Universite deLyon, LIRIS/INSA, France ................................................................................... 1643 Jöst, Matthias \ European Media Laboratory GmbH, Germany..................................................... 1006 Kallenbach, Jan \ Helsinki University of Technology, Finland ......................................................... 717 Kameas, Achilles D. \ Hellenic Open University and Computer Technology Institute / DAISy group, Greece ....................................................................................................................... 330 Karaiskos, Dimitrios C. \ Athens University of Business and Economics, Greece ........................ 1106 Karmakar, Nemai Chandra \ Monash University, Australia ........................................................... 648 Karyda, Maria \ University of the Aegean, Greece ......................................................................... 1331 Kaspar, Christian \ Georg-August-Universität Göttingen, Germany ................................................. 44 Katsumoto, Yuichiro \ Keio University, Japan ................................................................................. 206 Kehoe, Dennis \ University of Liverpool, UK .................................................................................. 1228 Kientz, Julie A. \ Georgia Institute of Technology, USA ................................................................... 370 Kim, Tschangho John \ University of Illinois at Urbana-Champaign, USA .................................. 1613 Kinoshita, Tetsuo \ Tohoku University, Japan ................................................................................. 1762 Kitsios, Fotis C. \ Technical University of Crete, Greece ................................................................ 1669 Kittl, Christian \ evolaris Privatstiftung, Austria & Karl-Franzens University, Austria ................ 1156

Knuth, Peter \ Technical University of Košice, Slovakia ................................................................ 1293 Koh, Sze Ling \ Nanyang Technological University, Singapore.......................................................... 20 Koivumäki, Timo \ VTT Technical Research Centre of Finland, Finland ...................................... 1021 Koskela, Kaisa \ University of Oulu, Finland ................................................................................. 1021 Kourouthanassis, Panayiotis E. \ Athens University of Business and Economics, Greece............ 1106 Laube, Annett \ SAP Research, France ........................................................................................... 1481 LeDonne, Keith \ Robert Morris University, USA........................................................................... 1350 Lee, Cheon-Pyo \ Carson-Newman College, USA ............................................................................ 845 Lee, Deirdre \ Trinity College Dublin, Ireland .................................................................................. 488 Lee, Mark J. W. \ Charles Sturt University, Australia ...................................................................... 524 Lekkas, Zacharias \ National & Kapodistrian University of Athens, Greece................................... 309 Leu, Huei \ Industrial Technology Research Institute, Taiwan ........................................................ 1219 Li, Dong \ University of Liverpool, UK............................................................................................ 1228 Li, Grace \ University of Technology, Sydney, Australia ................................................................. 1450 Li, Haifei \ Union University, USA .................................................................................................. 1358 Lietke, Britta \ Georg-August-Universität Göttingen, Germany............................................... 44, 1098 Lin, Chad \ Edith Cowan University, Australia ............................................................................... 1219 Lin, Koong \ Tainan National University of the Arts, Taiwan ......................................................... 1219 Lindgren, Rikard \ University of Gothenburg, Sweden & Viktoria Institute, Sweden .................... 1079 Little, Linda \ Northumbria University, UK .................................................................................... 1408 Littman, Marlyn Kemper \ Nova Southeastern University, USA .................................................... 815 Liu, Kinchung \ University of Liverpool, UK.................................................................................. 1228 Liu, Wei \ National University of Singapore, Singapore ................................................................... 905 Lo, Janice \ Baylor University, USA .................................................................................................. 867 Lugmayr, Artur \ Tampere University of Technology, Finland ............................................... 717, 1626 Lyardet, Fernando \ Technische Universität Darmstadt, Germany................................................ 1562 Machado, Ricardo J. \ University of Minho, Portugal ..................................................................... 408 Mangaraj, B.K. \ XLRI Jamshepur, School of Business and Human Resources, Jamshedpur, India ............................................................................................................................ 974 Manzoni, Pietro \ Technical University of Valencia, Spain ............................................................... 503 Marcante, Andrea \ Università degli Studi di Milano, Italy ............................................................. 212 Maret, Pierre \ Université de Lyon, France .................................................................................... 1643 Martin, Patrick \ Queen’s University, Canada.................................................................................. 276 Massey, Noel \ Motorola, USA ........................................................................................................... 678 Mazzoleni, Pietro \ IBM Watson Research, USA ............................................................................... 462 Meier, René \ Trinity College Dublin, Ireland ................................................................................... 488 Mei-Ling, Charissa Lim \ Nanyang Technological University, Singapore....................................... 905 Melski, Adam \ Georg-August-Universität Göttingen, Germany ........................................................ 44 Memmola, Massimo \ Catholic University, Italy .............................................................................. 623 Merabti, Madjid \ Liverpool John Moores University, UK .............................................................. 171 Metcalf, Crysta \ Motorola, USA ...................................................................................................... 678 Mikkonen, Karri \ TeliaSonera, Sweden ......................................................................................... 1052 Minakshi, \ CCS Haryana Agricultural University, India ................................................................ 957 Mitrou, Lilian \ University of the Aegean, Greece .......................................................................... 1331 Modrák, Vladimír \ Technical University of Košice, Slovakia ....................................................... 1293 Mohammadian, Masoud \ University of Canberra, Australia ......................................................... 782

Molinero, Ashli M. \ Robert Morris University, USA...................................................................... 1350 Mourlas, Constantinos \ National & Kapodistrian University of Athens, Greece ........................... 309 Mühlhäuser, Max \ Technische Universität Darmstadt, Germany................................................ 1, 717 Mulder, Ingrid \ Telematica Instituut and Rotterdam University, The Netherlands.......................... 191 Mussio, Piero \ Università degli Studi di Milano, Italy ..................................................................... 212 Nabelsi, Véronique \ École Polytechnique de Montréal, Canada................................................... 1144 Natkin, Stéphane \ Conservatoire National des Arts et Métiers, Pans, France.............................. 1738 Navarro-Prieto, Raquel \ Fundació Barcelona Media, Spain .......................................................... 353 Nestor, Susan J. \ Robert Morris University, USA .......................................................................... 1350 Nguyen, Ta Huynh Duy \ National University of Singapore, Singapore .......................................... 905 Niemelä, Marketta \ VTT Technical Research Centre of Finland, Finland .................................... 1396 Noll, Josef \ University Graduate Center – UniK, Norway ............................................................. 1067 Novak, Ashley \ Motorola, USA ......................................................................................................... 678 O’Grady, Michael \ University College Dublin, Ireland .................................................................. 129 O’Hare, Gregory \ University College Dublin, Ireland .................................................................... 129 Oh, Yeonjoo \ Carnegie Mellon University, USA .............................................................................. 730 Özelkan, Ertunga C. \ The University of North Carolina at Charlotte, USA ................................. 1250 Padula, Marco \ Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy........................................................................................... 212 Palo, Teea \ University of Oulu, Finland.......................................................................................... 1021 Palumbo, Giovanna \ Ospedale Valduce, Italy ................................................................................. 623 Papatheodorou, Christos \ Ionian University, Greece...................................................................... 931 Park, Kevin \ University of Auckland, New Zealand ......................................................................... 156 Parry, David \ Auckland University of Technology, New Zealand .................................................... 802 Pasquet, Marc \ GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie - CNRS), France .............................................................................................................................. 112 Patel, Shwetak N. \ Georgia Institute of Technology, USA ............................................................... 370 Peiris, Roshan \ National University of Singapore, Singapore ......................................................... 905 Petrovic, Otto \ evolaris Privatstiftung, Austria & Karl-Franzens University, Austria .................. 1156 Phillips, Patricia G. \ Duquesne University, USA ........................................................................... 1350 Pitkänen, Olli \ Helsinki Institute for Information Technology (HIIT), Finland ............................. 1396 Pohl, Alexandra \ Berlin-Brandenburg (rbb) Innovationsprojekte, Germany .................................. 717 Potdar, Vidyasagar \ Curtin University of Technology, Australia....................................................... 82 Powley, Wendy \ Queen’s University, Canada................................................................................... 276 Prasad, Gaya \ CCS Haryana Agricultural University, India ........................................................... 957 Provenza, Loredana Parasiliti \ Università degli Studi di Milano, Italy ......................................... 212 Pynnönen, Mikko \ Lappeenranta University of Technology, Finland ........................................... 1052 Qui, Tran Cong Thien \ National University of Singapore, Singapore ............................................ 905 Raibulet, Claudia \ Universitá degli Studi di Milano-Bicocca, Italy .............................................. 1527 Ramos, Carlos \ Polytechnic of Porto, Portugal ............................................................................... 137 Ramsay, J. \ University of the West of Scotland, UK ....................................................................... 1315 Rantakokko, Tapani \ Finwe LTD, Finland.................................................................................... 1425 Renaud, K. V. \ University of Glasgow, UK .................................................................................... 1315 Reynaud, Joan \ GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie - CNRS), France .............................................................................................................................. 112 Rohs, Michael \ Deutsche Telekom Laboratories, Germany ............................................................. 439

Roibás, Anxo Cereijo \ SCMIS, University of Brighton, UK .................................................. 707, 1498 Romano, Guy \ Motorola, USA ......................................................................................................... 678 Rossini, Mauro \ Ospedale Valduce, Italy ......................................................................................... 623 Rouillard, José \ Laboratoire LIFL - Université de Lille 1, France................................................ 1582 Roussos, George \ University of London, UK.................................................................................. 1517 Sadri, Fariba \ Imperial College London, UK................................................................................... 121 Samaras, George \ University of Cyprus, Cyprus ............................................................................. 309 Savolainen, Petri \ Lappeenranta University of Technology, Finland ............................................ 1052 Scala, Paolo L. \ Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerchev, Italy ...................................................................................... 212 Seah, Lily Leng-Hiang \ Nanyang Technological University, Singapore ........................................... 20 Sedlar, Patricia \ Johannes Kepler University, Austria ....................................................................... 35 See, Stanley \ Nanyang Technological University, Singapore ............................................................. 20 Segura, Daniela \ CICESE, Mexico ................................................................................................. 1786 Sheridan, Jennifer G. \ BigDog Interactive Ltd., UK ....................................................................... 439 Shih, Dong-Her \ National Yunlin University of Science & Technology, Taiwan............................ 1122 Shim, J. P. \ Mississippi State University, USA .................................................................................. 845 Shiratori, Norio \ Tohoku University, Japan ................................................................................... 1762 Soon, Chin-Boo \ The University of Auckland, New Zealand ................................................... 65, 1301 Sorniotti, Alessandro \ SAP Research, France ............................................................................... 1481 Stathis, Kostas \ Royal Holloway, University of London, UK ........................................................... 121 Stefanescu, Florina \ ePoly Centre of Expertise in Electronic Commerce, Canada ....................... 1144 Stojanovic, Zoran \ IBM Nederland BV, The Netherlands .............................................................. 1465 Suganuma, Takuo \ Tohoku University, Japan ................................................................................ 1762 Sugawara, Kenji \ Chiba Institute of Technology, Japan ................................................................ 1762 Symonds, Judith A. \ Auckland University of Technology, New Zealand ............................... 802, 1374 Tähtinen, Jaana \ University of Oulu, Finland ............................................................................... 1021 Tapia, Dante I. \ Universidad de Salamanca, Spain.......................................................................... 833 Tatnall, Arthur \ Victoria University, Australia .................................................................................. 28 Teh, Keng Soon \ National University of Singapore, Singapore ....................................................... 905 Tentori, Mónica \ CICESE and Universidad Autónoma de Baja California, Mexico..................... 1786 Terrenghi, Lucia \ Vodafone Group R&D, Germany ........................................................................ 191 Theng, Yin-Leng \ Nanyang Technological University, Singapore ............................................. 20, 905 Thillairajah, Velan \ EAI Technologies, USA .................................................................................... 581 Tokuhisa, Satoru \ Keio University, Japan ....................................................................................... 206 Trček, Denis \ University of Ljubljana, Slovenia ............................................................................. 1386 Truong, Khai N. \ University of Toronto, Canada ............................................................................. 370 Tsakonas, Giannis \ Ionian University, Greece ................................................................................. 931 Tsianos, Nikos \ National & Kapodistrian University of Athens, Greece .......................................... 309 Tullio, Joe \ Motorola, USA ............................................................................................................... 678 Ueki, Atsuro \ Keio University, Japan ............................................................................................... 206 Vacquez, Delphine \ ENSICAEN, France.......................................................................................... 112 van ‘t Hooft, Mark \ Kent State University, USA .............................................................................. 886 Vanderdonckt, Jean \ Université catholique de Louvain, Louvain School of Management (LSM), Belgium ............................................................................ 253 Vasilakos, Athanasios V. \ University of Peloponnese, Greece ............................................... 905, 1720

Veronikis, Spyros \ Ionian University, Greece .................................................................................. 931 Vildjiounaite, Elena \ VTT Technical Research Centre of Finland, Finland .................................. 1425 Vowels, Susan A. \ Washington College, USA ..................................................................................... 54 Walker, Ronald T. \ University of Arkansas, USA ............................................................................ 867 Wang, Te-Hua \ Tamkang University, China ..................................................................................... 557 Wang, Xiaojun \ University of Liverpool, UK ................................................................................. 1228 Watson, Genevieve \ University of Western Sydney, Australia.......................................................... 993 Weller, Michael Philetus \ Carnegie Mellon University, USA .......................................................... 730 Wilson, Kirk \ CA Inc., Canada ........................................................................................................ 276 Woodgate, Dawn \ University of Bath, UK ..................................................................................... 1179 Wright, David \ Trilateral Research and Consulting, UK............................................................... 1425 Wu, Chen \ Curtin University of Technology, Australia ...................................................................... 82 Wu, Ting-Ting \ National University of Tainan, Taiwan ................................................................... 520 Wyld, David C. \ Southeastern Louisiana University, USA .............................................................. 594 Xu, Heng \ The Pennsylvania State University, USA ....................................................................... 1284 Yan, Chen \ Conservatoire National des Arts et Métiers, Pans, France ......................................... 1738 Yan, Lu \ University College London, UK ....................................................................................... 1549 Yen, David C. \ Miami University, USA........................................................................................... 1122 Zebedee, Jared \ Queen’s University, Canada................................................................................... 276 Zhang, Jia \ Northern Illinois University, USA ............................................................................... 1358 Zoumboulakis, Michael \ University of London, UK ..................................................................... 1517

Contents

Volume I Section I. Fundamental Concepts and Theories This section serves as the foundation for this exhaustive reference source by addressing crucial theories essential to the understanding of ubiquitous and pervasive computing. Chapters found within this section provide a framework in which to position ubiquitous and pervasive tools and technologies within the field of information science and technology. Individual contributions provide overviews of ubiquitous grids, ambient intelligence, ubiquitous networking, and radio frequency identification (RFID). Within this introductory section, the reader can learn and choose from a compendium of expert research on the elemental theories underscoring the research and application of ubiquitous and pervasive computing. Chapter 1.1. Introduction to Ubiquitous Computing............................................................................... 1 Max Mühlhäuser, Technische Universität Darmstadt, Germany Iryna Gurevych, Technische Universität Darmstadt, Germany Chapter 1.2. Ubiquitous Computing History, Development, and Scenarios......................................... 20 Jimmy Chong, Nanyang Technological University, Singapore Stanley See, Nanyang Technological University, Singapore Lily Leng-Hiang Seah, Nanyang Technological University, Singapore Sze Ling Koh, Nanyang Technological University, Singapore Yin-Leng Theng, Nanyang Technological University, Singapore Henry B. L. Duh, Nanyang Technological University, Singapore Chapter 1.3. The Ubiquitous Portal....................................................................................................... 28 Arthur Tatnall, Victoria University, Australia Chapter 1.4. The Ubiquitous Grid.......................................................................................................... 35 Patricia Sedlar, Johannes Kepler University, Austria

Chapter 1.5. RFID Technologies and Applications................................................................................ 44 Christian Kaspar, Georg-August-Universität Göttingen, Germany Adam Melski, Georg-August-Universität Göttingen, Germany Britta Lietke, Georg-August-Universität Göttingen, Germany Madlen Boslau, Georg-August-Universität Göttingen, Germany Svenja Hagenhoff, Georg-August-Universität Göttingen, Germany Chapter 1.6. Understanding RFID (Radio Frequency Identification).................................................... 54 Susan A. Vowels, Washington College, USA Chapter 1.7. Radio Frequency Identification History and Development............................................... 65 Chin-Boo Soon, The University of Auckland, New Zealand Chapter 1.8. Automated Data Capture Technologies: RFID.................................................................. 82 Vidyasagar Potdar, Curtin University of Technology, Australia Chen Wu, Curtin University of Technology, Australia Elizabeth Chang, Curtin University of Technology, Australia Chapter 1.9. Contactless Payment with RFID and NFC...................................................................... 112 Marc Pasquet, GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie CNRS), France Delphine Vacquez, ENSICAEN, France Joan Reynaud, GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie CNRS), France Félix Cuozzo, ENSICAEN, France Chapter 1.10. Ambient Intelligence..................................................................................................... 121 Fariba Sadri, Imperial College London, UK Kostas Stathis, Royal Holloway, University of London, UK Chapter 1.11. Ambient Intelligence in Perspective.............................................................................. 129 Caroline Byrne, Institute of Technology Carlow, Ireland Michael O’Grady, University College Dublin, Ireland Gregory O’Hare, University College Dublin, Ireland Chapter 1.12. Ambient Intelligence Environments.............................................................................. 137 Carlos Ramos, Polytechnic of Porto, Portugal Chapter 1.13. On Ambient Information Systems: Challenges of Design and Evaluation................... 145 William R. Hazlewood, Indiana University Bloomington, USA Lorcan Coyle, University College Dublin, Ireland Chapter 1.14. Basics of Ubiquitous Networking................................................................................. 156 Kevin Park, University of Auckland, New Zealand Jairo A. Gutiérrez, University of Auckland, New Zealand

Chapter 1.15. Networked Appliances and Home Networking: Internetworking the Home................ 171 Madjid Merabti, Liverpool John Moores University, UK Paul Fergus, Liverpool John Moores University, UK Omar Abuelma’atti, Liverpool John Moores University, UK Section II. Development and Design Methodologies This section provides in-depth coverage of conceptual architectures, frameworks and methodologies related to the design of ubiquitous and pervasive tools, models, and interfaces. Throughout these contributions, fundamental development methodologies are presented and discussed. From broad examinations to specific discussions of particular frameworks and infrastructures, the research found within this section spans the discipline while also offering detailed, specific discussions. Basic designs, as well as abstract developments, are explained within these chapters, and frameworks for designing successful interactive systems, educational models, and mobile devices are examined. Chapter 2.1. Ubiquitous and Pervasive Application Design................................................................ 182 M. Bakhouya, The George Washington University, Washington DC, USA J. Gaber, Université de Technologie de Belfort-Montbéliard, France Chapter 2.2. When Ubiquitous Computing Meets Experience Design: Identifying Challenges for Design and Evaluation................................................................................................. 191 Ingrid Mulder, Telematica Instituut and Rotterdam University, The Netherlands Lucia Terrenghi, Vodafone Group R&D, Germany Chapter 2.3. Designing Ubiquitous Content for Daily Lifestyle......................................................... 206 Masa Inakage, Keio University, Japan Atsuro Ueki, Keio University, Japan Satoru Tokuhisa, Keio University, Japan Yuichiro Katsumoto, Keio University, Japan Chapter 2.4. Designing Pervasive and Multimodal Interactive Systems: An Approach Built on the Field................................................................................................................................. 212 Barbara R. Barricelli, Università degli Studi di Milano, Italy Piero Mussio, Università degli Studi di Milano, Italy Marco Padula, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy Andrea Marcante, Università degli Studi di Milano, Italy Loredana Parasiliti Provenza, Università degli Studi di Milano, Italy Paolo L. Scala, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerchev, Italy Chapter 2.5. Pervasive Computing: A Conceptual Framework........................................................... 234 Varuna Godara, University of Western Sydney, Australia

Chapter 2.6. Developing User Interfaces for Community-Oriented Workflow Information Systems............................................................................................................................ 253 Josefina Guerrero García, Université catholique de Louvain, Louvain School of Management (LSM), Belgium Jean Vanderdonckt, Université catholique de Louvain, Louvain School of Management (LSM), Belgium Juan Manuel González Calleros, Université catholique de Louvain, Louvain School of Management (LSM), Belgium Chapter 2.7. An Adaptable Context Management Framework for Pervasive Computing................... 276 Jared Zebedee, Queen’s University, Canada Patrick Martin, Queen’s University, Canada Kirk Wilson, CA Inc., Canada Wendy Powley, Queen’s University, Canada Chapter 2.8. Incorporating Human Factors in the Development of Context-Aware Personalized Applications: The Next Generation of Intelligent User Interfaces................................. 309 Nikos Tsianos, National & Kapodistrian University of Athens, Greece Panagiotis Germanakos, National & Kapodistrian University of Athens, Greece Zacharias Lekkas, National & Kapodistrian University of Athens, Greece Constantinos Mourlas, National & Kapodistrian University of Athens, Greece George Samaras, University of Cyprus, Cyprus Chapter 2.9. Deploying Ubiquitous Computing Applications on Heterogeneous Next Generation Networks.................................................................................................................. 330 Achilles D. Kameas, Hellenic Open University and Computer Technology Institute / DAISy group, Greece Chapter 2.10. Convergence Broadcast and Telecommunication Services: What are Real Users’ Needs?............................................................................................................... 353 Raquel Navarro-Prieto, Fundació Barcelona Media, Spain Nídia Berbegal, Universitat Pompeu Fabra, Spain Chapter 2.11. Designing a Ubiquitous Audio-Based Memory Aid...................................................... 370 Shwetak N. Patel, Georgia Institute of Technology, USA Khai N. Truong, University of Toronto, Canada Gillian R. Hayes, Georgia Institute of Technology, USA Giovanni Iachello, Georgia Institute of Technology, USA Julie A. Kientz, Georgia Institute of Technology, USA Gregory D. Abowd, Georgia Institute of Technology, USA Chapter 2.12. A Navigational Aid for Blind Pedestrians Designed with Userand Activity-Centered Approaches...................................................................................................... 389 Florence Gaunet, Laboratoire Eco-Anthropologie et Ethnobiologie UMR 5145, CNRS, France Xavier Briffault, CESAMES UMR 8136, Université René-Descartes Paris V, France

Chapter 2.13. Model-Driven Development for Pervasive Information Systems................................. 408 José Eduardo Fernandes, Bragança Polytechnic Institute, Portugal Ricardo J. Machado, University of Minho, Portugal João Álvaro Carvalho, University of Minho, Portugal Chapter 2.14. The Design Space of Ubiquitous Mobile Input............................................................. 439 Rafael Ballagas, RWTH Aachen University, Germany Michael Rohs, Deutsche Telekom Laboratories, Germany Jennifer G. Sheridan, BigDog Interactive Ltd., UK Jan Borchers, RWTH Aachen University, Germany Chapter 2.15. A Methodology for the Design, Development and Validation of Adaptive and Context-Aware Mobile Services................................................................................................... 462 Heinz-Josef Eikerling, Siemens AG SIS C-LAB, Germany Pietro Mazzoleni, IBM Watson Research, USA Chapter 2.16. Context-Aware Services for Ambient Environments.................................................... 488 René Meier, Trinity College Dublin, Ireland Deirdre Lee, Trinity College Dublin, Ireland Section III. Tools and Technologies This section presents extensive coverage of the tools and specific technologies that change the way we interact with and respond to our environments. These chapters provide an in-depth analysis of the use and development of innumerable devices and tools, while also providing insight into new and upcoming technologies, theories, and instruments that will soon be commonplace. Within these rigorously researched chapters, readers are presented with examples of specific tools, such as video surveillance systems, smart antennas, mobile technologies, and GIS systems. In addition, the successful implementation and resulting impact of these various tools and technologies are discussed within this collection of chapters. Chapter 3.1. Deploying Pervasive Technologies................................................................................. 503 Juan-Carlos Cano, Technical University of Valencia, Spain Carlos Tavares Calafate, Technical University of Valencia, Spain Jose Cano, Technical University of Valencia, Spain Pietro Manzoni, Technical University of Valencia, Spain Chapter 3.2. Embedding Ubiquitous Technologies............................................................................. 511 Susan A. Elwood, Texas A&M University, Corpus Christi, USA Chapter 3.3. Ubiquitous Computing Technologies in Education......................................................... 520 Gwo-Jen Hwang, National University of Tainan, Taiwan Ting-Ting Wu, National University of Tainan, Taiwan Yen-Jung Chen, National University of Tainan, Taiwan

Chapter 3.4. Mobile and Pervasive Technology in Education and Training: Potential and Possibilities, Problems and Pitfalls............................................................................................... 524 Mark J. W. Lee, Charles Sturt University, Australia Chapter 3.5. A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning............................................................................................................................... 557 Te-Hua Wang, Tamkang University, China Flora Chia-I Chang, Tamkang University, China

Volume II Chapter 3.6. Realizing the Promise of RFID: Insights from Early Adopters and the Future Potential....................................................................................................................... 581 Velan Thillairajah, EAI Technologies, USA Sanjay Gosain, The Capital Group Companies, USA Dave Clarke, GXS, USA Chapter 3.7. The Little Chip that Could: The Public Sector and RFID............................................... 594 David C. Wyld, Southeastern Louisiana University, USA Chapter 3.8. Web & RFId Technology: New Frontiers in Costing and Process Management for Rehabilitation Medicine........................................................................................... 623 Massimo Memmola, Catholic University, Italy Giovanna Palumbo, Ospedale Valduce, Italy Mauro Rossini, Ospedale Valduce, Italy Chapter 3.9. Smart Antennas for Automatic Radio Frequency Identification Readers....................... 648 Nemai Chandra Karmakar, Monash University, Australia Chapter 3.10. Getting to Know Social Television: One Team’s Discoveries from Library to Living Room.............................................................................................................. 678 Gunnar Harboe, Motorola, USA Elaine Huang, Motorola, USA Noel Massey, Motorola, USA Crysta Metcalf, Motorola, USA Ashley Novak, Motorola, USA Guy Romano, Motorola, USA Joe Tullio, Motorola, USA Chapter 3.11. Pervasive iTV and Creative Networked Multimedia Systems...................................... 707 Anxo Cereijo Roibás, SCMIS, University of Brighton, UK Stephen Johnson, Mobility Research Centre, UK

Chapter 3.12. Ambient Media and Home Entertainment..................................................................... 717 Artur Lugmayr, Tampere University of Technology, Finland Alexandra Pohl, Berlin-Brandenburg (rbb) Innovationsprojekte, Germany Max Müehhäueser, Technische Universitat Darmstädt, Germany Jan Kallenbach, Helsinki University of Technology, Finland Konstantinos Chorianopoulos, Bauhaus University of Weimar, Germany Chapter 3.13. TeleTables and Window Seat: Bilocative Furniture Interfaces..................................... 730 Yeonjoo Oh, Carnegie Mellon University, USA Ken Camarata, KDF Architecture, USA Michael Philetus Weller, Carnegie Mellon University, USA Mark D. Gross, Carnegie Mellon University, USA Ellen Yi-Luen Do, Georgia Institute of Technology, USA Chapter 3.14. Interactive Tables: Requirements, Design Recommendations, and Implementation............................................................................................................................. 741 Michael Haller, Upper Austria University of Applied Sciences–Digital Media, Austria Mark Billinghurst, Human Interface Technology Laboratory New Zealand–University of Canterbury, New Zealand Section IV. Utilization and Application This section introduces and discusses the utilization and application of ubiquitous and pervasive computing technologies. These particular selections highlight, among other topics, pervasive healthcare, the utilization of handheld computers, and m-commerce. Contributions included in this section provide coverage of the ways in which technology increasingly becomes part of our daily lives through the seamless integration of specific tools into existing processes. Chapter 4.1. Pervasive Healthcare: Problems and Potentials.............................................................. 764 Niels Boye, University of Aalborg, Denmark Chapter 4.2. Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching............................................................................................................................ 782 Masoud Mohammadian, University of Canberra, Australia Ric Jentzsch, Compucat Research Pty Limited, Australia Chapter 4.3. Using RFID to Track and Trace High Value Products: The Case of City Healthcare................................................................................................................................ 802 Judith A. Symonds, Auckland University of Technology, New Zealand David Parry, Auckland University of Technology, New Zealand Chapter 4.4. Implementing RFID Technology in Hospital Environments.......................................... 815 Marlyn Kemper Littman, Nova Southeastern University, USA

Chapter 4.5. RFID as the Critical Factor for Superior Healthcare Delivery........................................ 823 A. Dwivedi, University of Hull, UK T. Butcher, University of Hull Logistics Institute (UHLI), UK Chapter 4.6. An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care......... 833 Dante I. Tapia, Universidad de Salamanca, Spain Juan M. Corchado, Universidad de Salamanca, Spain Chapter 4.7. Ubiquitous Healthcare: Radio Frequency Identification (RFID) in Hospitals................ 845 Cheon-Pyo Lee, Carson-Newman College, USA J. P. Shim, Mississippi State University, USA Chapter 4.8. Ubiquitous Risk Analysis of Physiological Data............................................................ 853 Daniele Apiletti, Politecnico di Torino, Italy Elena Baralis, Politecnico di Torino, Italy Giulia Bruno, Politecnico di Torino, Italy Tania Cerquitelli, Politecnico di Torino, Italy Chapter 4.9. RFID in Healthcare: A Framework of Uses and Opportunities...................................... 867 Nebil Buyurgan, University of Arkansas, USA Bill C. Hardgrave, University of Arkansas, USA Janice Lo, Baylor University, USA Ronald T. Walker, University of Arkansas, USA Chapter 4.10. Tapping into Digital Literacy: Handheld Computers in the K-12 Classroom............... 886 Mark van ‘t Hooft, Kent State University, USA Chapter 4.11. Internet-Enabled User Interfaces for Distance Learning............................................... 905 Wei Liu, National University of Singapore, Singapore Keng Soon Teh, National University of Singapore, Singapore Roshan Peiris, National University of Singapore, Singapore Yongsoon Choi, National University of Singapore, Singapore Adrian David Cheok, National University of Singapore, Singapore Charissa Lim Mei-Ling, Nanyang Technological University, Singapore Yin-Leng Theng, Nanyang Technological University, Singapore Ta Huynh Duy Nguyen, National University of Singapore, Singapore Tran Cong Thien Qui, National University of Singapore, Singapore Athanasios V. Vasilakos, University of Peloponnese, Greece Chapter 4.12. Handhelds for Digital Libraries..................................................................................... 931 Spyros Veronikis, Ionian University, Greece Giannis Tsakonas, Ionian University, Greece Christos Papatheodorou, Ionian University, Greece

Chapter 4.13. South Korea: Vision of a Ubiquitous Network World................................................... 941 Jounghae Bang, Penn State University Mont Alto, USA Inyoung Choi, Georgetown University, USA Chapter 4.14. Ubiquitous Computing for Microbial Forensics and Bioterrorism............................... 957 Gaya Prasad, CCS Haryana Agricultural University, India Minakshi, CCS Haryana Agricultural University, India Section V. Organizational and Social Implications This section includes a wide range of research pertaining to the social and organizational impact of ubiquitous and pervasive computing around the world. Chapters included in this section analyze the cultural dimension of pervasive computing, consumer reactions to RFID, and user acceptance of technology. The inquiries and methods presented in this section offer insight into the implications of ubiquitous and pervasive computing at both an individual and organizational level, while also emphasizing potential areas of study within the discipline. Chapter 5.1. Cultural Dimension in the Future of Pervasive Computing............................................ 974 B.K. Mangaraj, XLRI Jamshepur, School of Business and Human Resources, Jamshedpur, India Upali Aparajita, Utkal University, India Chapter 5.2. Outline of the Human Factor Elements Evident with Pervasive Computers.................. 993 Genevieve Watson, University of Western Sydney, Australia Antony Glambedakis, University of Western Sydney, Australia Chapter 5.3. Adapting to the User...................................................................................................... 1006 Matthias Jöst, European Media Laboratory GmbH, Germany Chapter 5.4. How Research can Help to Create Commercially Successful Ubiquitous Services........................................................................................................................... 1021 Teea Palo, University of Oulu, Finland Kaisa Koskela, University of Oulu, Finland Timo Koivumäki, VTT Technical Research Centre of Finland, Finland Jaana Tähtinen, University of Oulu, Finland Chapter 5.5. Knowledge Sharing and Pervasive Computing: The Need for Trust and a Sense of History....................................................................................................................... 1039 Phillip W J Brook, University of Western Sydney, Australia Chapter 5.6. Ubiquitous Communication: Where is the Value Created in the Multi-Play Value Network?......................................................................................................... 1052 Mikko Pynnönen, Lappeenranta University of Technology, Finland Jukka Hallikas, Lappeenranta University of Technology, Finland Petri Savolainen, Lappeenranta University of Technology, Finland Karri Mikkonen, TeliaSonera, Sweden

Chapter 5.7. Identity Management for Wireless Service Access....................................................... 1067 Mohammad M. R. Chowdhury, University Graduate Center – UniK, Norway Josef Noll, University Graduate Center – UniK, Norway Chapter 5.8. Inscribing Interpretive Flexibility of Context Data in Ubiquitous Computing Environments: An Action Research Study of Vertical Standard Development..................................................................................................... 1079 Magnus Andersson, Viktoria Institute, Sweden Rikard Lindgren, University of Gothenburg, Sweden & Viktoria Institute, Sweden Chapter 5.9. Consumer Attitudes toward RFID Usage...................................................................... 1098 Madlen Boslau, Georg-August-Universität Göttingen, Germany Britta Lietke, Georg-August-Universität Göttingen, Germany Chapter 5.10. Determinants of User Acceptance for RFID Ticketing Systems................................. 1106 Dimitrios C. Karaiskos, Athens University of Business and Economics, Greece Panayiotis E. Kourouthanassis, Athens University of Business and Economics, Greece Chapter 5.11. An Empirical Study of Factors Affecting RFID’s Adoption in Taiwan....................... 1122 Dong-Her Shih, National Yunlin University of Science & Technology, Taiwan Yuh-Wen Chiu, National Yunlin University of Science & Technology, Taiwan She-I Chang, National Chung Cheng University, Taiwan David C. Yen, Miami University, USA Chapter 5.12. Impact of RFID Technology on Health Care Organizations....................................... 1144 Véronique Nabelsi, École Polytechnique de Montréal, Canada Florina Stefanescu, ePoly Centre of Expertise in Electronic Commerce, Canada Chapter 5.13. Learning by Pervasive Gaming: An Empirical Study................................................. 1156 Christian Kittl, evolaris Privatstiftung, Austria & Karl-Franzens University, Austria Francika Edegger, evolaris Privatstiftung, Austria Otto Petrovic, evolaris Privatstiftung, Austria & Karl-Franzens University, Austria Chapter 5.14. Using Mobile and Pervasive Technologies to Engage Formal and Informal Learners in Scientific Debate....................................................................................... 1179 Dawn Woodgate, University of Bath, UK Danaë Stanton Fraser, University of Bath, UK Amanda Gower, BT Innovate, UK Maxine Glancy, BBC Research & Innovation, UK Andrew Gower, BT Innovate, UK Alan Chamberlain, University of Nottingham, UK Teresa Dillon, Polar Produce, UK David Crellin, Abington Partners, UK

Volume III Section VI. Managerial Impact This section presents contemporary coverage of the managerial implications of ubiquitous and pervasive computing. Particular contributions address pervasive business infrastructure, RFID and the supply chain, and employee surveillance. The managerial research provided in this section allows executives, practitioners, and researchers to gain a better sense of how ubiquitous and pervasive computing can impact and inform practices and behavior. Chapter 6.1. Pervasive Business Infrastructure: The Network Technologies, Routing and Security Issues............................................................................................................................. 1199 Varuna Godara, University of Western Sydney, Australia Chapter 6.2. Decision Analysis for Business to Adopt RFID............................................................ 1219 Koong Lin, Tainan National University of the Arts, Taiwan Chad Lin, Edith Cowan University, Australia Huei Leu, Industrial Technology Research Institute, Taiwan Chapter 6.3. Intelligent Supply Chain Management with Automatic Identification Technology.................................................................................................................. 1228 Dong Li, University of Liverpool, UK Xiaojun Wang, University of Liverpool, UK Kinchung Liu, University of Liverpool, UK Dennis Kehoe, University of Liverpool, UK Chapter 6.4. When Does RFID Make Business Sense for Managing Supply Chain?....................... 1250 Ertunga C. Özelkan, The University of North Carolina at Charlotte, USA Agnes Galambosi, The University of North Carolina at Charlotte, USA Chapter 6.5. RFID and Supply Chain Visibility................................................................................ 1284 Sumeet Gupta, Shri Sankaracarya Institute of Management and Technology, India Miti Garg, The Logistics Institute – Asia Pacific, Singapore Heng Xu, The Pennsylvania State University, USA Mark Goh, NUS Business School, The Logistics Institute – Asia Pacific, Singapore Chapter 6.6. Security and Reliability of RFID Technology in Supply Chain Management............................................................................................................................ 1293 Vladimír Modrák, Technical University of Košice, Slovakia Peter Knuth, Technical University of Košice, Slovakia Chapter 6.7. Recognizing RFID as a Disruptive Technology............................................................ 1301 Chin-Boo Soon, The University of Auckland, New Zealand Jairo A. Gutiérrez, The University of Auckland, New Zealand

Chapter 6.8. Ubiquitous Connectivity & Work-Related Stress......................................................... 1315 J. Ramsay, University of the West of Scotland, UK M. Hair, University of the West of Scotland, UK K. V. Renaud, University of Glasgow, UK Chapter 6.9. Bridging the Gap between Employee Surveillance and Privacy Protection................. 1331 Lilian Mitrou, University of the Aegean, Greece Maria Karyda, University of the Aegean, Greece Section VII. Critical Issues This section addresses conceptual and theoretical issues related to the field of ubiquitous and pervasive computing. Within these chapters, the reader is presented with analysis of the most current and relevant conceptual inquires within this growing field of study. Particular chapters discuss ethical issues in pervasive computing, privacy issues, and quality of experience. Overall, contributions within this section ask unique, often theoretical questions related to the study of ubiquitous and pervasive computing and, more often than not, conclude that solutions are both numerous and contradictory. Chapter 7.1. The Ethical Debate Surrounding RFID......................................................................... 1350 Stephanie Etter, Mount Aloysius College, USA Patricia G. Phillips, Duquesne University, USA Ashli M. Molinero, Robert Morris University, USA Susan J. Nestor, Robert Morris University, USA Keith LeDonne, Robert Morris University, USA Chapter 7.2. Privacy Issues of Applying RFID in Retail Industry..................................................... 1358 Haifei Li, Union University, USA Patrick C. K. Hung, University of Ontario Institute of Technology, Canada Jia Zhang, Northern Illinois University, USA David Ahn, Nyack College, USA Chapter 7.3. An Evaluation of the RFID Security Benefits of the APF System: Hospital Patient Data Protection........................................................................................................ 1374 John Ayoade, American University of Nigeria, Nigeria Judith Symonds, Auckland University of Technology, New Zealand Chapter 7.4. Security and Privacy in RFID Based Wireless Networks............................................. 1386 Denis Trček, University of Ljubljana, Slovenia Chapter 7.5. Humans and Emerging RFID Systems: Evaluating Data Protection Law on the User Scenario Basis................................................................................................................ 1396 Olli Pitkänen, Helsinki Institute for Information Technology (HIIT), Finland Marketta Niemelä, VTT Technical Research Centre of Finland, Finland

Chapter 7.6. Privacy Factors for Successful Ubiquitous Computing................................................ 1408 Linda Little, Northumbria University, UK Pam Briggs, Northumbria University, UK Chapter 7.7. Privacy Threats in Emerging Ubicomp Applications: Analysis and Safeguarding....... 1425 Elena Vildjiounaite, VTT Technical Research Centre of Finland, Finland Tapani Rantakokko, Finwe LTD, Finland Petteri Alahuhta, VTT Technical Research Centre of Finland, Finland Pasi Ahonen, VTT Technical Research Centre of Finland, Finland David Wright, Trilateral Research and Consulting, UK Michael Friedewald, Fraunhofer Institute Systems and Innovation Research, Germany Chapter 7.8. Deciphering Pervasive Computing: A Study of Jurisdiction, E-Fraud and Privacy in Pervasive Computing Environment........................................................................... 1450 Grace Li, University of Technology, Sydney, Australia Chapter 7.9. Privacy Control Requirements for Context-Aware Mobile Services............................ 1465 Amr Ali Eldin, Accenture BV, The Netherlands Zoran Stojanovic, IBM Nederland BV, The Netherlands Chapter 7.10. Access Control in Mobile and Ubiquitous Environments........................................... 1481 Laurent Gomez, SAP Research, France Annett Laube, SAP Research, France Alessandro Sorniotti, SAP Research, France Chapter 7.11. Warranting High Perceived Quality of Experience (PQoE) in Pervasive Interactive Multimedia Systems.................................................................................... 1498 Anxo Cereijo Roibás, SCMIS, University of Brighton, UK Chapter 7.12. Pervasive and Ubiquitous Computing Databases: Critical Issues and Challenges................................................................................................................................... 1517 Michael Zoumboulakis, University of London, UK George Roussos, University of London, UK Chapter 7.13. Adaptive Resource and Service Management in a Mobile-Enabled Environment......................................................................................................... 1527 Claudia Raibulet, Universitá degli Studi di Milano-Bicocca, Italy Chapter 7.14. Service-Oriented Architectures for Context-Aware Information Retrieval and Access.......................................................................................................................................... 1549 Lu Yan, University College London, UK

Section VIII. Emerging Trends This section highlights research potential within the field of ubiquitous and pervasive computing while exploring uncharted areas of study for the advancement of the discipline. Chapters within this section highlight ambient learning, ubiquitous games, and new methods for patient monitoring. These contributions, which conclude this exhaustive, multi-volume set, provide emerging trends and suggestions for future research within this rapidly expanding discipline. Chapter 8.1. Ambient Learning.......................................................................................................... 1562 Fernando Lyardet, Technische Universität Darmstadt, Germany Chapter 8.2. Plastic Interfaces for Ubiquitous Learning.................................................................... 1582 José Rouillard, Laboratoire LIFL - Université de Lille 1, France Chapter 8.3. u-City: The Next Paradigm of Urban Development..................................................... 1601 Jong-Sung Hwang, National Information Society Agency, Korea Chapter 8.4. Planning for Knowledge Cities in Ubiquitous Technology Spaces: Opportunities and Challenges............................................................................................................ 1613 Tschangho John Kim, University of Illinois at Urbana-Champaign, USA Chapter 8.5. Emotional Ambient Media............................................................................................ 1626 Artur Lugmayr, Tampere University of Technology, Finland Tillmann Dorsch, Tampere University of Technology, Finland Pabo Roman Humanes, Tampere University of Technology, Finland Chapter 8.6. Leveraging Semantic Technologies towards Social Ambient Intelligence................... 1643 Adrien Joly, Alcatel-Lucent Bell Labs, France & Universite deLyon, LIRIS/INSA, France Pierre Maret, Université de Lyon, France Fabien Bataille, Alcatel-Lucent Bell Labs, France Chapter 8.7. From E to U: Towards an Innovative Digital Era......................................................... 1669 Spyros P. Angelopoulos, Technical University of Crete, Greece Fotis C. Kitsios, Technical University of Crete, Greece Eduard Babulak, Fairleigh Dickinson University, Canada Chapter 8.8. Ubiquitous Services and Business Processes................................................................ 1688 Alistair Barros, SAP Research, Australia Chapter 8.9. Ambient Intelligence on the Dance Floor..................................................................... 1720 Magy Seif El-Nasr, Penn State University, USA Athanasios V. Vasilakos, University of Peloponnese, Greece Chapter 8.10. Adaptive Narration in Multiplayer Ubiquitous Games............................................... 1738 Stéphane Natkin, Conservatoire National des Arts et Métiers, Pans, France Chen Yan, Conservatoire National des Arts et Métiers, Pans, France

Chapter 8.11. Concept of Symbiotic Computing and its Agent-Based Application to a Ubiquitous Care-Support Service............................................................................................... 1762 Takuo Suganuma, Tohoku University, Japan Kenji Sugawara, Chiba Institute of Technology, Japan Tetsuo Kinoshita, Tohoku University, Japan Fumio Hattori, Ritsumeikan University, Japan Norio Shiratori, Tohoku University, Japan Chapter 8.12. Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays................................................................................................................. 1786 Jesus Favela, CICESE, Mexico Mónica Tentori, CICESE and Universidad Autónoma de Baja California, Mexico Daniela Segura, CICESE, Mexico Gustavo Berzunza, CICESE, Mexico

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Preface

Imagine that your refrigerator can tell you if the food it contains is going bad, or that the clothes you wear can tell your thermostat when to change the temperature in a room. This may seem like the technology of science fiction novels, however, researchers in ubiquitous computing propose this sort of technology and more. Ubiquitous computing is a forward-looking research area which focuses on the integration of technology into everyday life with the end goal of making the technology second nature and nearly, if not completely, invisible to the user. This technology represents a movement away from the current second generation desktop model, in which the user’s interaction with the technology is intentional and deliberate, to a third generation computing model in which the user may engage several technologies at once, possibility without being aware of the interaction. As the world moves closer and closer to the integration of technology into every aspect of life there is a greater need for innovative research and development into the various aspects of ubiquitous computing. Issues surrounding ubiquitous and pervasive computing vary from the practical questions of hardware size and user interfacing to the more ethical questions of privacy and data protection. Every aspect of how users interact with technology and what role technology should play in the world is constantly being reviewed, revised, and updated in light of the ubiquitous computing movement. With such continual change it is important for researchers and practitioners in this field to stay abreast of the latest in technological and theoretical advances. With the constant changes in the landscape of ubiquitous and pervasive computing it is a challenge for researchers and experts to take in the volume of innovative advances and up-to-the-moment research in this multifarious field. Information Science Reference is pleased to offer a three-volume reference collection on this rapidly growing discipline, in order to empower students, researchers, academicians, and practitioners with a wide-ranging understanding of the most critical areas within this field of study. This collection provides the most comprehensive, in-depth, and recent coverage of all issues related to the development of cutting-edge ubiquitous technologies, as well as a single reference source on all conceptual, methodological, technical and managerial issues, and the opportunities, future challenges and emerging trends related to the development of the ubiquitous and pervasive computing model. This collection entitled, “Ubiquitous and Pervasive Computing: Concepts, Methodologies, Tools, and Applications” is organized in eight (8) distinct sections, providing the most wide-ranging coverage of topics such as: 1) Fundamental Concepts and Theories; 2) Development and Design Methodologies; 3) Tools and Technologies; 4) Utilization and Application; 5) Organizational and Social Implications; 6) Managerial Impact; 7) Critical Issues; and 8) Emerging Trends. The following provides a summary of what is covered in each section of this multi-volume reference collection: Section 1, Fundamental Concepts and Theories, serves as a foundation for this extensive reference tool by addressing crucial theories essential to the understanding of ubiquitous and pervasive computing.

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Chapters such as, “Introduction to Ubiquitous Computing,” by Max Mühlhäuser and Iryna Gurevych, as well as “Ubiquitous Computing History, Development, and Scenarios,” by Jimmy Chong, Stanley See, Lily Leng-Hiang Seah, Sze Ling Koh, Yin-Leng Theng and Henry B. L. Duh, provide foundational information on the history of and important topics related to ubiquitous computing. “Understanding RFID (Radio Frequency Identification),” by Susan A. Vowels, presents an explanation of RFID technology and describes how this technology can improve upon the limitations of the barcode system which is already pervasively used for identifying objects. Fariba Sadri and Kostas Stathis present a foundational review of the progress of ambient intelligence research and discuss the role of this technology for independent living in their chapter “Ambient Intelligence.” Ubiquitous computing, pervasive computing, and context computing as they relate to e-commerce are discussed in “Context Related Software Under Ubiquitous Computing” by N. Raghavendra Rao. “Ethical Issues and Pervasive Computing,” by Penny Duquenoy and Oliver K. Burmeister, emphasizes the need for an ethical perspective on the implementation of pervasive technologies and describes a code of professional conduct for consideration while designing and implementing ubiquitous technology. These and several other foundational chapters provide a wealth of expert research on the elemental concepts and ideas which surround the ubiquitous and pervasive computing models. Section 2, Development and Design Methodologies, presents in-depth coverage of conceptual design and architecture to provide the reader with a comprehensive understanding of the emerging technological developments within the field of ubiquitous computing. “Multimodal Software Engineering,” by Andreas Hartl, and “Designing Pervasive and Multimodal Interactive Systems: An Approach Built on the Field,” by Barbara R. Barricelli, Andrea Marcante, Piero Mussio, Loredana Parasiliti Provenza, Marco Padula and Paolo L. Scala, discuss the importance of multimodal technology for pervasive computing and present recommended approaches for the development of these technologies. Heinz-Josef Eikerling and Pietro Mazzoleni present a holistic methodology for the development of context-aware mobile services in their chapter “A Methodology for the Design, Development and Validation of Adaptive and Context-Aware Mobile Services,” while René Meier and Deirdre Lee discuss the iTransIT framework which ultimately leads to a method for creating context-aware ambient services in their chapter “Context-Aware Services for Ambient Environments.” From chapters covering a broad description of developmental concepts, such as Varuna Godara’s “Pervasive Computing: A Conceptual Framework,” to chapters describing the use of pervasive technologies to deal with a specific question, as in “A Mandarin E-Learning System in Pervasive Environment” by Yue Ming and Zhenjiang Miao, this section provides a vast array of methods and approaches to designing relevant and useful ubiquitous technologies. With more than 20 contributions from leading international researchers, this section offers copious developmental approaches and methodologies for ubiquitous and pervasive computing. Section 3, Tools and Technologies, presents extensive coverage of the various tools and technologies used in the development and implementation of ubiquitous and pervasive technologies. This comprehensive section includes chapters such as “An Intelligent Wearable Platform for Real Time Pilot’s Health Telemonitoring,” by Christos Papadelis, Chrysoula Kourtidou-Papadeli, Fotini Lazaridou and Eleni Perantoni, as well as “A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning,” by Te-Hua Wang and Flora Chia-I Chang, which describe pervasive technologies developed with niche specific practical uses in mind. “Ubiquitous Computing Technologies in Education,” by Gwo-Jen Hwang, Ting-Ting Wu and Yen-Jung Chen, describes potential issues surrounding the implementation of ubiquitous and mobile technologies in e-learning. The EMURCT system to assist with randomizing circuit training programs in an effort to keep trainees from becoming bored with their workout is described in “Electronic Multi-User Randomized Circuit Training For Workout Motivation” by Corey

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A. Graves, Sam Muldrew, Tiara Williams, Jerono Rotich and Eric A. Cheek. Authors Artur Lugmayr, Alexandra Pohl, Max Müehhäueser, Jan Kallenbach and Konstantinos Chorianopoulos describe ubiquitous technology for domestic use in home entertainment systems in their chapter “Ambient Media and Home Entertainment.” With more than a dozen additional contributions, this section provides coverage of a variety of tools and technologies under development and in use in the ubiquitous and pervasive technologies community. Section 4, Utilization and Application, describes the implementation and use of an assortment of cutting edge ubiquitous technologies. Including more than 25 chapters such as “Motorola’s Experiences in Designing the Internet of Things,” by Andreas Schaller and Katrin Mueller, and “To Connect and Flow in Seoul: Ubiquitous Technologies, Urban Infrastructure and Everyday Life in the Contemporary Korean City,” by Jaz Hee-Jeong Choi and Adam Greenfield, this section provides insight into the application of ubiquitous technologies for both professional and private use. “Using RFID to Track and Trace High Value Products: The Case of City Healthcare,” by Judith A. Symonds and David Parry, describes the replacement of barcodes with RFID tags by City Healthcare of New Zealand and the implications, benefits, issues and challenges associated with that change. The application of ubiquitous technology to the healthcare field is also discussed in “An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care,” by Dante I. Tapia and Juan M. Corchado, as well as “RFID as the Critical Factor for Superior Healthcare Delivery,” by A. Dwivedi and T. Butcher. The practical use of handheld devices for accessing digital library materials is described in “Handhelds for Digital Libraries” by Spyros Veronikis, Giannis Tsakonas and Christos Papatheodorou. Contributions found in this section provide comprehensive coverage of the practicality and present use of ubiquitous technologies. Section 5, Organizational and Social Implications, includes chapters discussing the impact of ubiquitous technology on social and organization practices. Chapters such as “Consumer Attitudes toward RFID Usage,” by Madlen Boslau and Britta Lietke, as well as “Adapting to the User,” by Matthias Jöst, focus on the attitude and acceptance of individuals interacting with and using ubiquitous technologies. “How Research can Help to Create Commercially Successful Ubiquitous Services,” by Teea Palo, Kaisa Koskela, Timo Koivumäki and Jaana Tähtinen, stresses the importance of research to the implementation and successful marketing of ubiquitous services. The impact of ubiquitous technology on education and learning environments is discussed in chapters such as “Collaborative Technology Impacts in Distributed Learning Environments,” by Martha Grabowski, Greg Lepak and George Kulick, and “Learning by Pervasive Gaming: An Empirical Study,” by Christian Kittl, Francika Edegger and Otto Petrovic. B.K. Mangaraj and Upali Aparajita, in their chapter “Cultural Dimension in the Future of Pervasive Computing,” advocate the importance of a cultural focus when considering the introduction of a ubiquitous technology in order for the technology to be accepted and successful. Section 6, Managerial Impact, presents a focused coverage of ubiquitous computing as it relates to improvements and considerations in the workplace. Varuna Godara’s chapter “Pervasive Business Infrastructure: The Network Technologies, Routing and Security Issues” provides an overview of pervasive business technology and discusses related business concerns such as confidentiality and authenticity. “Intelligent Supply Chain Management with Automatic Identification Technology,” by Dong Li, Xiaojun Wang, Kinchung Liu and Dennis Kehoe, proposes RFID-enabled business models for implementation in supply chain management. Also focusing on the application of ubiquitous technology to supply chain management is “RFID and Supply Chain Visibility” by Sumeet Gupta, Miti Garg, Heng Xu and Mark Goh, which discusses the adoption of RFID technology for supply chain visibility while reviewing related issues. J. Ramsay, M. Hair and K. V. Renaud in their chapter, “Ubiquitous Connectivity & Work-Related Stress,” present a study of e-mail usage by workers and describe their findings in relation to the changes in work place stressors over the last 25 years.

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Section 7, Critical Issues, addresses vital issues related to the ubiquitous computing model such as privacy, access control, and data protection, among others. Chapters such as Denis Trček’s “Security and Privacy in RFID Based Wireless Networks” and “Privacy Issues of Applying RFID in Retail Industry,” by Haifei Li, Patrick C. K. Hung, Jia Zhang and David Ahn, tackle the difficult question of privacy and data security for the application of RFID technology. In “Invisibility and Visibility: The Shadows of Artificial Intelligence,” by Cecile K. M. Crutzen and Hans-Werner Hein, the authors discuss the construction of new meanings relating to human computer interaction as the visible action of users will be both preceded and followed by the invisible action of intelligent technology. “Pervasive and Ubiquitous Computing Databases: Critical Issues and Challenges,” by Michael Zoumboulakis and George Roussos, provides an explanation of the importance of databases to the ubiquitous and pervasive computing movements. Ambient information displays and issues related to their evaluation are discussed in “Issues for the Evaluation of Ambient Displays” by Xiaobin Shen, Andrew Vande Moere, Peter Eades and SeokHee Hong. The chapter “IPML: Structuring Distributed Multimedia Presentations in Ambient Intelligent Environments,” by Jun Hu and Loe Feijs, discusses the IPML markup language as an answer to issues relating to distributing multimedia presentations in ambient intelligent environments. These and other chapters in this section combine to provide a lively review of those issues which are most important to ubiquitous and pervasive computing technologies. The concluding section of this authoritative reference tool, Emerging Trends, highlights areas for future research within the field of ubiquitous computing, while exploring new avenues for the advancement of the technology. Jong-Sung Hwang’s chapter, “u-City: The Next Paradigm of Urban Development,” describes South Korea’s u-City project. The project is based on an emerging concept that uses ubiquitous technology to provide innovative urban services. “Voices from Beyond: Ephemeral Histories, Locative Media and the Volatile Interface,” by Barbara Crow, Michael Longford, Kim Sawchuk and Andrea Zeffiro, describes the emerging technology and theories used by the Mobile Media Lab in two of their recent projects. José Rouillard describes his research into the delivery of content via heterogeneous networks and devices resulting in the adaptive pervasive learning environment PerZoovasive. The description and results of his research project can be found in the chapter “Plastic Interfaces for Ubiquitous Learning.” The state of research into next generation Internet and telecommunications technologies, as they relate to a variety of research projects such as Future House 2015, can be found in the chapter “From E to U: Towards an Innovative Digital Era” by Spyros P. Angelopoulos, Fotis C. Kitsios and Eduard Babulak. In his chapter “Life in the Pocket: The Ambient Life Project Life-Like Movements in Tactile Ambient Displays in Mobile Phones,” Fabian Hemmert presents the results of his study in which ambient displays are used to notify users of missed events on their mobile phones. These and several other emerging trends and suggestions for future research can be found within the final section of this exhaustive multi-volume set. Although the primary organization of the contents in this multi-volume work is based on its eight sections, offering a progression of coverage of the important concepts, methodologies, technologies, applications, social issues, and emerging trends, the reader can also identify specific contents by utilizing the extensive indexing system listed at the end of each volume. Furthermore to ensure that the scholar, researcher and educator have access to the entire contents of this multi volume set as well as additional coverage that could not be included in the print version of this publication, the publisher will provide unlimited multi-user electronic access to the online aggregated database of this collection for the life of the edition, free of charge when a library purchases a print copy. This aggregated database provides far more contents than what can be included in the print version in addition to continual updates. This unlimited access, coupled with the continuous updates to the database ensures that the most current research is accessible to knowledge seekers.

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Although the concept of ubiquitous and pervasive computing may once have been the imaginative fodder of science fiction writers and readers alike, it is fast becoming a technological reality. This model of computing continues to grow and thrive as researchers and practitioners rethink the way that we interact with and understand the role of technology in everyday life. As ubiquitous technology becomes more and more of a reality, the demand for thorough integration, smaller hardware, and thoroughly invisible technology will continue to grow. The move from second generation desktop computing to the third generation ubiquitous model is certain to increase the demand for greater improvements and cutting edge research in RFID, ambient intelligence, and other areas related to the advancement of ubiquitous computing. Access to the most up-to-date research findings and firm knowledge of proven techniques and models from other researchers and practitioners of the ubiquitous computing model will facilitate the discovery and invention of increasingly more effective methods and technologies. The diverse and comprehensive coverage of ubiquitous and pervasive computing in this three-volume authoritative publication will contribute to a better understanding of all topics, research, and discoveries in this developing, significant field of study. Furthermore, the contributions included in this multi-volume collection series will be instrumental in the expansion of the body of knowledge in this enormous field, resulting in a greater understanding of the fundamental concepts and technologies while fueling the research initiatives in emerging fields. We at Information Science Reference, along with the editor of this collection and the publisher, hope that this multi-volume collection will become instrumental in the expansion of the discipline and will promote the continued growth of all aspects of ubiquitous and pervasive computing.

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Ubiquitous and Pervasive Computing:

Concepts, Methodologies, Tools, and Applications Judith Symonds Auckland University of Technology, New Zealand

IntroductIon A lot of people around the world are interested in ubiquitous computing. If you are reading this, then perhaps you are too. Many, many different applications have been developed around through three decades. Some areas seem to be developing faster than others. My colleague, Associate Professor Jeffrey Soar from the University of Southern Queensland pointed out to me one day that ubiquitous technologies in cars are far more developed than ubiquitous home technologies. Being a studious, young and perhaps overly eager to please, I didn’t take Prof. Soar at his word. I investigated the situation and in this chapter I write about my findings and relate them to an overview of pervasive and ubiquitous computing. Hopefully, this chapter provides a thought provoking and intriguing introduction to this multi-volume set on pervasive and ubiquitous computing. I start this chapter thinking about the following question: Is automobile technology more ubiquitous than home technology and if so, why is this so?

Motivation Why is this an important question? What will be gained if we know the answer to this question? Currently, many aspects of ubiquitous computing are the domain of enthusiasts. Enthusiasts push the development forward in pockets. However, for true transformation, a broader approach is needed. If we can understand what is driving forward the development of ubiquitous applications in one vertical sector, then perhaps we can work out ways to encourage development in other sectors. To understand the aspects of technology better, let’s look at the Technology Adoption Lifecycle (Figure 1). Current smart home implementations are innovators & early adopters. As more and more innovators develop home automation applications, we can expect to find that the ideas penetrate the market to the

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Figure 1. Technology adoption lifecycle

early and late majority. There are some companies who cater to certain buyers and who specialise in home automation such as Kristil in New Zealand (http://www.kristil.co.nz/home.html). However, currently, the automation technology implemented centres around climate control, security and entertainment systems. These home automation systems do not yet know whether the user is home or not for example which would be an important functionality of full home automation. The 2007 Hack A Day overall winner was shifd (shifd.com) which essentially involved a mobile phone and a laptop and automatically shifting information from one device to the other based on whether the user is home or not. Their crude test of whether the user was home or not was to use an RFID tag and a RFID reader set up as a cradle to tell if the user was home or not. If the RFID reader detects that mobile phone, then the system assumes that the user is home. Notice, that I said ‘crude’ as this system does not allow for people who leave their mobile phone by accident, like me. (Hack A Day is a 24 hour competition and this results in ‘quick and dirty’ conceptual creations often based on impossible dreams.) The concept of the shiftd application was that when at home, the user could queue things to look at later such as the location of a coffee shop on google maps ready to be downloaded to the mobile phone. As soon as the system sensed that the phone was away from home (i.e., out of range of the RFID reader in the cradle), information was downloaded to the mobile phone. The opposite is also true in that an email address could be added to the phone during coffee and then uploaded to the user email box from the phone when the system recognises that the user has returned home (i.e., the phone is replaced in the cradle). Another application of sensing if the user is home or not can be seen in the use of naked DSL and VOIP in the home where the mobile phone can detect that it is as ‘home’ and make use of the home connection, while only using cell phone technology when away from ‘home,’ thus saving the user money on mobile phone bill payments. The same could be used for WAP Internet browsing where the WAP is only used with the phone is away from ‘home.’ Can a modern car sense that the driver is there? Well, I guess the concept is much easier as the key being inserted in the ignition of the car signifies that the user is in the car and likewise, the key being taken from the ignition perhaps signifies that the user is leaving the car. However, leaving the key in the ignition and opening the driver side door will cause the car to alarm as these are conditions that can lead to the driver locking their key inside the car. Final central locking of the car probably signals best that the user is leaving the car.

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Probably the main motivation behind checking that when the drive side door is open, the key is removed from the lock is that driver lockouts can be a major cause of driver stranding. According to the AA in the United Kingdom, driver lockouts and accidental vehicle immobilisation are the two fastest rising reasons for calling for assistance (source: http://www.breakdowncover.co.uk/) This is not to say that being locked out of your own home is not a similar hazard. However, driver lockouts are recorded by roadside assistance companies such as AAA and such organisations have considerable influence over vehicle manufactures as they also perform other roles such as new vehicle testing and reviews. There isn’t the same level of call-out assistance for being locked out of home. While being stranded outside your own home is frustratingly, hair pulling-outly annoying, it isn’t quite as dangerous as being stranded on the side of a motorway or even worse, a dusty isolated track in the Northern Territories of Australia. Also, electronically ‘knowing’ whether the user is about to lock themselves out of the house is infinitely more complex for the house situation because many house systems mean that you lock the mechanism from inside and then pull the door shut. Or alternatively, in the case of a standalone building with an internal car garage, you flick the automatic car garage door switch to start the door closing while you slip out under the closing door. It may be that the simplest way around this is to create a house ignition. The home owner comes home, opens the garage door with the wireless garage door key and then inserts a key into the home ignition to signify that the user is home. This could signal many electronic processes like the water heater, fridge and freezer powering up ready for the prospect of hot water being consumed and fridge doors being opened, hence requiring more power to refrigerate to the same level as when there is no one home using hot water and opening the fridge door to let in hot air or placing relatively warm shopping contents in the fridge to be cooled. This system would also deactivate the iron, portable heater and the cook top before leaving. This discussion has outlined the motivations of this chapter. It is useful now to move on and consider a definition of ubiquitous computing to provide a scope for this discussion.

ubIquItous coMputIng defIned It is important to define what is meant by ubiquitous and pervasive computing before continuing. First, I looked at a couple that caught my interest: Ubiquitous Computing: Computing that is omnipresent and is, or appears to be, everywhere all the time; may involve many different computing devices that are embedded in various devices or appliances and operate in the background. (source: http://www.mansfieldct.org/schools/mms/palms/Meet_the_Team/Glossary.htm) Ubiquitous Computing: The practice of making computers so common and accessible that users are not even aware of their physical presence. The ideal of ubiquitous computing could be defined as a high-speed network that covers any kind of geography and is easily installed and automatically maintained. (source: http://substratum.ca/subs/Resources/TechTerms/?letter=U) Ubiquitous Computing (ubicomp): is a post-desktop model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. (source: http://en.wikipedia.org/wiki/Ubiquitous_computing)

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The age of calm technology, when technology recedes into the background of our lives. (source: http://www.ubiq.com/hypertext/weiser/UbiHome.html ) Ubiquitous computing: An integration of microprocessors into everyday objects like furniture, clothing, white goods, toys even paints. (Alcaniz 2005, p. 3) (source: https://igi-pub.com/downloads/excerpts/reference/IGR6314.pdf) I identify most with the definition of calm computing given by Mark Weiser. I also like the idea of technology being omnipresent and everywhere which seem very similar to Weiser’s use of the term ‘background.’ All the other definitions seem to show pieces of the management elephant; embedded computing devices, high-speed networks that are everywhere and the meeting of mobile intelligent devices with everyday objects such as furniture and clothing. This gives a very wide scope, which is probably very necessary. Much about the extension of digital computing to digital objects is still unknown.

sMart thIngs Some work on digital objects is carried out by ‘Things That Think’ (source: http://ttt.media.mit.edu/vision/vision.html) at MIT in Cambridge in the USA. Things That Think Co-Directors Professors Hiroshi Ishii, Joe Paradiso & Roz Picard put forward three themes of research on smart things: 1. Research into sophisticated sensing and computational architectures that augment, animate and coordinate networks of things; 2. Research into seamless interfaces that bridge digital, physical and human perspectives; 3. Research into an understanding about what makes things think at a much deeper level. Some thinking has already occurred around the first theme of networks of things by the Auto ID Centre. The centre came up with the concept of the internet of things. This poetic description can be expressed as the building of a global infrastructure for RFID tags. You could think of it as a wireless layer on top of the Internet where millions of things from razor blades to euro banknotes to car tires were constantly being tracked and accounted for. A network where, to use the rhetoric of the Auto ID Centre, it is possible for computers to identify “any object anywhere in the world instantly.” (source: http://www.guardian.co.uk/technology/2003/oct/09/shopping.newmedia) The second research theme is considered by the Oxygen project. The lofty goal of the project, funded partly by the Defense Advanced Research Projects Agency, is to create a new computing environment, in which computer firepower would be ubiquitous and manipulating computers as easy for people as breathing. Oxygen researchers want people to throw away the mouse and talk to their computers, some of which, researchers suggest, would be embedded in walls and ceilings (source: http://www.sfgate.com/ cgi-bin/article.cgi?file=/chronicle/archive/2004/11/22/BUG719UPI31.DTL&type=business ). Such a prospect would give new meaning to the idea that the walls have eyes and ears. The third research theme of having a much deeper understand of what makes digital things think is perhaps addressed by the concept of Digital Object Memories proposed at a workshop on Digital Object Memories (source: http://www.dfki.de/dome-workshop) by Michael Schneider at the Intelligent Environments Conference. Schneider proposes that Digital Object Memories“comprise hardware and software components that physically and/or conceptually associate digital information with real-world objects in an application-independent manner.” The significance of this work is that over time, digital objects can build up a meaningful record of an object’s history and use. Such information could lead to discoveries about how objects can think.

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faMous applIcatIons One of the earliest ubiquitous systems was artist Natalie Jeremijenko’s “Live Wire”, also known as “Dangling String,” installed at Xerox PARC during Mark Weiser’s time there. This was a piece of string attached to a stepper motor and controlled by a LAN connection; network activity caused the string to twitch, yielding a peripherally noticeable indication of traffic. Weiser called this an example of calm technology. More recently, Ambient Devices has produced an “orb,” a “dashboard,” and a “weather beacon”: these decorative devices receive data from a wireless network and report current events, such as stock prices and the weather.

systeMs developMent Recent developments in the ubiquitous computing area in terms of systems development show momentum building for AmI (Ambient Intelligent systems development) (source: https://igi-pub.com/downloads/ excerpts/reference/IGR6314.pdf The ambient intelligence paradigm builds upon ubiquitous computing, profiling practices and humancentric computer interaction design and is characterized by systems and technologies (Zelkha & Epstein 1998) that are: • • • • •

embedded: many networked devices are integrated into the environment context aware: these devices can recognize you and your situational context personalized: they can be tailored to your needs adaptive: they can change in response to you anticipatory: they can anticipate your desires without conscious mediation.

Ambient intelligence is closely related to the long term vision of an intelligent service system in which technologies are able to automate a platform, embedding the required devices for powering context aware, personalized, adaptive and anticipatory services. A typical context of ambient intelligence environment is a Home environment (Bieliková & Krajcovic 2001). Maybe mobile phones will form an integral part of this environment, controlling other networked devices that are integrated into the environment. Source: http://www.ercim.org/publication/ Ercim_News/enw47/bielikova.html Wiser & others originally envisioned a world where we would have many different computers that represent different functionalities. That is, Wiser’s vision was many computers for each person. However, the convergence of personal devices has given rise to the ubiquitous mobile which functions as a phone, voice recorder, music player, camera and game console, among other things. Simon Andrews (www.mindshareworld.com) writes that mobile phones are central to many aspects of people’s lives and they are a key access point to all media uses (refer to Table 1). One futuristic representation of family activities in the home related to ubiquitous computing (source: http://www.at.capgemini.com/m/at/ tl/2016_The_Future_Value_Chain.pdf) shows only one application not deliverable via mobile phone. In the example, the milk carton ‘beeps’ to signal that it is below temperature and needs to be replaced to the fridge. Currently this functionality can be seen on the side of fresh-up juice bottle containers that include a motif that changes colour when the juice is chilled. All of the other functionalities in the example can be delivered via mobile phone such as a message to say that the weekly grocery order is due for collection, test results, and bus time-table information.

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Table 1. Mobile motivation (Source http://www.istart.co.nz/index/HM20/PC0/PVC197/EX245/ AR211048) MOBILE MOTIVATION Below are the five key motivations behind technology use. The mobile phone is a platform to all of them. Motivation

Mobile Function

Information

Mobile internet access, location-based services

Entertainment

Music storage/services, mobile gaming, mobile TV

Communication

Voice, SMS, IM, email

Transaction

Mobile internet, barcode coupons, swipe & pay

Self expression

Handset ‘look’, ringtones

developMent approach to ubIquItous coMputIng Mark Wiser and friends first began experimenting with pervasive devices at the auto-id lab by trying to make the devices that they envisioned. This development approach favours a Design Science research approach. Innovative ubiquitous computing artefacts often seem to come from working with simpler technologies. Microprocessor technologies such as PICAXE microprocessors and Audino kits encourage research students to experiment with the technology in new ways. Arduino is an open-source electronics prototyping platform based on flexible, easy-to-use hardware and software. It’s intended for artists, designers, hobbyists, and anyone interested in creating interactive objects or environments. Low cost microprocessors (PICAXE kits cost under $NZ50 to purchase) give researchers an easy way to manipulate computer equipment in new ways. Many fabulous ubiquitous computing ideas have arisen from hack day competitions. Microsoft recognises the importance of the hack day concept and runs a similar program called the imagine cup. Hack day organisers simply run a kind of ‘byo’ party where the participants bring any old hardware lying around their offices such as old keyboards, mobile phones and dance pads. Examples of hack day projects include being able to txt a VCR to be able to tape a program while the owner remains away from the home. Done with a virtually worthless VCR and 2nd Generation mobile phone, the outcome is clunky and still requires the user to have had the presence of mind to leave a suitable cassette in the player before leaving the house in the morning. However, with the introduction of high definition free to view TV with a hard disk embedded in the decoder, the ability to use a txt to book a movie or to record a favourite television program remotely becomes all that much more likely. Many new ubiquitous computing developments are being played out in Second Life. Developments in Second Life, the virtual reality world game, are not restricted by the availability of hardware. School children are able to use the high level scripting languages to quickly develop any concept. Universities have their own areas in Second Life and even lectures can be help in Second Life. Virtual reality environments such as Second Life are perfect for trying out development and ubiquitous worlds without being constrained by reality. However, virtual reality computing is not ubiquitous computing. Ubiquitous environments are everything that virtual environments are not. However, there is a very interesting crossover between the environments. Companies like Right Hemisphere in Auckland, New Zealand develop sophisticated visual design software that can interpret a high specialised design in virtual reality into a physical prototype. (source: http://www.righthemisphere.com ). The virtual work that Right Hemisphere does integrates back into real world objects such as televisions and even electronic docu-

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ments where virtual software can allow the user to use a mouse to turn an image of an object around as though it were three dimensional. Ubiquitous computing development is constrained by what the hardware developers know will sell. RFID interrogators embedded in mobile phones are a good example. Nokia released the first one in 2004. Unfortunately, at that time the infrastructure to allow customers to use the technology was not present and so RFID embedded phones did not go past the first release. Open source software development is an important aspect of ubiquitous computing development. Often ideas are launched from others and only by seeing others work, new conclusions are reached and developments are improved with each cycle of use. Similar to the ideals of open source software, service oriented architecture allows ubiquitous computing researchers to develop applications which can be used in many different ways and applications. Many ubiquitous computing research problems lend themselves to a design science research approach. Design science uses a problem based approach to the design and development of artefacts. This research approach ensures that the artefact is scientifically tested and evaluated as well as placing strong emphasis on the problem solving aspects of research (Hevner, March, Park & Ram 2004). In order to be successful, ubiquitous computing systems must also be accepted by human users and therefore, there are essentially two streams of research in this area: 1. Technology acceptance work which looks at influencing factors such as ease of use and perceived usefulness (Venkatesh & Davis 2000) and 2. Usability testing of specific pervasive devises and services (Nielsen 1992). Much of the development of pervasive systems originates from the hobby field from people who like to build and invent things. In this regard, there are also books of projects such as RFID toys (http:// www.rfidtoys.net/) which provide samples of different project ideas to experiment with and try. Projects included in RFID toys include digital bookshelves that can track the addition and removal of books and keep a report of borrowers. You can even create a pet door that unlatches only when you pet approaches and not when other neighbour’s cats approach for example. This application can also be helpful if you live in a very cold environment and you want to have the door unlatched only when the pet enters and not when a cold wind blows through. Apart from hobby toys with neat outcomes, as suggested by the title ‘RFID toys’ as in play things, there are other researchers who build things out of a problem. Take, for example, the work of AUT student Doug Hunt (http://www.slideshare.net/jsymonds/doug-hunt) who encountered a problem with providing unobtrusive feedback to equine dressage riders about the position of the wrists. The position of the wrists is crucial in dressage riding as the wrong position can impact on the rider’s ability to stop the horse and to communicate sensitively to the horse. Doug has designed a device to be worn on the wrist of the rider that provides unobtrusive feedback to the rider about the position of the hands and wrists and also captures data for later retrieval. Doug has trialled this device with many equestrian riders and developed several prototypes of his design. One way of evangelising the new pervasive environment in the business world is to build a demonstrator–a space where examples of new technology that still have ‘toy’ like properties in that a real business need has not yet been made can be installed to scale and can be viewed working. One example of such a system for RFID can be seen at http://itri.uark.edu/rfid.asp. Called the RFID Research Centre, this real life demonstrator has a number of parts showing different organisational aspects of the supply chain including the delivery dock, the conveyor belt, the warehouse and the shop floor. At the technological development end of the field, researchers rely on development prototype platforms to provide infrastructure on which to conduct developmental work. For example, Intel’s Wireless Identification and Sensing Platform (WISP) where researchers are working on ways that RFID chips

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can do complex calculations in short bursts when the passive tags are powered up by a RFID reader (Anderson 2009).

ubIquItous coMputIng applIcatIons Ubiquitous Computing is considered across a wide range of application areas. These include commerce, management, health, security, personalisation, education and smart environment applications.

u-commerce U-commerce (ubiquitous commerce) is an extension of digital e-commerce. Initial examples include using mobile phone sms messages to pay for car parking and other small items (i.e., vending machine purchases) using a mobile phone account. Next generation u-commerce will likely involve the use of NFC (Near Field Communication) embedded into a mobile phone to pay for services ubiquitously, such as purchasing event tickets as you pass through the front gate of the event. Watson, Leyland, Berthon and Zinkhan (2002) suggest that the best way to envision u-commerce is on a scale of embeddedness and mobility as shown in Table 2. We are currently at least one decade out from seeing any true u-commerce. Junglas & Watson (2003) predict that the capability maturity steps for u-commerce will be similar to the number of letters in the alphabet between each step. That is, five steps to ‘E’ – e-commerce, eight steps to ‘M’ – ,m-commerce and eight steps to ‘U’ – u-commerce

Management Aggregated Internet nusiness models, increased supply chain visibility enabled through increased tracking and traceability ability, and contactless payment will have a lasting effect on the future value chain (source: http://www.at.capgemini.com/m/at/tl/2016_The_Future_Value_Chain.pdf ). The key industry trends likely to impact the value chain are consumer behaviour, information flow and product flow.

network Management Some researchers have estimated that there are more than 10 billion wireless sensors deployed in diverse applications including environmental monitoring, agricultural monitoring, machine health monitoring, surveillance and medical monitoring. These networks of wireless sensors connect the physical world with the digital world. However, the current wireless networking infrastructure that allows for support of wireless sensor communication is going to be overloaded with connecting so many sensors. Therefore, the very important work of Associate Professor Wendi Heinzelman of the Electrical and Computer Engineering Department at the University of Rochester will be groundbreaking in facilitating such Table 2. U-commerce embeddedness and mobility Embeddedness

Pervasive

Ubiquitous

Traditional

Mobile

Mobility

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communication between the wireless sensors and the digital world. The work is in adaptive network management to support dynamic mapping and the use of network aware architectures.

personalisation A new era has just started in many homes around the world with the first consumers having access to on demand high definition television. This allows consumers, for the first time, to control the programming and availability of television programs. It also allows the user to be able to pause the program as though they were watching their own personal copy of the television program. Personalisation has been spurred by consumer access to the World Wide Web and mobile phones. A popular Internet business model is to create personalise products via the World Wide Web. For example, it is possible to customise your own pair of sports shoes. In many ways the mobile phone is actually replacing the home phone. Home phones are shared and the caller uses one number to effectively access a household and then to further ask to speak with one particular individual. However, a mobile phone number reaches a specific individual. Therefore, with a mobile phone number, marketing companies can be sure to be speaking to the right person. Mobile phone users have the ultimate ability to personalise their advice choosing the colour of their device, the wallpaper on their display screen, the phone numbers in their address book and the functionality of their individual device. I am proud to lead a team of researchers in a Health Research Council of New Zealand feasibility study developing a personalised intelligent prompt to help people affected by Traumatic Brain Injury in their recovery. The device automates a process called Goal Management Training which would normally be done using traditional methods of creating a poster or an instruction booklet with simple steps for everyday tasks that are relevant to the patient such as the steps for getting dressed each day. Carers can then help the patient through these steps. By automating this process with a personalised device, we are empowering the patient to be more independent and hence complete more tasks without the help of a carer and we are supporting the important aspect of errorless learning while also providing only the right amount of prompting to encourage the patient to think for themselves and to begin to get the brain neurons and pathways firing again. When a carer helps, the process of recovery is so slow for brain injury patients that the carer may develop a habitual approach to prompting the patient and may not be patient enough to encourage brain development. A personal device is patient and not governed by habitual approaches. Each time the patient attempts the task with a digital device, there is no history based on previous habits, performance or happenings.

education I recently heard about a project in the United States where researchers had deployed sensors in a pond. The sensors record water temperature, quality and level and are wirelessly enabled to report the information that they collect back to an application which then will be made accessible via the World Wide Web. The information is intended for one group of scientists; however, the information is available to many different users of the World Wide Web including school groups who can use the data to inform their own studies on ecosystems. Not all of the research need be conducted online, the students can experiment with collecting their own data at a real pond, and they can aggregate this with the data from the World Wide Web which is longitudinal and scientific. They can also join with other school groups to exchange this information. Perhaps there are many other examples of pervasive information that would have immense education benefits. We can see this trend happening a little already with many museums and science centres already using pervasive systems to enhance the educational value for visitors. For

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example, many multimedia displays can be activated by the participants themselves using their mobile phone and therefore, the user can interact much more individually with the information linked to the exhibit and manipulate the presentation on their own personal device rather than being played a static multimedia display on in a shared presentation space.

Office Applications Most office buildings use electronic RFID cards to access the building. In the case of a university, that card will be used to access lecture rooms, meeting rooms and specific areas of the building. Although most of the reader devices are effectively dumb and contain no microprocessing ability at all, it is possible to capture all information from the cards of entry access by individual cards. Using current technology, the information captured would then need to be aggregated with other information from the database that tracks the issue of cards to be able to draw up a high level tracking database information. Managers could then tell arrival and departure times at work and also how prompt that lecturer was to a class and also how long the lecture stayed. With more information about the user other than the unique identification number, this information will be able to be drawn up more quickly. One interpretation of such invasive data may be that this information is unhelpful. However, there is a much more helpful interpretation of this information such that as the lecturer approaches the lecture room, the computer is already logged on for them and the room is already configured as they would like it (lighting and so on). This could also happen in conference rooms. At Microsoft Research in India, a project of this type is being undertaken. The project is called Sixth Sense (Ravindranath, Padmanabhan & Agrawal 2008) which seeks to build what is called an ‘Enterprise Intelligence.’ The researchers at Microsoft Research India have been working on a pervasive work environment that uses RFID and other technologies to aggregate data about the employees. This information base serves as an infrastructure onto which the team has started work on applications that use the data to created intelligent systems. One application they built to showcase the ambient information that they had collected was the semi-automated image catalog which allowed users to take photos and then cataloged the image according the where the image was taken. The image catalog is itself adding to the infrastructure of data that is being built up that would allow still other applications to replace the unique identification number of an object with a picture of it in its natural place in the office. The second application that they built was an automated conference room booking system which could identify participants in the room and if they intended to stay, check for a booking, advise of a booking clash or place a booking to indicate that the room is in use. Teleworking has long been part of an ideal virtual world where office space can be minimised by having employees work from home for some part of their working week and share a hot-desk for the other days (Emily uses the hot-desk on Monday & Tuesday and Helen uses the hot-desk on Wednesday, Thursday and Friday). Alternatively, workers have access to a pool of generic consulting rooms where workers book any of the rooms or desks for the days that they are in the office. However, this becomes a very cold and sterile environment to work in. Therefore, if the employee card could identify them as they walk into the office, it could cause the desk to configure to their preferences. Emily’s photos could be displayed in the digital photo frame when she arrives in the room and her computer could sense her arrival and automatically log on and configure with her preferences. This phenomenon is a daily reality in the doctor/patient consulting practice. The traditional model is that each doctor occupies a room and the patients are summoned in by the doctor for each consult. However, where the number of consultations that a doctor can perform everyday translates into the amount of money the doctor or the practice can earn, people are always looking for new approaches. It is much more efficient to have the doctor visit the patients in different rooms. This approach is used in emergency rooms and in some centres. However,

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the doctor now has to work with a generic set of medical equipment, stationery and even a generic desk and chair. However, more intelligent environments could help to make the doctor more comfortable by customising the settings in the room according to their preferences whenever they use that room based simply on the event of their employee proximity tag coming through the door of the consulting room. I have thought about my own application that I would build using Ravindranath, Padmanabhan & Agrawal’s (2008) infrastructure. It’s a pervasive meeting schedule assistant. Being Mum means cut throat time management. Being Mum also means that when I am coming in to the office specifically for a meeting, sometimes I end up being 5-10 minutes late. The clock in my car is permanently set 10 minutes fast. However, this doesn’t work because I know it is 10 minutes fast and I spend time making calculations back and forth and wishing that the clock was another 10 minutes faster. This pervasive meeting schedule assistant would be intelligent enough to know my appointments and my location. Assuming that I have a 10:00am appointment with Bethany, it would work like this: 1. 2. 3. 4. 5.

9:55am check my location – am in the office? If no, use location information to work out where I am (this would involve a wider location aware system based on cell phone networks perhaps). Calculate time to walk from location to office based on historical personal information gathered each day. Access Bethany’s details from my address book. SMS Bethany to notify her that I am a 5 minute walk away.

Currently, I tend to either try to stop the car along the way to make the text message. Alternatively, I try to walk and text, which uses up less time, but is not efficient because usually as I glance up, predictive txt ends up typing something different and I have to delete several characters to fix it, causing more stress. The alternative is attractive to me; I can know that I am late, but also know that the assistant will text my appointment and let them know to wait for me. I can drive uninterrupted to my parking space, and walk to my office taking a few deep breaths and mentally prepare for the meeting.

securIty In a pervasive environment, it is becoming more and more likely that there will be many different sensors, RFID tags and other small smart microprocessor controlled devices. If such devices are effectively accessed by any wirelessly enabled device and there is personal information stored on that device, then there will be a need for heightened levels of security. To illustrate the problem, any wireless computer can currently sense any wireless network within the vicinity of the device. On many occasions, I have noticed that a wireless network is not protected by authenticated access and although it would be unethical to do so, it is possible to access the Internet through that network without incurring a service charge. Imagine an environment where there are at least many thousands of smart microprocessor controlled devices that do tasks from sensing temperature, air quality, and noise level and record this information locally for access later by other devices. Clearly access to such information must be controlled. In my recent work with colleague John Ayoade (Ayoade & Symonds 2009), we have explored this problem specifically related to data-on-tag RFID systems by developing a prototype using a two level security system. Firstly, the application ensures that only authentic/registered devices and users are able to connect to the application and therefore the ability to read data from the tag is restricted. Secondly,

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Figure 2. Pervasive computing privacy continuum Irrational Hysteria

Ignorance

Ambivalence

New Development

Maturity

the data stored is encrypted so that in case an unauthorised reader/user was able to gain access to the information, the data would be encrypted and therefore meaningless.

prIvacy In this discussion, I explain the pervasive computing privacy continuum (see figure 2). I begin the discussion at the far left hand side of the model (irrational hysteria) and move to the far right of the model (mature). Watch with any YouTube video on RFID and you will see comments about this technology being the ‘mark of the beast.’ This expression comes of course from the biblical book of revelation. The expression runs to persecution of the so called holy people. Only cash is truly anonymous. With any other more advanced electronic means of exchanging currency, there is a high level of authentication and the trader’s identity is always known. If governments or organisations were to begin to persecute people on the basis of religious belief for example, it would be very easy to make a false entry on an individual’s credit history or to freeze all funds in a back account or even to prevent that individual from leaving the country. Electronic transfer of funds and consumer credit history are already well embedded into the fabric of western society and it is these technologies, more than anything, that represent a loss of anonymity, to be truly free from being identified. RFID is, of course, one technology that facilitates electronic transaction processing, however, to truly believe that it is the mark of the beast is inaccurate and a dramatisation. Despite the hysteria that surrounds RFID and other pervasive technologies, this is not nearly as large a problem as simple ignorance. Only a select few of the population understand, for example, the data that is held in an RFID access key card and how this relates to an individual. In the majority of RFID implementations, all the data that is held in an RFID card is an EPC global unique identification number that can then be matched to a record in a database in a centralised secure location that can then give further information about the user. Misconceptions that are spurred by fictional representations of the future in the movies (i.e., Minority Report) are very often not based on fact and therefore, not accurate. Those that are ambivalent regard pervasive technologies as part of the context or environment. For example, they regard RFID as the new barcode technology and have a very narrow understanding of the application of the technology. That is, that RFID tags will replace barcodes on every product and will allow fast checkout. RFID readers are related to barcode scanners and whether a hospital wristband has a barcode or an RFID tag embedded, there is no real difference. As technologies are released and new applications of new technologies are shown to consumers, there are occasional problems. For example, American Express had the local encryption of data stored in an RFID chip on the actual card cracked by BoingBoing TV and reported all over the Internet. The publicity has been so high that no credit card company will make the same mistake ever again, that is for sure. Card companies will know that more secure private key encryption is required.

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Mature pervasive technology will protect the consumer’s privacy and will assist people. At the moment, the US government has approved human RFID tagging only for special services staff such as policeman and fireman with the purpose being to identify a cadaver. However, think of the recent tsunami crisis in Indonesia where many people were killed. Loved ones endured the agonising wait to find out where the remains of their loves ones were so that they could continue on with their own process of grieving. RFID tags would provide instant identification of people and alleviate many difficulties in cadaver identification. On a more positive note, pervasive technologies are making lasting changes to the way that people are able to live in their own homes. My own work in assisted living devices that use the Goal Management Training approach to help traumatic brain injury patients recover is one of many examples. Some technologies simply provide easier devices to measure changing body states (blood pressure and blood sugar levels for example) by the patient at home and to provide the carer with more up to date information. Other devices provide a summary of complex medical information to predict whether this will be a good day or a bad day and allow the patient to plan accordingly. These devices can be simple as providing a voice recording of the colour of items of clothing on a personal digital assistant for blind people through to complex CCTV and sensor technology to tell whether the user has fallen in their own home and raise the alarm accordingly. Pervasive technologies will make our world better by making it safer, providing users with more real-time information and making life easier. As with all technology, pervasive technologies will follow the differences between intended behaviour and actual behaviour are evident. Fishbein & Ajzen (1975) first wrote about this phenomenon. Here is a practical example to demonstrate this point. Large department stores that have already implemented RFID tags on products heeded customers’ concerns about stores marketing products to them based on previous purchases. This be facilitated when a user purchases an item tagged with RFID and later has that item on their person upon entering another store, which could then allow the store to identify the tagged items and market to the consumer based on that information. The department stores addressed this concern by installing kill stations beyond the checkout area inviting customers to kill their tags after purchase as the product has reached the end of the supply chain. Anecdotally, evidence suggests that the tag killers are not often used. Air NZ Koru Lounge (frequent flyers) customers have been issued with a RFID tag the size of a small button designed to attach to a mobile phone that can be used to automatically check in. The tag contains a unique identification number to identify the customer. The customer’s details are stored centrally on an enterprise system. Suggesting that the customer details be stored locally on the tag was not the chosen solution. Much more detail than just a customer details record is regularly stored in a personal Blackberry and these are often reported lost at a restaurant. It is more that storing customer details and other sensitive information on an RFID tag that can be accessed by others smells of big brother – someone watching. We find this a problem because humans value freedom. Toddlers strive for it, you work your whole life trying to attain it and when you become elderly you will fight tooth and nail to maintain it. So the question is, is your customer details record more secure on you or in an Enterprise Resource Programme? With the customer details record on you, the customer address details are stored in a small device, such as an RFID tag. The address details are matched with a unique id in the ERP system. Provided the customer address information is encrypted, there is no danger of someone skimming the information from your back pocket in the street. Without the unique encryption key, information will be unreadable. A unique ID is difficult to store incorrectly because the number can include a digital check sum. The

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Figure 3. Company A

Customer Address

Company B

Unique ID

Customer Address

Company C

Customer Address

Company A

Unique ID

Company B

Customer Address

Unique ID

Company C

Unique ID

customer has the ability to update the information immediately and there is no need to change multiple instances of the address. Companies must notify the customer of access to the customer address details. Compare this with customer details stored in a company ERP where the unique identification number is the only information stored on the device that the customer has. The unique identification number is associated with the address record in each instance of the customer address each time it is stored. Control of storage of the address is outside the control of the customer and may be stored incorrectly. Also, the organisation accesses the address details when they need to and the customer does not know when their details are accessed. Because there are multiple instances of the customer address, there is the potential that address details can become out of date. Overall, if customers were worried about hackers skimming their personal details stored on electronic devices on their person such as on RFID tags being accessed without their knowledge, then they could always get a foil lined wallet or tote. In fact, as damage control for the recent credit card company that did not encrypt the personal customer information stored on the card well enough, they would manufacture a foil tote with suitable branding. It would be interesting to see how many customers would actually use this, however; it is the gesture to address the perception.

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Professor Andrew Monk of the Centre for Usable Home Technology, University of York in the United Kingdom is linked into the view of “technology to connect you” as opposed to “technology to watch over you.” Professor Monk has been working on the design of conceptual digital jewellery that aims to facilitate communication in extended families through a virtual presence. The Centre for Usable Home Technology also does some fascinating development that engenders pervasive systems. For example, one project was to develop a digital sign to help elderly people immediately see which stove hotplate they had turned on because even with diagrams and led lights on the diagram by the light, the project found that elderly people who find themselves in a strange new place and confronted with many new challenges outside their comfort zone can genuinely become very confused about which knob corresponds to which hotplate and mistakes can lead to kitchen and even house fires. For those that think that the data is safer in the enterprise system, consider the following sobering statistics. The Australian Office of the privacy commissioner reports that in December 2003, a USB stick containing details of over 6,000 prisoners was lost by a health agency in a UK prison. Details of almost 900 customers, including accounts, phone numbers and addresses copied on a USB stick were lost by a Bank of Ireland employee in November 2008. The information was not encrypted. A recent UK survey carried out by a data security firm found an estimated 9,000 USB sticks have been left in people’s pockets when they have their clothes dry cleaned. See www.privacy.gov.au

socIal Issues In our fast paced pervasive environment, social isolation can be a problem, particularly for older family members who live apart from their adult children. Often they only want to know that their children are home at night for example, but they don’t want to bother the children to find out because it seems that they are checking up on them. The adult children have children and jobs of their own and are too busy to remember to call each time they arrive home in the evening. However, researchers at IBM have suggested the concept of a special lamp in the home of the elderly parent which simply comes on when there is someone home and stays off when no one is home.

desIgn We are already seeing some advances in appliance design toward a more ubiquitous environment. For example, dryers can sense when the clothes are dry, washers can sense when they are off balance or overfull of water and fridges can sense when the door has been left open for too long and beep. However, these features are currently only available on high end models and basic entry level appliances still do not have these capabilities. Thinking past fridges and stoves that can take stock and cook, designers are also working on furniture that is intelligent. Take, for example, a concept visualisation of a graduation project at the Design Academy Eindhoven, entitled “An interactive seat which follows the user at the library” (see http:// www.youtube.com/watch?v=2Dgaz6NIUFk). The seats are intelligent enough to follow the user once checked out using a library card and will also return to a ‘seat rank’ once the library patron walks over a line on the floor which signals exiting the library area. The final visualisation shows the seats assembled in lecture hall style ready for a presentation.

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consuMer orIented consIderatIons Most interaction with consumers currently requires consumers to consent to information being collected about them and the best way to do this is to encourage an ‘opt-in’ policy and provide clear guidelines about how to ‘opt-out’. Take for example, new digital data collection laws and codes of ethics. However, in an environment where ambient information is being collected and processed all around them, how do consumers consent to that information being collected about them and how can they ‘opt-out’ of such systems when they are pervasive? There is a difference between spaces. Employees are potentially more at risk when employers use ubiquitous technologies to track information. Tracking of objects is fairly well accepted. Take for example the Aeroscout systems (http://www.aeroscout.com/), which have been used extensively to track high cost hospital assets. The system can track assets in real-time and can provide alerts when too many assets are in one room, for example, or when assets leave a defined area. However, take for example the case of Eastpac, the New Zealand Kiwi Fruit coolstore organisation who installed real-time tracking on every forklift in the coolstore (see http://www.logisticsmagazine.com. au/Article/Kiwi-Fruit-packer-implements-inventory-positioning/173594.aspx). Managers can access a graphical interface showing the layout of the entire coolstore with real-time representation and identity of every forklift. If you have encountered forklift drivers, they rarely alight from their vehicles for their eight hour shift with the forklift wheels becoming their defacto legs. Therefore, by tracking forklifts, Eastpac are essentially tracking forklift drivers in real-time. Eastpac produced an amazing return on investment through not having to pay $.25 million loading penalties and gained efficiency through the real-time location of every pallet of fruit in the coolstore as well as the ability to know about missing pallets of fruit before they are needed and to optimise packing and stacking of the coolstores according to picking orders. However, employee acceptance was slow with forklift drivers initially intentionally disabling their units, ‘forgetting’ to turn them on and not replacing and recharging flat batteries. However, what emerges from this experience are also stories about how the employees were protected by the system in instances where they were on a three minute cigarette break which was confirmed by management who initially assumed that they had been on their cigarette break for 30 minutes and also freedom for blame from damages that occur overnight and for which the culprit might not own up to, thus placing all workers on that shift under a cloud of suspicion. It is argued that employees can choose whether to work in the spaces. They should be informed and they should be able to leave (although this depends on the mobility of the job market and the skills of the individual worker). Public spaces are increasingly being monitored with CCTV. New advances in CCTV technology have been able to provide a blurring effect for all people in the footage so that people with public or low level access have an automatically censored view of the footage where they can recognise a shape as being a person, but the features of the person are blurred. Higher security access allows the uncensored images to be viewed. Spaces that are protected by CCTV should be clearly signed and footage should be kept secure. Monitoring of public spaces does make them more secure.

technIcal orIented consIderatIons Much of the future development around sensors in the network will involve ruggardisation and encapsulation of the sensors. Development is also spurred along by cost and availability.

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Table 3. RFID technology cost vs. ethical obligations Frequency

Read Range

UWB

Military precision

VHF

10m – 100m

UHF

45cm – 75cm read range

HF

5-45cm read range

LF

7-10 cm read range

Cost of purchase

Availability

Ethical Risk

Not available

Extreme

Developers kit approx $US500

Tracient kit available

Some potential for exploitation

Reader < $US100 Tags < 10cents

Phidget kit easily ships & has project book

Low

Table 3 shows some examples of the technology industry either naturally or otherwise showing some ethical consideration for privacy and use of RFID technology. Lower level RFID is freely available at a low cost. However, as the power and reach of the technology increases, so too do the barriers to access which include cost and availability.

Interface desIgn Much of the current human computer interface relies on a visual user interface. Visual interfaces have been fine for most office and productivity work, writing emails, interacting on Facebook and similar. However, once the system begins to move to a more pervasive style, visual interfaces become too cumbersome, requiring the user to direct their attention away from the task and focus on the display device. Visual interfaces are a problem too for mobile devices with small display areas. Mobile music device interface designers are experimenting with much more basic interfaces that allow the user to choose music tracks by interacting with a large graphic. Researchers at Georgia Institute of Technology (http:// sochi.cms.si.umich.edu/?q=content/hci-faculty-candidate-talk-lena-mamykina) in the United States have developed graphical user interfaces that use graphics such as that of an aquarium to communicate complicated medical information. When the user has healthy statistics, there are more fish in the aquarium. On less healthy days, there are fewer fish. This application is a great example of ubiquitous technology being supportive and helpful. The provision of such technologies will mean a better quality of life for those that use it. It is likely that work will channel away from visual output and try to develop voice enables systems and haptic interfaces. This task is being made easier with the widespread use of Bluetooth accessories and some initial work can be seen in the work on the LCD Bluetooth vibrating bracelet (http://www. engadget.com/2009/02/26/lcd-bluetooth-vibrating-bracelet-is-a-watch-short-of-awesome).

analysIs of house vs. car envIronMent Returning to my initial topic of comparing home to car ubiquitous computing environments, I searched out descriptions of leading house designs and car models. I chose Signature homes and a Toyota Levina. Hansmann et al. (2003) in his chapter discussing embedded controls discusses how pervasive technology will be used in home and automotive settings. They give no rationale for choosing the home and the car, however, these are two of the most private and personal spaces in our universe. The home and

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Table 4 Inventory of ubiquitous applications in the Signature Homes House

Technology

Microprocessor fire alarm

X

burglar alarm

X

domestic ventilation systems

X

PAN – cinema/data network BAN

X

Multi-function printer/telephone/fax/scanner

X

MP3s

X

Climate control

X

Microprocessor integrated into everyday object

Omnipresent

Calm

X

X

X

X

X

X

Digital photo frame

X

X

X

Dryer sensing dry needed

X

X

X

Washer sensing off-balance load

X

X

X

Tunstall alarm for frail elderly who live alone

X

Frequency

11

3

X

X

4

7

Total

25

Table 5 Inventory of ubiquitous applications in a late model Toyota Levina

Technology

Microprocessor

Microprocessor integrated into everyday object

Omnipresent

Calm

MP3s

X

Game ports

X

Navigation systems

X

Seating memory

X

X

X

Climate control

X

X

X

Automatic transmission

X

X

X

X

Trip meter/ fuel consumption

X

X

X

X

Hands free phone

X

Key alarm (left in ignition)

X

X

X

X X

Lights on alarm

X

X

X

X

Automatic lighting when you open the door and stays on till you drive off.

X

X

X

X

Parking proximity alarm

X

X

X

X

Radar alert

X

X

X

X

Cruise Control

X

X

X

X

Anti-lock breaking

X

X

X

Airbags

X

X

X

Anti-theft immobiliser

X

X

X

Seat belt on/occupancy/heating

X

X

X

X

X

X

Audible line marking Electronic Windows

X

Frequency

18

Total

11

X

X

13

16 58

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the car are two common environments for ubiquitous computing environments and that is my rationale for choosing them here. For each, I identified a list of ubiquitous computing devices. Based on my research into the definitions for a ubiquitous computing environment, I adopted four criteria; the presence of a microprocessor, whether the microprocessor was integrated into an everyday object, whether the application appeared omnipresent and whether it fit with the calm computing aspect of computing blending into the background. The result of my rough analysis is contained in table 1 for the home environment and table 2 for the car environment. Overall, I was able to identify almost twice the ubiquitous computing applications for the car environment as compared to the home environment. Almost all of the applications involved the embedding of a microprocessor except the audible line marking for the car. More of the applications identified in the car that were embedded into everyday articles seemed omnipresent and also blended into the background. Remember that I restricted my list to ubiquitous computing applications that already existed in either the car or home environment respectively. Other urban ubiquitous technologies that are not yet integrated into the home but do exist in other environments include: • • • • • •

Smart, self cleaning loos Bathroom tap sensors Hand dryer sensors Bin lid sensors Automatic sliding/opening doors Electronic drape drawing

Many of these urban applications are driven by a need for hygiene. Particularly in the current climate of easy and incredibly fast global travel there is a potential for a human plague of gigantic proportions to spread around the world. Current examples of SARS, avian flu and swine flu are all examples. One key way to fight the spread of disease is to minimise contact with communal artefacts such as bathroom tags, door handles and bin lids. Bathroom tap sensors allow users to use the facilities without touching the taps. An interesting additional concept is that of cleaning registries for public facilities. That is, where the facility meets a certain quality of cleanliness standard by being checked every certain number of hours. These are notorious for not being updated correctly. However, consider the notion of a ubiquitous bathroom cleaning register that is updated by the presence of an employee badge and maybe a cleaning cart. This information is very difficult to fabricate while also being very easy to keep current and can be very crucial information in areas highly populated by humans. So, what is driving the development of ubiquitous technology in cars and why is the development so far ahead of similar development in homes? First, let us consider the life of each environment. The overall life of a car (10-20 years) is much shorter than a house life, which might be anywhere between 50 and 100 years and potentially longer depending on construction materials and location. However, the average household appliance life is shorter, possibly 5-10 years. And houses very rarely remain in their original condition for their life and would be remodelled every 10-20 years, particularly around the kitchen and bathroom area. Therefore, the life of each environment does not seem to be much different for either environment. Next, let us consider the potential danger of the environment. The general perception is that the car environment is more dangerous than the house environment. However, statistics do not support this perception. The total number of road deaths in New Zealand over the last 12 months is 371 (number includes pedestrians, cyclists and motorcyclists). It is estimated that 500 people will die in New Zealand

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during 2009 as a results of an injury sustained at home http://www.homesafety.co.nz/didyouknowpresentation/ 65,000 NZers will be injured in their own home each year. (http://www.homesafety.co.nz/ Article.aspx?ArticleId=18 ACC 2008) 11,667 injuries occurred in New Zealand during 2007 (http://www. transport.govt.nz/annual-statistics-2007/ ACC2007). Therefore, statistically, people are injured in and around the home than they are in motor vehicles. Therefore, the home environment is more dangerous. However, both environments are proven to be dangerous, suggesting that the amount of danger associated with the environment is not driving the faster development of ubiquitous computing devices. Next, let us consider consumer convenience. This afternoon I watched as my husband got in our 10 year old Toyota and then got out to close the rear door correctly. A warning light on the dash showed that the door was open. Ok, so sure, there are safety issues with a car and one would certainly not want to be hurtling down the highway at 100km per hour and have a door come open as you go around a sweeping bend. Wouldn’t it be great to have your own personal display that could show, on leaving the house, which doors and windows were open? Granted, this is not a life and death issue, however, it is fully achievable with existing technology for not very much money. Keyless entry has been possible for modern cars for the past 10 years. Computer laboratories and tutorial rooms have been ‘keyless’ in some way even from the early 1990’s with combination locks that require the users to punch in a remembered code. Modern day lecture theatres have keyless entry through RFID enabled swipe cards. However, homes still come with a key to the front door. Not that the dwellers don’t want keyless entry – take for example the remote opening garage and front gate. These allow users to open the garage using a remote control. This is still a far cry from central locking in the car environment. Overall, I think that the most telling factor of why ubiquitous devices are more prevalent in the car environment than in the home environment is simply because automobiles are made in factories by robotic production lines. Automobiles benefit from a wide range of technologies including aerodynamic designs, engine performance, conceptual design models and so on. Houses do not benefit from technology in quite the same way. Houses can be manufactured in pieces and installed on the site, however, there are teams of contractors who must install various aspects of the house and whilst houses generally do benefit from technology such as strength testing and innovation in new materials (such as weather boards with more durable surfaces) they don’t benefit from the same level of technological innovation as for an automobile. The follow on from this is that the home environment lags behind the car environment. However, it is highly likely that trends or applications developed in the car environment will begin to apply to the home environment.

trends It’s nice to have future ubiquitous features for the car. Music choice, by collecting volume information when playing MP3s the system could make a decision about how much I like that particular song by the volume that I play it at and then recommend other similar songs based on that information. Aggregated with several hours of listening preferences, such a facility could be very powerful. IPv6, with its extended address spacing, allows for more IP addresses which will enable enough unique addresses to allow electronic appliances other than PCs to be on the Internet. This will allow domestic appliances such as washing machines, fridges, TV box top sets and motor vehicles to have access to the Internet. IPv6 also allows for enhanced routing and classification which will be necessary with so much more traffic on the Internet.

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Contactless payment will likely be a boom area. Contactless payment will like take over from magnet strip reader systems. The advantage of contactless payment is that the payment process will be faster; it is possible for the payment to be executed without the user removing the associated object from their wallet or purse. Contactless payment systems have already been implemented in high congestion public transport systems such at the Hong Kong International airport. This will be important to speed up checkout process, especially in larger stores with many stores already investing in network infrastructure to make electronic eftpos transactions faster and faster processing meaning smaller cues, or probably more likely less check-out operators. These trends will see telecommunications companies take a much more important position in our environment. Service oriented employment opportunities are likely to decrease. Entirely unmanned service stations already exist where the customer processes the electronic payment and fills up their petrol tank. Banks now actively encourage customers to use Internet banking and ATMs, thus resulting in fewer bank clerk positions. Some airlines have automated check-in where the customer uses a printed two-dimensional barcode to check-in bags and confirm their booking at the airport. This trend is likely to increase in sales and service areas. For example, fashion stores may implement intelligent mirrors that recommend fashion choices, sizes and colours. Jewellery stores may implement sales assistance systems that allow the customer to virtually try out jewellery still at design concept stages. It is very likely that there will be more work in the short term at least services such complex systems. Home automation is likely to increase. Although I have shown in this chapter that the integration of pervasive equipment is slower in houses when compared with cars, I am sure that homes will begin to have such features as keyless entry and locking alarms that alert the owner to open windows on leaving the house. Houses may even evolve to have the equivalent to power windows and blinds. Powerful X10 technology has been available on the market for many years. X10 technology uses the existing power circuit around the house as a network. However, it may be the emergence of the Personal Area Network (PAN) which enables more pervasive technologies in the home because houses more than automobiles are shared spaces and with shared spaces comes complexity and a need for some generality. This perhaps makes pervasive systems within the home much more complex. Perhaps PAN will enable some customisation of shared spaces within the home according to the PAN present. Automatic identification is also likely to increase. RFID tags in some form or another will eventually replace or be incorporated with most barcodes. Every product will be identified with an RFID enabled identification method. Every appliance, vehicle and device will be identified by a unique Internet address. People too will automatically identifiable through RFID tag identification. Mobile phone use is likely to increase. Mobile phone ownership is in reach of many with further price barriers likely to fall. Mobile phones are likely to replace residential phones and could also replace all office phones. Mobile phones will most likely become enabled by voice over IP (VOIP) thus removing the need for cell phone networks. In addition, the mobile phone has been integrated with so many other personal features such as games, music, television and organisers. Users can use their mobile phones to take pictures, set alarms, tell the time and organise their schedule. In addition, second level applications are beginning to become reality such as extended packaging, taking of customer surveys, health monitoring systems that take information from other devices (example heart rate) and relay it to the user via the mobile phone. Even gym exercise plans that use cycle and weight machines and record this information in a history to create motivation and a record of improvement have been proposed. Personalisation is also likely to increase, whether the devices are incorporated into wearable fabrics – such as the vests used by sports athletes to monitor performance or maybe the devices are an extension of the personal mobile phone or are part of personalised medical equipment. As the population of the world increases, it seems that the ability to treat people as individuals also increases. Such a high level

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of personalisation can also mean a high level of personal safety. For example, school children often make their way to and from school unaccompanied. However, they are also electronically protected to check their safe arrival and departure. Globalisation is likely to increase further still. At the desktop computing level, it is becoming easier to video conference. The introduction of applications such as Skype and GoToMeeting has made it possible to conduct virtual group meetings and collaborations without needing highly costly and fixed video conferencing equipment. Skype has already moved into the mobile phone market and it is likely that others will follow. This means that collaborative teams of researchers based around the world can meet more often and work together more interactively.

the Way forWard Wireless networks such as BlueTooth and Zigbee are the fabric of the future that will allow society to weave a rich tapestry of wireless sensors and collections of ambient data and information. As you will be well aware, wireless technology is weakened by many common building materials such as steel and concrete. Therefore, in addition to providing sandboxes for computer science technical development, business development, healthcare and personal development, there is an immense need for sandboxes for architects to be able to play with and understand the technology and its limitations. On the forefront of development, new buildings and designs need to take into consideration the communication needs and develop approaches to building construction that can facilitate pervasive communication. For example http://www.youtube.com/watch?v=dGl9T4fxeoM shows an example of using coloured cards to control lighting (in this case a flight of stairs) and the sounds in a building. The user can place and remove a number of differently coloured cards onto a white table and this action causes the lighting of an internal stair well to change. The internal workings involve RFID tags and a reader as well as a microprocessor and various coloured lights installed in the lighting for the stairwell. This application has pure ‘toy’ status at the moment and you can see how such a demonstrator or sandbox has the potential to stimulate architects to think about wireless communication technology friendly building architectures. However, the legacy of many buildings will remain with us for many decades to come and therefore, designers for architecture and computer science will need to collaborate on innovative ways to overcome existing wireless telecommunication unfriendly situations. To return to the original question about why ubiquitous technologies have developed quicker in cars than houses, I think that this question has become much more symbolic of a much larger issue. If you take the houses to represent buildings and not make distinctions between private and public dwelling spaces, it is important to begin to influence the development of building structures now to intervene in a legacy of telecommunications unfriendly buildings. The forefront of building development that is telecommunications friendly seems to be the apartment complex, especially where there is a lot of control asserted by the developer as is the case in retirement complexes and villages. Many such complexes have instigated fully digital telecommunications systems where telephone access is enabled through the Internet (VOIP). Developer led retirement complexes are a little more like the major car manufacturers when you consider that retirement complexes are a little more technologically advanced to cater for their elderly residents more. My overall feeling is that ubiquitous development is encouraged by highly technologically sophisticated environments. That is why we see the motor vehicle advancing so quickly into the ubiquitous realm. Other transport areas are probably following, such as bus, rail and commuter ferry. To what extent, I am not qualified to comment. Similarly, medical establishments appear to be more interested in techno-

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logical solutions, of which retirement home complexes are part. As this development is accepted into mainstream use (i.e., it becomes part of the ‘background’) it also will extend to less technologically developed areas. In this way, so called toy applications of technological developments are very important because they challenge conventional understanding and also provide a path for more mainstream development of ubiquitous technologies. So, yes, I hope that we will see warning lights on the dash of houses that show that the windows are open, or better yet, such intelligence that my house knows when I have left and shuts all the windows for me.

acknoWledgeMents I have drawn on many of my personal professional networks and experiences in writing this chapter. Many of my personal contacts are mentioned in this chapter and for sharing your ideas with me, I thank you. I hope that I have similarly inspired you.

references Anderson, M. (2009, May). Update: RFID chips gain computing skills. IEEE Spectrum. Ayoade J.. & Symonds J. (2009). RFID for Identification of Stolen/Lost Items. In J. Symonds, J. Ayoade & D. Parry (Eds.), Auto-identification and ubiquitous computing applications. Hershey, PA: Information Science Reference. Bieliková, M., & Krajcovic, T. (2001, October). Ambient intelligence within a home environment. ERCIM News, 47. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Hansmann U., Merk L., Nicklous M.S., & Stober T. (2003). Pervasive Computing (2nd ed.). New York: Springer. Hevner, A.R., March, S.T., Park, J., & Ram, S. (2004). Design science in information systems research. Management Information Systems Quarterly. Venkatesh, V, & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. Junglas, I., & Watson, R. (2003). U-commerce: A conceptual extension of e- commerce and m-commerce. ICIS 2003 Proceedings. Nielsen, J. (1992). The usability engineering life cycle. Computer, 25(3), 12-22. Ravindranath, L., Padmanabhan, V.N., & Agrawa, P. (2008). SixthSense: RFID-based enterprise intelligence. Paper presented at MobiSys’08 June 17-20, Breckenridge, Colorado, USA. Watson, R. T., Pitt, L.F., Berthon, P., & Zinkhan, G.M. (2002). U-commerce: expanding the universe of marketing. Journal of the Academy of Marketing Science. 30(4), 329-343.

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Weiser, M. (1991). The computer for the 21st century. Retrieved from http://www.ubiq.com/hypertext/ weiser/SciAmDraft3.html Weiser, M. (1996). Ubiquitous computing. Zelkha, E., & Epstein, B. (1998). From devices to “Ambient Intelligence.” Paper presented at the Digital Living Room Conference, June 1998.

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About the Editor

Judith Symonds is a senior lecturer at AUT University (Auckland, New Zealand). Judith serves as Editor-in-Chief of the International Journal of Advanced Pervasive and Ubiquitous Computing. Judith holds a PhD in rural systems management from the University of Queensland (Australia, 2005). Judith has published in international refereed journals, book chapters, and conferences, including the Australian Journal of Information Systems and the Journal of Cases on Information Technology. She currently serves on editorial boards for the Journal of Electronic Commerce in Organizations and the International Journal of E-Business Research. Her current research interests include data management in pervasive and ubiquitous computing environments.

Section I

Fundamental Concepts and Theories This section serves as the foundation for this exhaustive reference source by addressing crucial theories essential to the understanding of ubiquitous and pervasive computing. Chapters found within this section provide a framework in which to position ubiquitous and pervasive tools and technologies within the field of information science and technology. Individual contributions provide overviews of ubiquitous grids, ambient intelligence, ubiquitous networking, and radio frequency identification (RFID). Within this introductory section, the reader can learn and choose from a compendium of expert research on the elemental theories underscoring the research and application of ubiquitous and pervasive computing.

1

Chapter 1.1

Introduction to Ubiquitous Computing Max Mühlhäuser Technische Universität Darmstadt, Germany Iryna Gurevych Technische Universität Darmstadt, Germany

a brIef hIstory of ubIquItous coMputIng Mark Weiser The term ubiquitous computing was coined and introduced by the late Mark Weiser (1952-1999). He worked at the Xerox Palo Alto Research Center (PARC, now an independent organization). PARC was more or less the birthplace of many developments that marked the PC era, such as the mouse, windows-based user interfaces, and the desktop metaphor (note that Xerox STAR preceded the Apple Lisa, which again preceded Microsoft Windows), laser printers, many concepts of computer supported cooperative work (CSCW) and media spaces, and much more. This success is contributed (among other reasons) to the fact that PARC managed to integrate technology research and humanities research (computer science and “human factors” in particular) in a

truly interdisciplinary way. This is important to bear in mind since a considerable number of publications argue that the difference between UC and Ambient Intelligence was the more technology/networks-centered focus of the former and the more interdisciplinary nature of the latter that considered human and societal factors. We do not agree with this argument, in particular due to the nature of the original UC research at PARC—and the fact that quite a number of UC research labs worldwide try to follow the PARC mindset. Indeed, Mark Weiser concentrated so much on user aspects that quite a number of his first prototypes were mere mockups: during corresponding user studies, users had to imagine the technology side of the devices investigated and focus on use cases, ideal form factors and desired features, integration into a pretend intelligent environment, and so forth.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Introduction to Ubiquitous Computing

Weiser’s Vision of UC Mark Weiser’s ideas were first exposed to a large worldwide audience by way of his famous article The Computer of the 21st Century, published in Scientific American in 1991. A preprint version of this article is publicly available at: http://www. ubiq.com/hypertext/weiser/SciAmDraft3.html. Maybe the most frequently cited quotation from this article reads as follows: “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” This was Mark’s vision for the final step in a development away from “standard PCs”, towards a proliferation and diversification of interconnected computerbased devices. A deeper understanding of Mark Weiser’s visions can be drawn from his position towards three dominant, maybe overhyped trends in computer science at his time: virtual reality, artificial intelligence, and user agents. With a good sense for how to raise public attention, Mark criticized these three trends as leading in the wrong direction and positioned UC as a kind of “opposite trend“. We will follow Mark’s arguments for a short while and take a less dramatic view afterwards.

UC vs. Virtual Reality (VR) According to Mark, VR “brings the world into the computer”, whereas UC “brings the computer into the world”. What he meant was that VR technology is generally based on elaborate models of an existing or imagined (excerpt of the) world. This model contains not only 3D (geometric) aspects but many more static and dynamic descriptions of what is modeled. For instance, digital mockups of cars have been pushed to the point of simulating crash tests based on the car /obstacle geometry, static, and dynamic material characteristics, laws of physics, and so forth. As the sophistication of models grows, more and more aspects of the world are entered into the computer, finally

2

almost everything happens in the virtual space and even the human becomes a peripheral device for the computer, attached via data gloves and head-mounted displays. Mark Weiser criticized mainly the central and peripheral roles of computers and humans, respectively. He proposed to follow the UC vision in order to invert these roles: by abandoning the central role of computers and by embedding them in the environment (in physical objects, in particular), room is made for the human in the center. In this context, he used the term “embodied virtuality” as a synonym for UC. The cartoons in Figure 1 were made by Mark Weiser and provided by courtesy of PARC, the Palo Alto Research Center, Inc.

UC vs. Artificial Intelligence (AI) In essence, Mark Weiser criticized the overly high expectations associated with AI in the 1980’s. In the late 1980’s and early 1990’s, that is, at the time when he developed his UC vision, AI research had to undergo a serious confidence crisis. The term AI had not been associated with a commonly accepted, reasonably realistic definition, so that the association with human intelligence (or the human brain) was destined to lead to disappointments. The AI hype had provided researchers with considerable funds—but only for a while. Mark Weiser proposed to take a different approach towards a higher level of sophistication of computer-based solutions (which had been the goal of AI at large). He considered it a more reasonable objective to concentrate on small subsets of “intelligent behavior” and to dedicate each computer to such a subset. Higher sophistication would be fostered by interconnecting the special-purpose computers and by making them cooperate. This reasoning lead to the term smart, considered more modest than the term intelligent. Sensor technology plays an important role in dedicating computers to a small subset of “understanding the world around us” (a key element of intelligent behavior). By widely deploying and interconnect-

Introduction to Ubiquitous Computing

Figure 1. Mark Weiser’s cartoons about UC vs. virtual reality

ing sensor-based tiny computers, one would be able to integrate environmental data (location, temperature, lighting, movement, etc.) and use this information to produce smart behavior of computers and computerized physical objects.

2.

UC vs. User Agents (UA) In contrast to virtual reality and artificial intelligence, the term user agent is not very prominent in the general public. At the time referred to, UAs were thought as intelligent intermediaries between the user and the computer world, that is, as an approach towards increased ease-of-use or better human-computer interaction. User agents were often compared to the common perception of British butlers who are very discreet and unobtrusive, but always at disposal and extremely knowledgeable about the wishes and habits of their employers. Following this analogy, UAs were installed as autonomous software components between applications and users, inspecting and learning from the user-software application. Mark Weiser challenged five requirements usually derived from this analogy for user agents and proposed UA as a better alternative for the first three; as to the last two, he judged the necessary base technology as immature: 1.

UAs were supposed to give advice to their users based on what they had learned. Mark

3.

4.

5.

Weiser asked, in essence, why they would not do the job themselves—a promise that UC should fulfill; UAs were supposed to obey the user, for example, by applying planning algorithms to basic operations with the aim to fulfill the goals set by a user. In contrast to this approach, UC was intended to behave rather proactively, that is, to propose and even act in advance as opposed to reacting on command; A third widespread requirement suggested that UAs should intercept the user-application interface. UC in contrast should be more radical and take over the interaction or carry out functions on its own—an approach presumed by Mark Weiser to be the only viable one if humans were to be surrounded by hundreds of computers; A basic assumption about UAs was that they would listen to the (interactions of) the user. Mark Weiser considered natural language processing technology and speech recognition technology at his time to be far too immature to promise satisfying results in this respect; UAs should learn the users’ preferences, wishes, and so forth by observation. Again, the necessary (machine learning) technology was judged to be too immature to live up to this promise.

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Introduction to Ubiquitous Computing

We will resume the VR / AI / UA discussion in the next large section.

Mark Weiser’s Three Key Devices We want to finish this lengthy but still extremely compressed, much simplifying and abstracting treatment of Mark Weiser’s contributions by looking at three devices. These complementary UC devices were prototyped at his lab; investigated in the context of PARC’s typical creative, team-oriented setting, all three were thought as electronic replacements for the common “analog” information appliances. The Xerox “Pad” can be considered to be the prototype and father of present PDA’s, introduced even before Apple’s Newton appeared in 1993. The initial concept was that of an electronic equivalent to “inch-size” information bearers, namely “PostIt Notes”: easy to create and to stick almost everywhere, available in large quantities. As the PDA analogy suggests, the prototypes had a lot more functionality than PostIt Notes—but were also a lot more expensive and cumbersome to handle by design (not only due to short and mid-term technology limitations). The Xerox “Tab” can be considered to be the prototype and father of present Tablet PC’s. The analogy from the traditional world was that of a “foot-size” information bearer, namely a notebook or notepad. One may infer from the rather stalling market penetration of Tablet PC’s that technology is still not ready for mass market “Tabs” today, but one may also expect to find a pen centric, foot size, handheld computer to become very successful any time soon. An interesting facet of the original Tab concept was the idea that Tabs would in the future lay around for free use pretty much as one finds paper notebooks today, for example, as part of the complementary stationery offered to meeting participants. The Xerox “Liveboard” was the prototype of present electronic whiteboards. A PARC spinoff company designed and marketed such boards,

4

and today many companies like Calgary-based SmartTechnologies Inc. still sell such devices. Liveboards represented the “yard-size” information bearers in the family of cooperating devices for cooperating people. In contrast to many devices sold today, Liveboards supported multi-user input pretty early on. The developments and studies conducted at Mark Weiser’s lab emphasized the combination of the three device types for computer supported cooperation, and cooperative knowledge work in particular. While Mark Weiser was a truly outstanding visionary person with respect to predicting the future of hardware, that is, UC nodes (proliferation of worn and embedded networked devices, specialized instead of personal general-purpose computers, numbers by far exceeding the number of human users), two other people were more instrumental in generating awareness for the two remaining big challenges mentioned in the preface of this book, namely integrative cooperation and humane computing; the former of these challenges was emphasized by Kevin Kelly, the latter by Don Norman. A deeper analysis reveals that for the second aspect, humane computing, it is very difficult to argue about the true protagonists. Readers remember that Mark Weiser was actually placing a lot of emphasis on usability, by virtue of his education and mindset and in the context of the human focus of PARC. He also coined the exaggerated term “invisible” for mature technology. On the other hand, Don Norman was not advocating the humane computing challenge in all its facets yet. Nevertheless, we want to highlight him next as maybe the single most important advocate of this challenge.

the book Out of Control by kevin kelly In 1994, K. Kelly published a book entitled Out of Control. The thoughts expressed by Kelly were an excellent complement to Mark Weiser’s

Introduction to Ubiquitous Computing

publications. While the latter emphasized the emergence of networked small “neuron like” (i.e., smart) UC nodes, Kelly emphasized the integrated whole that these neurons should form. His starting argument was the substantiated observation that the complexity of the made, that is, of humanmade systems or technology, approached the complexity of the born, that is, of “nature-made” systems, such as human or biological organisms, human or biological societies (cf. ant colonies), and so forth. This observation led to the obvious requirement to investigate the intrinsic principles and mechanisms of how the born organized, evolved, and so forth. By properly adopting these principles to ‘”the made”, this complexity might be coped with. Research about the organization and evolution of the born should be particularly concerned with questions such as: how do they cope with errors, with change, with control, with goals, and so forth. For instance, beehives were found not to follow a controlling head (the queen bee does not fulfill this function), and it is often very difficult to discern primary from subordinate goals and to find out how goals of the whole are realized as goals of the individuals in a totally decentralized setting. Kevin Kelly summarizes central findings and laws of nature several times with different foci. Therefore, it is not possible to list and discuss these partly conflicting findings here in detail. An incomplete list of perceived central laws “of God” reads as follows: (1) give away control: make individuals autonomous, endow them with responsible behavior as parts of the whole, (2) accept errors, even “build it in” as an essential means for selection and constant adaptation and optimization, (3) distribute control truly, that is, try to live with no central instance at all, (4) promote chunks of different kinds (e.g., hierarchies) for taming complexity, and (5) accept heterogeneity and disequilibrium as sound bases for survival.

the book The Invisible Computer by donald norman Don Norman emphasized the “humane computing” grand challenge described in the preface of this book. World renowned as an expert on usability and user-centered design, he published The Invisible Computer in 1999. He considered the usability problems of PC’s to be intrinsically related to their general-purpose nature and thus perceived the dawning UC era more as a chance than a risk for humane computing. The intrinsic usability problems that he attributed to PCs were rooted in two main anomalies, according to Don Norman: (1) PCs try to be all-purpose and all-user devices—a fact that makes them overly complex, and (2) PC’s are isolated and separated from daily work and life; truly intuitive use—in the context of known daily tasks—is therefore hardly possible. From this analysis, Norman derived various design guidelines, patterns, and methodological implications, which we will summarize again at an extremely coarse level: 1.

2.

3.

4.

He advocated UC nodes using the term “information appliances”: dedicated to a specific task or problem, they can be far simpler and more optimized; He further advocated user-centered development: especially with a specific user group in mind, “information appliances” as described previously can be further tailored to optimally support their users; Norman stated three key axioms, that is, basic goals to be pursued during design and development: simplicity (a drastic contrast to the epidemic “featurism” of PC software), versatility, and pleasurability as an often forgotten yet success critical factor; As a cross-reference to the second big UC challenge (integrative cooperation), he advocated “families of appliances” that can be easily and very flexibly composed into systems.

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Introduction to Ubiquitous Computing

history revised The preceding paragraphs are important to know for a deeper understanding of the mindset and roots of UC. However, about 15 years after the time when the corresponding arguments were exchanged, it is important to review them critically in the light of what has happened since. We will first revise the three “religious disputes” that Mark Weiser conducted against AI, VR, and UAs. To put the bottom line first, the word “versus” should rather be replaced by “and” today, meaning that the scientific disciplines mentioned should be (and have, mostly) reconciled: As to UC and VR, specialized nodes in a global UC network can only contribute to a meaningful holistic purpose if models exist that help to cooperatively process the many specialist purposes of the UC nodes. In other words, we need the computer embedded into the world and the world embedded in the computer. Real Time Enterprises are a good example for very complex models—in this case, of enterprises—for which the large-scale deployment of UC technology provides online connectivity to the computers embedded into the world, that is, specialized nodes (appliances, smart labels, etc.). In this case, the complex models are usually not considered VR models, but they play the same role as VR models in Mark Weiser’s arguments. The progress made in the area of augmented reality is another excellent example of the benefit of reconciliation between UC and VR: in corresponding applications, real-world vision and virtual (graphical) worlds are tightly synchronized and overlaid. As to UC and AI, Mark Weiser had not addressed the issue of how interconnected, smart, that is, “modest”, specialized nodes would be integrated into a sophisticated holistic solution. If the difference between AI and the functionality of a single smart UC node (e.g., temperature sensor) was comparable to the difference between a brain and a few neurons, then how can the equivalent of the transition (evolution) from five pounds of

6

neurons to a well-functioning brain be achieved? Mark Weiser did not have a good answer to that question—such an answer would have “sounded like AI” anyway. Today, there is still not a simple answer yet. The most sophisticated computer science technology is needed in order to meet the integration challenge of how to make a meaningful whole out of the interconnected UC nodes. However, the state of the art has advanced a lot and our understanding for what can be achieved and what not (in short term) has improved. For instance, socionic and bionic approaches have become recognized research areas. A mature set of methods and algorithms is taught in typical “Introduction to AI” classes today and has replaced the ill-defined, fuzzy former understanding of the area. Thus the boundaries between AI and computer science are more blurred than ever and their discussion is left to the public and press. As to UC and UAs, remember that Mark Weiser considered UAs as “too little” in terms of what they attempted (at least too little for the UC world envisioned by him), yet “too much” in terms of what the underlying technology was able to provide. This left doubts about how the even more ambitious goals of UC could be met, namely active (proactive, autonomous, even responsible) rather than reactive (obeying) behavior. In other words, Mark Weiser was right when he advocated active as opposed to reactive behavior, but he had little to offer for getting there. Luckily, the technologies that he had then considered immature (e.g., speech processing, NLP, machine learning) have advanced a lot since. All in all, Mark Weiser’s arguments from 15 years ago (1) provide a deep understanding of the field, (2) should be modified towards a more conciliatory attitude (in particular with respect to AI and VR / complex “world models”), and (3) have become more substantiated in certain respects since technology advancements make some of his more audacious assumptions more realistic (but most visions of his “opponents”, too). In other

Introduction to Ubiquitous Computing

words, Mark Weiser’s visions were and still are marking the research and developments made by the UC community. His concepts and predictions were accurate to a degree that was hardly paralleled by any other visionary person. Restrictions apply as to his overly drastic opposition to VR, AI, and UAs: some of the exaggerated promises of these were repeated by him in the UC context - right when he denounced the over-expectations raised by AI and UAs! VR and AI in particular should be reconciled with UC. Maybe Weiser underestimated the two grand challenges of the UC era, namely “integrative cooperation” and “humane computing”. Kevin Kelly and Donald Norman emphasized these two challenges, respectively. Looking at the advancements in totally decentralized systems, Kelly’s promises can be evaluated as too extreme today: bionics social science inspired, and autonomic (or autonomous) computing have advanced a lot. However, two restrictions still apply: (1) less decentralized systems still prove to be extremely viable in daily operation—it will be hard for fully decentralized systems to really prove their superiority in practice; (2) system-wide goals must still be planned by some centralized authority and—to a certain extent manually—translated into methods for fully decentralized goal pursuit; evolution-like approaches that would generate optimization rules and their pursuit automatically in a fully decentralized systems are still hardly viable. As a consequence, the present book will not only describe the above-mentioned computing approaches in part “Scalability”, but also other aspects of scalability. As to Don Norman, he was right to advocate simplicity as a primary and key challenge. However, he maybe underestimated the ‘humane computing’ problems associated with the nomadic characteristics and ‘integrative cooperation’ challenge of the UC era. The usability of the integrated whole that we advocate to build out of UC nodes is by far not automatically endowed with easy-to-use user interaction just because the

participating appliances exhibit a high degree of usability. On the other hand, only the integration that is, federation of miniature appliances with large interaction devices (wall displays, room surround sound, etc.) may be able to provide the usability desired for an individual device. As we conclude this section, we should not forget to mention that the UC era was of course not only marked by just three visionary people.

terMs and selected standards While there is a lot of agreement among researchers and practitioners worldwide that the third era of computing is dawning as the era of networked, worn/portable and embedded computers, there is not so much agreement about what to call that era. This fact is something of an obstacle, for instance for wider recognition in politics (the crowd does not scream the same name as one may put it). This situation is aggravated by the fact that partial issues and aspects of Ubiquitous Computing are also suffering from buzzword inflation. With this background in mind, one may understand why we list a considerable number of these buzzwords below and provide a short explanation, rather than swapping this issue out into a glossary alone. Knowledge of the following terms is indeed necessary for attaining a decent level of “UC literacy”.

synonyms for ubiquitous Computing First, we want to look at the terms that describe—more or less—the third era of computing as introduced: •

Post-PC era: The root of this term is obvious, it describes ‘the era that comes after the second, that is, the PC era. We suggest avoiding this term since it points at what it

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Introduction to Ubiquitous Computing

•

•

•

8

is not (PC’s) rather than at what it actually is. Pervasive computing: A distinction between the word ubiquitous and pervasive is difficult if not artificial. One could argue that the term pervasive eludes more to the process of penetration (i.e., to the verb pervade) whereas ubiquitous eludes more to the final state of this process. We suggest that pervasive computing and ubiquitous computing are synonyms, one (pervasive) being slightly more common in industry (its origin has been attributed to IBM), the other one (UC) being slightly more common in academia. Ubiquitous computing: The term may be interpreted as “computers everywhere”. We are using it as the notion for the third era of computing throughout the book and prefer it, among others, because we try to fight buzzword mania and dislike the invention of additional terms for a named concept. We therefore propose to stick to the first (reasonable) term invented and somewhat broadly accepted; since Mark Weiser is the first visionary person who sketched essential characteristics of the dawning era and since he invented the term UC, the question of what is the oldest well-known term should not be questionable. Ambient intelligence: This term was invented in particular in the context of the European Union’s research framework programs (5, 6, 7). As a positive argument, one may say that the two words reflect the grand challenges of UC as stated in this book: ambient may be associated with the challenge of humane computing, making UC systems an integral part of our daily life. Intelligence may be interpreted as the challenge of integrative cooperation of the whole that consists of myriads of interconnected UC nodes. On the downside, one should remember that Mark Weiser had intentionally avoided the term

•

•

•

•

“intelligence” due to the over-expectations that AI had raised. We suggest avoiding this term, too, because it is still burdened with these over-expectations and because it is still ill defined. Disappearing / invisible / calm computing: All three terms are less common than UC and pervasive computing. Their roots have been discussed in the historical context above. Obviously, disappearing describes again a process while “invisible” describes a final state. “Calm” emphasizes hearing as opposed to vision like the other two. In any case, the terms “invisible” and “disappearing” are not very well chosen (despite our tribute to Don Norman) since computers and interfaces that have totally disappeared cannot be commanded or controlled by humans any more. Since we doubt that 100% satisfactory service to the user can be paid at all without leaving the customer, that is the user, the option to explicitly influence the service behavior, we consider the term misleading. We favor again Mark Weiser’s notion of computers that are so well interwoven with the fabric of our lives that we hardly notice them. Mixed-mode systems: This is a term used to describe the heterogeneity of UC nodes, in contrast to the rather resource rich, general purpose PC’s of the last era. This term is even less common, but pops up every now and then like those previously discussed, and should not be used to describe UC as a whole since it emphasizes a particular aspect. Tangible bits: This term has found some currency in the Netherlands and Japan, but remained rather uncommon in general. It refers mainly to the fact that networked computers are becoming part of the physical world. Real time enterprise: This term has been explained in the preface of the book and is not

Introduction to Ubiquitous Computing

thought as a synonym for UC, but rather as a very important and cutting-edge application domain that may drive down the learning curve, that is, prices of UC hardware and solutions. It was mentioned in the preface that some authors argued in favor of one or the other of the UC synonyms, saying that their choice was more farreaching in time (the other ones being intermediate steps) or space (the other ones only comprising a subset of the relevant issues). However, we cannot follow these arguments, mainly because research labs and projects around the world work on the same subjects, some more advanced or holistic, some less ambitious or more specialized, carrying the names UC, pervasive computing, and ambient intelligence rather randomly.

towards a taxonomy of uc nodes Throughout this book, UC nodes will be categorized according to different aspects. In the context of reference architectures further below, we will emphasize the role of UC nodes in a holistic picture. In the present paragraph, we want to try categorizing them as devices. It should be noted that in the preface of the book, we already provided a preliminary, light weight introduction. The difference between carried (worn, portable) and encountered nodes was emphasized and four preliminary categories (wearables, sensors, appliances, and smart labels) were briefly described. It soon became clear that smart labels attached to goods must be distinguished again from those attached to humans, although the base technology may be the same. In a second, more serious attempt to categorize UC nodes as device categories, we propose the following distinction (see Figure 2): 1.

Devices attached to humans a. Devices carried: Here we further distinguish three subcategories: (1) mobile

2.

devices, synonymous with portable devices, contain rather general purpose computers and range from laptops via PDA’s to mobile phones and the like, (2) smart badges, that is, smart labels serve for identification, authentication and authorization of humans and possibly further purposes, and (3) body sensors of all kinds play an increasingly important role in particular in the fitness and health context; b. Devices worn: These wearables range from truly sophisticated, computeraugmented cloths and accessories to prototypes that are built from standard components (PDA in a holster with headset, etc.). A further categorization is not attempted since the spectrum is rather blurred; c. Devices implanted: while there is a lot of hype about implanted RFID tags and networked health implants, the many issues (e.g., health, privacy, or dependability) around the necessary device-environment communication have not permitted this category to become widespread. Devices encountered a. Smart items denote computer-augmented physical objects. The terms “smart object” and “smart product” are used with subtle differences depending on the context to denote more sophisticated variants of smart items, such as smart items that proactively communicate with the users. We suggest treating smart items as the most general term and to distinguish the following subcategories: (1) smart tags as the least sophisticated variant: they can be considered to be mimicry for embedded computers: by attaching a smart tag to a physical object, a physically remote computer (often

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Introduction to Ubiquitous Computing

Figure 2. Taxonomy of UC devices

b.

in proximity, though) can take over some of the functionality that would be embedded otherwise. This approach opens the door for turning even the cheapest products into UC nodes. The term “smart label” is sometimes used synonymously; sometimes it is used as the comprehensive term for smart tags and smart badges (attached to humans, see earlier discussion). We suggest sticking to the term smart tag for the smart item sub-category described here; (2) networked sensor nodes, and (3) networked appliances denote the other subcategories of smart items. They were already introduced in the preface of this book. Smart environments denote the surroundings of smart items, that is, the additional communication and compute power installed in order to turn an assembly of smart items into a local, meaningful whole.

The reader must be aware that all terms arranged in the taxonomy are not settled yet for a common understanding. For instance, one might argue whether a sensor network that computes context information for networked appliances and users should be considered a set of smart items

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(as we defined it) or a part of the smart environment. Nevertheless, we find it useful to associate a well-defined meaning with these terms and to apply it throughout the book (see Figure 2). In addition, it should be noted that smart environments (with integrated smart items) constitute a particularly important research area—maybe because they permit researchers and project leaders to implement self-contained “little UC worlds” without a need for multiparty agreements about interoperability standards. In particular, “smart homes” were among the first subjects of investigation in the young history of UC. Prestigious projects in the smart home area were and are conducted by industry (Microsoft eHome, Philips AmbientIntelligence initiative, etc.) and academia (GeorgiaTech AwareHome, MIT House, etc.). HP made an early attempt to overcome the isolation of such incompatible islands by emphasizing standard middleware in the Cooltown project). Quite a number of projects about smart homes terminated without exciting results, not to the least due to insufficient business impact (note our argument in favor of Real Time Enterprises as a more promising subject). More recently, smart homes projects have focused on issues considered to be particularly promising, as was discussed in the preface to this book. Important areas comprise home security, energy conservation, home entertainment, and particu-

Introduction to Ubiquitous Computing

larly assisted living for the aging society—a topic considered particularly interesting in Europe (1 year prolongation of independepnt living saving about half a billion Euros in Germany alone). Renowned large-scale projects were carried out, for example, in Zwijndrecht (Belgium) and Tønsberg (Norway) in this respect.

•

a few More relevant terms A few more UC terms—and sometimes, corresponding concepts—are worth mentioning. •

•

•

•

•

Smart dust is a term used for sensor networks if the emphasis is on miniaturization and the concept is based on one-time deployment and zero maintenance. Environment data sensors are often cited as an example, the vision then is to deploy them, for instance, from an aircraft, and let them monitor the environment until they fail. Environmentfriendly degradation is a major issue here, of course. Things that think was the name of an early UC project led by Nicholas Negroponte at the MIT media lab. Other authors have since hijacked the term. Smart paper denotes the vision of a display device that would exhibit characteristics comparable to traditional paper in terms of weight, robustness, readability, and so forth, and loadable with the content of newspapers, journals, books and so forth,. it would help to save paper and revolutionize the press distribution channels and more. Many projects that were not even close to this vision had, and continue to have, the name “smart paper”. Smart wallpaper is a similar term to smart paper in that it extrapolates the above mentioned characteristics to wall-size devices. Smart : virtually every noun has been associated with the attribute smart recently, not always alluding to the

characteristics of UC nodes. For instance, smart materials are supposed to adapt to the context of use, with no IT involved. Most of the time though, smart alludes to a physical object that has been augmented with an embedded computer. The Internet of things is a term favored by the press. It is not considered appropriate as a term for UC as a whole by the authors since it emphasizes the hardware side of UC as opposed to the human side, which was already described as crucial and as a major challenge (cf. humane computing). Most publications that favor this term concentrate on the two standards discussed in the following section.

the epcglobal standard As mentioned at the beginning, we will only sketch two important standards in the UC context. Other standards are too unimportant, too immature, too specific (they might be treated in one of the focused parts of this book), or part of the background knowledge about well-established technology that this book cannot cover. The first standard to mention is EPCglobal and was mentioned in the preface of this book. As mentioned, it is meant to succeed the barcodes that encode the European article number or universal product code on current consumer products. The 96-bit Electronic Product Code EPC is usually stored on RFIDs (a subcategory of smart tags, as we can now say) and can be read: • • •

•

From a greater distance (e.g., 10m) With better reading accuracy With much less effort (e.g., en-passant by a RFID reader gate as opposed to carefully with line-of-sight connection by a barcode scanner) In bulk (RFID readers can read, for example, a hundred tags at once)

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Introduction to Ubiquitous Computing

Since the EPC contains a 36-bit serial number, individual items can be tracked and traced. For instance, theft can be much more easily attributed to criminals, product life cycles can be recorded more accurately, product lots with manufacturing errors can be called back more specifically, etc. On the other hand, the serial number may in principle be used to trace an individual, too, if she carries around an RFID tagged product. This privacy issue has raised many concerns in recent years and amplified the decision of the whole sales and retail industry to focus on tagging their containers, palettes, cases, etc., for a start. So-called item level tagging is only envisioned for highly valuable goods initially; it may enter the mass market when tag prices and system costs have come down and after settling the privacy issues. Figure 3 depicts the functioning of EPC smart tags in an overall IT infrastructure. In step 1, an EPC code is read from a product. In the example, each carton on the palette could contain a number of tagged products. The residual example would then explain the action for just one such tag. Usually prior to reading the tag, the system has already searched and discovered servers capable of ‘resolving’ certain ranges of EPC code. Based on the results of this discovery process, the appropriate ‘resolution node’, called an ONS server, is asked to resolve the EPC code, that Figure 3. RFID / EPC scheme

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is to translate it into a global Internet address where the relevant product information is actually stored. The product information is encoded in a standardized way, using the so-called product markup language PML, an XML derivate. The second generation of RFID tags introduced in 2006 features improved bulk reading (hundreds of tags simultaneously), size and cost improvements. “Printable” tags have become common: these paper labels with embedded RFID chips can be custom imprinted with custom human-readable information. The chips themselves are not altered in the printer and they come with pre-assigned EPC codes from the manufacturer.

the osgi standard The Open Services Gateway Initiative (OSGi) is an industry driven nonprofit consortium. OSGi standardized a Java virtual machine (JVM). This JVM can be considered a standardized virtual ‘computer’ that runs on any real computer and is capable of executing programs that are transmitted to it, so-called bundles. OSGi standardizes not only the format for bundles, but also the necessary protocols and procedures for authenticating and authorizing senders of bundles, for replacing and updating bundles (remote maintenance), for discovering other bundles, and so forth. OSGi

Introduction to Ubiquitous Computing

bundles are particularly useful for controlling the functionality of networked appliances. Possible use cases include SetTopBoxes, Vehicles (note that car electronics today requires much shorter maintenance cycles than the mechanical parts, especially for software updates!), consumer electronics, and so forth. As to smart homes, the favored concept is that of a residential gateway that is connected to the global Internet and receives updates for smart home appliances via OSGi. The residential gateway may then forward bundle updates and so forth to the relevant appliances if needed. OSGi has a number of deficiencies. For instance, it is not considered to be very resource effective. Nevertheless, it has tremendous impact as a de facto standard for dealing with some of the elementary aspects of coping with global UC systems in a platform and vendor independent way.

reference archItectures for ubIquItous coMputIng The Importance and Role of a reference architecture A sophisticated distributed infrastructure is needed in order to make a myriad of networked UC nodes communicate and cooperate. If interoperability is to take on a worldwide scale, means for agreement among arbitrary participants must be provided. Ideally, the move from isolated proprietary UC solutions to a world of cooperating UC components is driven by so-called reference architectures which establish several levels of agreement: on level one, a common terminology and conceptualization of UC systems is established in order for researchers and practitioners to speak the same language and to work on the same global UC vision. On the second level, a common understanding of the ensemble and components of a typical UC system is established, including the

potential roles of the components. On level three, basic functional principles can then be agreed upon. A fourth level is desirable but beyond the scope of reference architectures, that is concrete standards for intercomponent cooperation. This level is discussed in the introduction to the part Scalability.

Reference Architectures in a More Realistic World In reality, a worldwide common understanding and corresponding standards have to be developed in a struggle for the best solution. Real life has a large impact on what becomes widespread. By “real life” we mean breaking research results, industry practice, experiences gained with proprietary prototypes and realizations, user acceptance, and not least business interests defended by global industrial players. Nevertheless, the exercise of proposing and refining reference architectures—in communication with the stakeholders mentioned—plays a key role in a struggle for globally interoperable solutions. Here reference architectures must be invented and published and then consolidated and reiterated based on feedback by the stakeholders.

Prominent Examples from the Past The ISO reference architecture for open systems interconnection (OSI) was developed in the 1970s as an important step towards global networks. OSI was very successful in that it led to a common terminology and a common understanding of the components of computer networks including their roles. The fourth level aforementioned above: ISO standards for communication protocol, were not nearly as successful as the reference architecture itself. Rather, the Internet protocols TCP and IP took over almost the entire market. Nevertheless, the OSI reference architecture was extremely influential on the computer networking community as a whole and on the Internet in particular.

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Introduction to Ubiquitous Computing

Another ISO reference architecture is ODP (open distributed processing). It emphasizes complex distributed systems and applications. An influential contribution of ODP is its support for different viewpoints of various stakeholders. In particular, ODP emphasized the importance of enterprise modeling for application development. All too often, applications are modeled and built with a technology focus and thus neglect the (dynamically changing) organization they should support. ODP addresses important issues, but came at a time when distributed applications were usually rather simple: ODP was considered overkill.

•

Layered Architectures vs. Component Architectures

Although we focus on the Computer Networks / Distributed Systems aspects of UC in the remainder of this chapter, readers should note that the entire book represents a holistic approach.

Before we introduce concrete reference architectures, it is worth recalling the two complementary flavors: •

14

Layered reference architectures serve as a blueprint for layered software architectures. Both arrange sets of functions into layers that act as virtual machines: only the “what” (provided functionality and how to access it) must be known to users in higher layers, whereas the internal “how” (realization) is hidden and can be independently modified. The layer stack represents the range from higher to lower function sets, where higher means “closer to what users and applications need” and lower means “closer to what hardware provides”. Strict variants preclude higher layer components to access lower layers except for the one immediately below. Recent research has concentrated on approaches for automatic, selective custom configuration of the entire layer stack, according to the needs of applications—this trend is important in the UC world where dedicated, resource-poor UC nodes cannot host fat all-purpose layers.

Component reference architectures take a birds-eye view on the world addressed. They define a number of cooperating components or rather component types, and specify inter-component cooperation at a certain level of detail. Again, a kind of art of right-sizing exists: too few component types do not really help to understand and discern relevant roles and specializations common to the world addressed, too many component types lead to overly complex architectures and problems in matching reference and reality.

Why Component Reference Architectures are Important for UC The OSI reference architecture assumes a network consisting of rather homogeneous nodes, namely general-purpose computers with ‘sufficient’ CPU and memory capacity. Accordingly, a common definition of a computer network reads as follows: A computer network CN is a set of autonomous nodes AN, each of which disposes of CPU(s) and memory, plus a Communication Subsystem CSS capable of exchanging messages between any of the nodes: CN :== {AN} ∪ CSS. In the definition, “all nodes are created equal”. At a closer look, computer networks rely on four mandatory constituents of nodes (ANs): 1. Communication capability: The capacity of exchanging messages with other nodes through the CSS. 2. Address: A unique identifier that can be used to specify the recipient or sender of messages.

Introduction to Ubiquitous Computing

3. Processor: A general purpose CPU. 4. Memory: Means for storing—at least—incoming messages. In a UC world, resource scarcity and the special-purpose nature of many nodes are key issues. A holistic UC approach must scale from servers to sensors and support the consideration of smart labels etc. The definition of a UC node must be different from the one above—the four constituents now read as follows: 1. Communication is mandatory, but may be passive (cf. passive RFID tags) 2. Address is not necessarily a unique identifier; for example, in a sensor network, a random node out of a redundant set with identical address may provide a certain functionality 3. Processor becomes an optional constituent 4. Memory becomes an optional constituent, too With the above modifications, not all nodes are autonomous (ANs) any more.

Proposed UC Component Reference architectures The definition introduces a first possibility for distinguishing nodes as components of an application, that is, from the component architecture point of view. However, it only discerns between existing versus missing fundamental characteristics. More interesting is the aspect of different roles that nodes can play in the network—not application specific roles, but fundamental roles in the set of cooperating resources. Thus UC systems will take on more complex node topologies than what was considered in the eras of simple interprocess communication and client-server computing. In addition, a holistic approach needed for UC systems raises issues such as security, which are

important when trying to find important node types at different levels of granularity. One of the first proposals for a UC component reference architecture was made by the Fraunhofer research institute FOKUS in Berlin. They did not distinguish different node types that would assume different roles, but identified important roles that each UC node may potentially assume. Their concept is coined I-Centric Services and achieved a certain level of influence on the industrial Object Management Group (OMG). In their view, a UC node (usually a software service) should provide standard interfaces for four major issues: 1. Discovery of peers in a spontaneous, configuration-free manner 2. Maintainance, i.e., software update and revision 3. Reservation, that is, pre-allocation of some of the node’s resources as a basis for service guarantees 4. Configuration as a means for customizing the service for a dedicated role Nodes that conform to these interfaces are called super distributed objects (SDO) in this proposal. We will discuss another component architecture in some more detail since it attempts to discern between more specific roles of UC nodes. It was developed in the Telecooperation Group at the Technische Universität Darmstadt and is called Mundo, see Figure 4. Mundo distinguishes five different node types: Me, us, It, We, and they. Me (Minimal Entity): Mundo emphasizes the importance of a distinct personal UC node, that is the device tightly associated with its user: the Me. Every user uses exactly only one Me at any time. The rationale is rooted in the envisioned ubiquity of computer support in everyday life: if every step that one takes is potentially computer supported and controlled, then humans need a high level of trust that the computers “do the right thing”. For instance, users will want to make sure

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Introduction to Ubiquitous Computing

Figure 4. Mundo reference architecture

that their actions are only recorded and disclosed to the degree they consent to or that is legally imposed. As another example, they want to be sure that they only trigger actions which they understand in their legal, financial, and other consequences and that they agree to. To this end, the Mundo researchers propose to conceptualize a single, truly owned UC node type that acts in the user’s stead and controls when, how, and to what extent other UC node types are invited or chartered to participate in actions. Since computer use becomes ubiquitous, such a personally-owned node type must be carried along virtually at all times. This imposes strong requirements with respect to miniaturization, robustness, and the conflicting goals of (a) the impossibility to falsify or duplicate such a node, and (b) the possibility to replace it easily in case of theft or failure. An important research questions is concerned with the minimum functionality of a Me. Me nodes are considered as the representation of their users in the digital world—a digital persona involved in all user activities. It is a small wearable computer with minimal functionality. In order to support interaction with UC environments in a sensible way, the term minimal must be associated with a set of specific requirements regarding size, identity, security, interaction, context awareness, and networking. The design was guided by the principle that the minimal feature set of a system is determined by the worst-case environmental conditions under which the application must run satisfactorily (Satyanarayanan, 2001). This leads to a focus on speech based interac-

16

tion and it is described in detail by Aitenbichler, Kangasharju, and Mühlhäuser (2004). Any Me can augment its capabilities through association with other entities of the Mundo architecture as described next. Us (Ubiquitous aSsociable object): Minimization pressure will not permit feature-rich Mes. Hence, they must be able to connect to other mobile devices or devices embedded into the environment to offer more powerful services to their users, such as large display space. This process is called association and such devices are called ubiquitous associable objects (us). A us is a computing device that extends the user’s personal environment by adding storage, processing capacity, displays, interaction devices, and so forth. During association, the Me sends authentication information to the us, sets up a secure communication link, and personalizes the us to suit the user’s preferences and needs. For privacy reasons, any personalization of a us becomes automatically unavailable if it is out of range of the user’s Me. It (smart ITem): There are also numerous smart items that do not support association that would classify them as us. Vending machines, goods equipped with radio frequency IDs, and landmarks with “what is” functionality are just a few examples. Such devices are called smart items (Its). An It is any digital or real entity that has an identity and can communicate with a us or the Me. Communication may be active or passive. Memory and computation capabilities are optional (cf. the four constituents of a UC node described previously). We (Wireless group Environment): Ad-hoc networking is restricted to an area near to the user of a Me device, as connections with remote services will involve a non ad hoc network infrastructure. The functionality of a wireless group environment is to bring together two or more personal environments consisting of a Me and arbitrary us entities each. It enables cooperation between the devices and also allows for sharing

Introduction to Ubiquitous Computing

and transferring hardware (e.g., us devices) and software or data between We users. they (Telecooperative Hierarchical ovErlaY) stands for the backbone infrastructure as part of the Mundo component architecture. It connects users to the (nonlocal) world, and delivers services and information to the user. The they integrates different physical networks and provides transparent data access to users. Frequently used data may be cached on us devices.

are often called smart spaces or more specifically smart houses, labs, offices, homes etc. Work on these kinds of environments emphasizes the tangible, physical (computer-augmented) objects to be handled. As for smart information spaces, an interesting reference architecture was proposed in the LifeSpaces project in South Australia (Bright & Vernik, 2004). Their architecture incorporates some of the findings from ODSI and distinguishes four layers:

uc layered reference architectures 1. Many actual UC projects are based on a layered architecture. Most of them are just first approaches to software architectures, only a few of them are intended to serve as a crystallization point for the community and future standards. Nevertheless, one of them may turn out to be so successful that a future reference architecture will evolve from it. We will concentrate on a small selection of the few projects that have a general reference model in mind. They concentrate on different challenges or foci, that is their findings will have to be merged if a holistic layered architecture is to be derived. A first focus is the enterprise modeling that ODP already addressed. A reference architecture worth mentioning here is ODSI, the so-called open distributed services infrastructure (Bond, 2001). Although already outdated, ODSI was influential since it fostered the move away from ODP’s more top-down approach to a component-based, that is service based approach that supports the concept of applications being compositions of services. Other reference architectures emphasize Smart Environments. Two facets are important and investigated—still—in different camps even as to the work on reference architectures: smart information spaces and smart physical spaces. By smart information spaces, we mean environments which concentrate on cooperative treatment of ITand data/media centric work (cf. Mark Weiser’s three initial UC devices). Smart physical spaces

2.

3.

4.

Enterprise model: This layer supports rules, processes, and organizational models of roles and services in the enterprise. Coordination and control including interaction support: On this layer, a shared and persistent event space of limited capacity, and an agent-based workspace infrastructure are offered. Enterprise bus: This term refers to a communication layer based on the publish/subscribe paradigm. The service layer: Here, the core functionality is represented by easily composable services. An enterprise bus is offered for services to communicate and cooperate; this bus connects so-called peers which host the services.

As for smart physical spaces, a prominent example is the reference architecture developed by the Gator Tech Smart House project of the University of Florida (see Figure 5). The reference architecture depicted is a more recent version of what was published by Helal, Mann, El-Zabadani, King, Kaddoura, and Jansen (2005) and is included by courtesy of the authors (the commercial version is called Atlas now). For more information, the reader may consult the group’s Web Site at www. icta.ufl.edu or the Atlas Web site at www.pervasa. com. The architecture emphasizes sensors (plus actuators) and networked embedded devices at the lowest layer as the hardware foundation of UC

17

Introduction to Ubiquitous Computing

Figure 5. SmartSpace middleware layered reference architecture

applications. The OSGI standard is exploited for customizing and maintaining these sensors and embedded devices in a dedicated second layer. The third layer contains three large parts which reflect major insights into the nature of the UC world (note that these insights have a large influence on the present book, too): •

•

•

18

The context management layer reflects the importance of context-awareness for UC as a whole, as discussed in the preface of the book; The service layer reflects services (and service-oriented architectures, SOA) as the dominating paradigm for building autonomous software components in a UC setting; The knowledge layer reflects the fact that large-scale service composition cannot rely on standardized interfaces that are distributed prior to software (service) development; rather, service discovery and service interaction must rely on machine readable descriptions of the service semantics available at runtime;

•

Due to the strictly service-oriented concept used, application development boils down to service composition; the top layer offers corresponding tools.

In conclusion, it should have become clear that both a component based and a layered reference architecture, if widely accepted, would be important steps from UC islands towards truly global UC. The reference architectures presented could serve as a basis for better communication among the UC protagonists and for the necessary standards.

references Aitenbichler, E., Kangasharju, J., & Mühlhäuser, M. (2004). Talking assistant headset: A smart digital identity for ubiquitous computing. Advances in pervasive computing (pp. 279-284). Austrian Computer Society. Bond, A. (2001). ODSI: Enterprise service co-ordination. In Proceedings of the 3rd International Symposium on Distributed Objects and Applications DOA‘01 (pp. 156-164). IEEE Press.

Introduction to Ubiquitous Computing

Bright, D., & Vernik, R. (2004). LiveSpaces: An interactive ubiquitous workspace architecture for the enterprise in embedded and ubiquitous computing. Springer (pp. 982-993).

Helal, A.A., Haskell, B., Carter, J.L., Brice, R., Woelk, D., & Rusinkiewicz, M. (1999). Any time, anywhere computing: Mobile computing concepts and technology. Springer.

Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005, March). The Gator Tech Smart House: A programmable pervasive space. IEEE Computer, 38(3), 64-74.

Huber, A. J. F. & Huber, J.F. (2002): UMTS and mobile computing. Artech House.

Satyanarayanan, M. (2001). Pervasive computing: Vision and challenges. IEEE Personal Communications (pp. 10-17). IEEE Press.

addItIonal readIng Aarts, E., & Encarnaco J. L. (Eds.). (2006). True visions. The emergence of ambient intelligenc. Berlin, Germany: Springer. Adelstein, F., Gupta, S. K. S. et al. (2004). Fundamentals of mobile and pervasive computing. New York: McGraw-Hill Professional Publishing. Antoniou, G., & van Harmelen, F. (2004). A semantic web primer. Massachusetts: MIT Press. Hansmann, U., Merk, L. et al. (2003). Pervasive computing handbook. The mobile world. Berlin, Germany: Springer. Hedgepeth, W.O. (2006): RFID metrics: Decision making tools for today’s supply chains. University of Alaska, Anchorage, USA

Jurafsky, D., & Martin, J. H. (2000). Speech und language processing. Upper Saddle River, NJ: Prentice Hall. Lu, Y., Staff, L.Y., Zhang, Y., Yang, L.T., & Ning, H. (2008). The Internet of things. Taylor & Francis Group McTear, M. F. (2004). Spoken dialogue technolog. London: Springer. Moreville, P. (2005): Ambient findability. O’Reilly. Riva, G., Vatalaro, F., Davide, F., & Alcañiz, M. (2005): Ambient intelligence. Amsterdam: IOS Press. Sharp, H., Rogers, Y., Preece, J. (2002). Interaction design: Beyond human-computer interaction. J. Wiley & Sons. Stajano, F. (2002). Security for ubiquitous computing. Cambridge: John Wiley & Sons, Ltd.

Weber, W., Rabaey, J. M., & Aarts, E. (Eds.). (2005). Ambient intelligence, Berlin, Germany: Springer.

This work was previously published in Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises, edited by M. Mühlhäuser and I. Gurevych, pp. 1-20, copyright 2008 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.2

Ubiquitous Computing History, Development, and Scenarios Jimmy Chong Nanyang Technological University, Singapore Stanley See Nanyang Technological University, Singapore Lily Leng-Hiang Seah Nanyang Technological University, Singapore Sze Ling Koh Nanyang Technological University, Singapore Yin-Leng Theng Nanyang Technological University, Singapore Henry B. L. Duh National University of Singapore, Singapore

abstract

IntroductIon

This chapter gives a brief history of ubiquitous computing, highlights key issues, and assesses ubiquitous computing research and development under the broad categories of design architecture and systems, implementation challenges, and user issues. Using Singapore as a case example, the chapter then concludes with selected scenarios, presenting exciting possibilities in the future ubiquitous landscape.

history and vision of ubiquitous Computing Technology in computing has undergone extensive changes over the years. In the early 1970s, mainframe computers dominated the computing scene based on the principle of one computer serving many people. In the 1980s, mainframe computers gave way to personal computers and notebooks,

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Ubiquitous Computing History, Development, and Scenarios

and, in contrast, the emphasis was one computer to one person. In the 1990s, with increased computing powers available at affordable prices, we are witnessing a new era of personal computing, that is, a phenomenon in which multiple computers are serving one person. Through the ages, technology has dramatically transformed our lives, changing the way we learn, live, work, and play. Technology shrank transistors to such microscopic sizes that they enable computer chips to be found in the things we use daily, even down to a pair of shoes made by Adidas (McCarthy, 2005). Technology also connects computers around the world breaking down geographical boundaries as people are able to “travel” virtually everywhere, collaborate with others online, and be connected with loved ones virtually even though they may be miles away physically. Mark Weiser (1991; 1993a; 1993b), father of “ubiquitous computing” (or “ubicomp” in short), coined the term “ubiquitous” to refer to the trend that humans interact no longer with one computer at a time, but rather with a dynamic set of small networked computers, often invisible and embodied in everyday objects in the environment. Keefe and Zucker (2003) see ubicomp as a technology that enables information to be accessible any time and anywhere and uses sensors to interact with and control the environment without users’ intervention. An example often cited is that of a domestic ubicomp environment in which interconnected lighting and environmental controls incorporate personal biometric monitors interwoven into clothing so that illumination and heating conditions in a room might be modulated according to “needs” of the wearer of such clothing. Other examples of ubiquitous environment include applications in homes, shopping centres, offices, schools, sports hall, vehicles, bikes, and so forth. The principle guiding ubicomp is the creation of technology that brings computing to the background and not the foreground, making technology invisible. Philosophers like Heidegger

(1955) called it “ready-to-hand” while Gadamer (1982) coined it “horizon.” This means that people do not need to continually rationalize one’s use of an ubicomp system, because once having learned about its use sufficiently, one ceases to be aware of it. It is literally visible, effectively invisible in the same way, for example, a skilled carpenter engaged in his work might use a hammer without consciously planning each swing. Hence, ubicomp defines a paradigm shift in which technology becomes invisible, embedded and integrated into our everyday lives, allowing people to interact with devices in the environment more naturally.

current research challenges Research challenges in ubicomp remain interdisciplinary, and this is evident as we trace the development of the Ubicomp Conference Series into its ninth year in 2007. The conference series began as Handheld and Ubiquitous Computing in 1999, focusing on areas relating to the design, implementation, application, and evaluation of ubicomp technologies, a cross-fertilization of a variety of disciplines exploring the frontiers of computing as it moves beyond the desktop and becomes increasingly interwoven into the fabrics of our lives. Over the years, the Ubicomp Conference Series from 1999 – 2006 has grown in participation by region, with papers addressing more diverse application areas, as well as innovative supporting technologies/media (see Table 1). In the following sections, we highlight key issues and assess the current situation of ubicomp research and development under the broad categories of design architecture and systems and implementation issues.

design architecture and systems For the ubicomp vision to work, we need an infrastructure supporting small, inexpensive,

21

Ubiquitous Computing History, Development, and Scenarios

Table 1. Breakdown of participation by region, application areas and technologies ubicomp conference series from 1999-2006 Ubicomp Conf

Region Asia Pacific

Europe

Application Areas U.S. & Canada

Education

Health Care

Tourism

Technologies Gen

Others

Mobile Devices

Internet

Wireless

Several Devices

Others

1999 (53 paper)

4

39

10

1

1

3

40

8

13

3

-

5

32

2000 (18 paper)

2

8

8

-

1

-

14

3

3

1

-

2

12

2001 (30 paper)

1

12

17

2

-

3

23

2

2

3

2

10

13

2002 (29 paper)

2

14*

15*

1

2

2

19

5

1

1

-

5

22

2003 (26 paper)

-

8

18

1

2

1

19

3

2

-

1

7

16

2004 (26 paper)

3

7*

17*

2*

2*

1

17

5

-

-

1

4

21

2005 (22 paper)

2

10*

12*

-

3

-

16

3

4

-

1

1

16

2006 (30 paper)

4*

7

20*

-

4

2

19

5

7

1

1

2

19

Note:* in 2002: 2 papers written by Europe-U.S. authors * in 2004: 1 paper written by Europe-U.S. authors; 1 paper can be applied both in education and health care * in 2005: 2 papers written by Europe-U.S. authors * in 2006: 1 paper written by Asia-U.S. authors

robust networked processing devices. Current contemporary devices giving some support to this vision include mobile phones, digital audio players, radio-frequency identification tags and interactive whiteboards. For a fully robust ubicomp implementation, we also require a better understanding of the yet-to-emerge “natural” or intuitive interaction paradigms. Challenges facing design architecture and systems also include issues relating to the wireless network, power component, and standards for service discovery. In the ubicomp world, anyone can interact with thousands of wirelessly connected devices, implying implicit mobility. Hence, mobility and density of data transferred require a robust network infrastructure in place. Such networks should have the capacity to transmit and receive wireless data at ultrahigh speed virtually anywhere and everywhere.

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Different standards are currently adopted by different countries, for example, the U.S. standards include analog and digital services; GSM, the European standard, is meant for wide area cellular service; Japan uses CDMA, and so forth, and hence pose problems in interoperability. In order to build a better wireless network environment, some countries are working towards adopting the WiMAX wireless broadband technology in cooperation with telecommunications operators to create wireless broadband cities. Examples include Mobile-Taiwan project (Mobile Taiwan Initiative, 2004) and Singapore SG@Wireless project (Wireless@SG, 2006). Increased use of wireless networks and mobile devices has also resulted in increasing need to manage and administer the interconnection of networked devices with less complexity. Wireless and mobile infrastructure will play a major role in achieving the ubicomp vision; hence, more research should be done to resolve current issues.

Ubiquitous Computing History, Development, and Scenarios

Design and Implementation challenges The ubicomp paradigm presents a novel interpretation of the post-desktop era, and these interfaces thoroughly integrated into everyday objects and activities have to take on different forms. This means that users “using” ubicomp devices engage themselves in many computational devices and systems simultaneously, and may not necessarily even be aware that they are doing so when performing these ordinary activities. Hence, models of contemporary human-computer interaction describing command-line, menu-driven, or GUI-based interfaces may seem inadequate. The challenges facing designers are in making access easy for users to retrieve information on the Internet through either desktops or handheld devices. We discuss some of these challenges in design and implementation: •

•

•

Smaller screen display. Designers need to work within constraints of smaller screen sizes when displaying information (Want & Pering, 2005). Scalable interfaces are also explored as applications extend to desktops, PDAs, and even phone interfaces (Abowd, 1999). Location-based and context-sensitive data. Many ubicomp applications “push” information based on the location of users and display information implicitly to users on a mobile device (Rogers, Price, Randell, Fraser, & Weal, 2005). Hence, designing ubicomp systems also requires designers to consider context awareness. Information needs to be personalized according to user’s location, time, mood, and history (Abowd, 1999). Cultural differences. Users are diverse and they can come from all over the world. We need to have in place some degree of

standardization to prevent diverse cultural conflicts (Rosson & Carroll, 2002). • Privacy. With widespread use of wireless broadband, we have to be vigilant in protecting our personal information and our personal network access. Users should be educated that tapping into other people’s wireless network is unethical and that detailed tracking of individuals accessing illegally is possible (IDA, 2005b). Yamada (2003) highlighted privacy management considerations asking three fundamental questions: (i) “where” to store personal data (network centric or end-user centric); (ii) “who” to manage the privacy (user, network operator or service provider); and (iii) “how” to protect privacy (principle of minimum asymmetry, pawS system or P3P). Designers of ubicomp applications need to address carefully these important privacy questions. • Security. Bardram (2005) discussed tradeoffs between usability and security. In the ubicomp environment, we have many public computers serving individual computers. For context awareness systems, users’ details and profiles need to be captured. New design challenges involve understanding security tradeoffs of having users logging into the public computers as opposed to not having authentication where users enjoy access into these various systems. To address these design and implementation challenges, Jones and Marsden (2006) see designers/developers as playwrights developing “scripts” with scenarios and use cases on how technologies are used. Carroll (2000) stresses the importance of maintaining a continuous focus on situations of and consequences for human work and activity to promote learning about the structure and dynamics of problem domains, thus seeing usage situations from different perspectives, and managing tradeoffs to reach usable and effective design outcomes.

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Ubiquitous Computing History, Development, and Scenarios

Design is difficult and is never completely “done,” resulting in the task-artifact cycle dilemma (Carroll, 2000). This is so because at the start of any software development, tasks help articulate requirements to build artifacts, but designed artifacts create possibilities (and limitations) that redefine tasks. Hence, managing the task-artifact cycle is not a linear endeavour with different starting and ending points. There will always be a further development, a subsequent version, a redesign, a new technology development context. That is, the design scenarios at one point in time are the requirements scenarios at the next point in time. Claims analysis was later developed by Carroll (2000) to enlarge the scope and ambition of scenario-based design approach to provide for more detailed and focused reasoning. Norman’s influential Model of Interaction (Norman, 1988) is used as a framework in claims analysis for questioning the user’s stages of action when interacting with a system in terms of goals, planning, execution, interpretation, and evaluation.

sIngapore as a case exaMple: dIscussIon of scenarIos In educatIon In Singapore, the IT initiatives underwent three phases of implementation in the early 1990s through the Civil Service Computerization Plan, the National IT Plan, and IT 2000. The IT2000 Master Plan was launched in 1992 (National Computer Board, 1992), just 6 months after Weiser’s seminal article on ubicomp in Scientific America (Weiser, 1991). The IT2000 vision in Singapore aimed to provide a nationwide information infrastructure to link every home, school, and workplace in Singapore, creating an intelligent island (Choo, 1997). In 2005, the IT 2000 vision was revised and the Singapore Government Intelligent Nation 2015(iN2015) is a 10-year blueprint to enable every individual to have seamless access to intelligent technology (IDA, 2005). The goal

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was to create smart, sentient entertainment spaces with networked, embedded spaces padded with sensory and distributed intelligence characterized by human-friendly computing as well as businessefficient automation. In the 2006 survey (IDA Survey, 2006), the high mobile penetration rate of 104.6% of infocomm usage in households and individuals showed that the Internet and mobile phones had perpetually been weaved into the daily activities of Singaporeans. Some examples of innovation usage included (IDA Infocomm Survey, 2006): (i) distributing critical information to the entire population during crisis situations; (ii) voting in contests, donating money during charity events; (iii) booking services for movie or taxi; and (iv) electronic road pricing system utilizing unique vehicle identification units, smart cards, distributed data collection points, and a centralized data centre to provide variable pricing information to drivers going into the central district areas and highways. In education, we are witnessing applications/ services being implemented in some schools in Singapore towards the 2015 vision of making learning truly global and out of the classroom, to align with Singapore’s 10-year Infocomm Plan iN2015 (IDA, 2005a, 2005b, 2006). We illustrate in the following scenarios how different personas could interact with the ubiquitous ecosystem and the contactless smart badge/card as a wearable computer concept that communicates with sensors in the surrounding.

scenario 1 Presently, all school-going students in Singapore possess built-in smart cards, serving as identity cards as well as as cash cards. Kiosks are set up in some schools where students could pay for food using their student cards. These kiosks also record purchasing habits of students. Parents could go online to check expenditures incurred and eating

Ubiquitous Computing History, Development, and Scenarios

habits of their children, attendance records, and homework details.

scenario 2 In the near future, perhaps smart badges with built-in Radio Frequency Identification (RFID) could replace the traditional student card. The smart card storing students’ personal particulars with RFID could automatically send out a unique identifier to sensors located on walls and ceilings in schools. Hundreds of interconnected closed circuit televisions (CCTVs) and images could be installed at every corner in the school to survey and record daily activities, and keep track of incoming or out-going students.

scenario 3 Classrooms could be fully equipped with computing resources involving multimedia features such as screen, pen stylus, and table could be neatly arranged like a Swiss army knife at the side of a chair, and students could scan their smart badges to activate resources. Upon activation, the online learning portal could be launched. The computer could be interconnected to all fellow students and the teacher-in-charge during the class. The portal could allow students to learn, work on their assignments, and take tests or exams in an interactive way. Instead of carrying bags containing books, students could carry tablet PCs, capable of communicating wirelessly with other devices.

scenario 4 Perhaps teachers could also have smart badges with built-in RFIDs and tablet PCs. At the start of school each morning, the tablet PC could automatically take attendance of students in class, and start “tracking down” the absentees. After 15 minutes, it could send a text message via mobile phones to parents concerned. Similarly, parents could remotely find out about their children’s well-

being and whereabout. For example, if a parent wishes to check whether her youngest child at school is having a fever, she could do so by logging onto a portal to activate the smart card for the body temperature to be taken.

conclusIon With all the hype about ubicomp, one could easily get carried away with the lure of benefits it promises to bring. With these challenges, ubicomp also brings along many unknowns and changes radically the way people interact with one another and with the environment. As technology becomes embedded in everyday artefacts, the modes of interaction change constantly. Although Weiser (1991) started the ubicomp vision more than a decade ago, current ubicomp literature keeps revolving around Weiser’s vision for the future of implementing ubicomp applications or services that could provide a seamless interconnected environment. In fact, Bell and Dourish (2006) suggest that we stop talking about the “ubicomp of tomorrow” but rather at the “ubicomp of the present.” Doing so, they advocate getting out of the lab and looking at ubicomp as it is being developed rather than what it might be like in the future. Hence, this chapter highlighted key issues and assessed the current situation of ubicomp development in design architecture and systems, implementation issues, and challenges. Selected scenarios in the Singapore’s education landscape were also described, presenting possibilities and challenges in the future ubiquitous landscape. To conclude, there is perhaps no need for heroic engineering; the heterogeneous technologies could be utilised as well. We are already living in the world of ubicomp. Gibson (1999), father of cyberpunk fiction, rightfully pointed out “the future is here, it is just not evenly distributed” (retrieved on March 11, 2007 from http://en.wikiquote.org/ wiki/William_Gibson).

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Ubiquitous Computing History, Development, and Scenarios

acknoWledgMent The authors would like to thank the 2006-2007 Usability Engineering Class in the M.Sc. (Information Systems) programme at the Division of Information Studies (Nanyang Technological University) for their discussion on ubiquitous computing.

references Abowd, G. (1999). Software engineering issues for ubiquitous computing. ACM Press. Bardram, J. (2005, July 23). The trouble with login: On usability and computer securityin ubiquitous computing. Springer-Verlag London Limited. Bell, G., & Dourish, P. (2007, January). Yesterday’s tomorrows: Notes on ubiquitous computing’s dominant vision. Personal Ubiquitous Computing, 11(2), 133-143. Carroll, J. (2000). Making use: Scenario-based design of human-computer interactions. The MIT Press. Choo, C.W. (1997). IT2000: Singapore’s vision of an intelligent island. In P. Droege (Ed.), Intelligent environments. North-Holland, Amsterdam. Gadamer, H.G. (1982). Reason in the age of science (Trans.). Cambridge: MIT Press. Heidegger, M. (1955, 1977). The question concerning technology (Trans.). In The question concerning technology and other essays (pp. 3-35). New York: Harper & Row Publishers. IDA. (2005a). Enhancing service, enriching experience, differentiating Singapore. iN2015 (p. 14). Retrieved January 16, 2008, from http://www. in2015.sg/download_file.jsp?file=pdf/11_Tourism_Hospitality_and_Retail.pdf

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IDA. (2005b). Innovation. Integration. Internationalisation. iN2015 (p. 92). Retrieved January 16, 2008, from http://www.in2015.sg/download_file. jsp?file=pdf/01_iN2015_Main_Report.pdf IDA. (2006). iN2015. Retrieved January 16, 2008, from http://www.in2015.sg/about.html IDA Infocomm Survey. (2006). Annual survey of Infocomm usage in households and individuals 2006. Retrieved January 16, 2008, from http:// www.ida.gov.sg/doc/Publications/Publications_ Level2/2006_hh_exec%20summary.pdf Jones, M., & Marsden, G. (2006). Mobile interaction design. John Wiley & Sons Ltd. Keefe, D., & Zucker, A. (2003). Ubiquitous computing projects: A brief history. In Ubiquitous Computing Evaluation Consortium. Arlington, VA: SRI. McCarthy, M. (2005). Adidas puts computer on new footing. Retrieved January 16, 2008, from http://www.usatoday.com/money/ industries/2005-03-02-smart-usat_x.htm Mobile Taiwan Initative. (2004). Retrieved January 16, 2008, from http://www.roc-taiwan.org/uk/ TaiwanUpdate/nsl022005h.htm National Computer Board. (1992). A vision of an intelligent island: IT2000 report. Singapore: National Computer Board. Norman, D. (1998). The psychology of everyday things. Basic Books. Rogers, Y., Price, S., Randell, C., Fraser, D. S., & Weal, M. (2005). Ubi-learning integrates indoor and outdoor experiences. Communications of the ACM, 48(1), 55-59. Rosson, M. B., & Carroll, J. M. (2002). Usability engineering in practice. In Usability EngineeringScenario-Based Development of Human-Computer Interaction (pp. 349-360). San Francisco: Morgan Kaufmann Publishers.

Ubiquitous Computing History, Development, and Scenarios

Want, R., & Pering, T. (2005). System challenges for ubiquitous & pervasive comput-ing. In ICSE’05, ACM 1-58113-963-2/05/00. Weiser, M. (1991). The computer for the twentyfirst century. Scientific American, 265(3), 94104. Weiser, M. (1993a). Some computer science issues in ubiquitous computing. Communications of the ACM, 36(7), 75-84.

Weiser, M. (1993b). Ubiquitous computing. IEEE Computer, 26(10), 7 l-72. Wireless@SG project. (2006). Retrieved January 16, 2008, from http://www.ida.gov.sg/Infrastructure/20070202144018.aspx Yamada, S. (2003). Overview of privacy management. Ubiquitous Computing Environments, National Institute of Informatic.

This work was previously published in Ubiquitous Computing: Design, Implementation, and Usability, edited by Y. Theng & H. Duh, pp. 1-8, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 1.3

The Ubiquitous Portal Arthur Tatnall Victoria University, Australia

IntroductIon

background

The word portal can be used to represent many different things, ranging from the elaborate entranceway to a medieval cathedral to a gateway to information on the Internet. What all the usages have in common, though, is the idea of facilitating access to some place or some thing. In addition to its use in relation to Web portals, the term can also be used more metaphorically to allude to an entranceway to far away places or new ideas, new knowledge, or new ways of doing things. Some new, or different, ideas, knowledge, or ways of doing things have had a beneficial effect on society, while others have had a detrimental affect. A portal can thus lead to various different places, things, or ideas, both good and bad. Before a portal can be used, however, it must be adopted by the individual or organisation concerned, and adoption of technological innovations such as portals is the subject of this article.

Gateways come in all shapes and sizes, and likewise so do portals. Portals are seen everywhere (Tatnall, 2005a) and it would be difficult to make any use of the Web without encountering one. On the Web there are government portals, science portals, environmental portals, community portals, IT industry portals, professional society portals, education portals, library portals, genealogy portals, horizontal industry portals, vertical industry portals, enterprise information portals, medical and health portals, e-marketplace portals, personal/mobile portals, information portals, niche portals, and many more. Portals have become truly ubiquitous. In literature and film also, many mentions are made of portals, although not all of the Web variety. These range from a description of the sun by William Shakespeare in Richard II (Act 3, Scene 3): “See, see, King Richard doth himself appear, as doth the blushing discontented sun from out

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Ubiquitous Portal

the fiery portal of the east.” (Shakespeare, 1595), to the means of moving around the universe in the TV series Stargate SG-1. The transportation device used by Ford Prefect and Arthur Dent in the Hitch Hiker’s Guide to the Galaxy (Adams, 1979) could also be considered a portal, as could the teleport mechanism employed by the crew leaving or returning to the Enterprise in Star Trek. In much science fiction and fantasy literature, a portal-like device is used to move from one place to another without the need for inconvenient (or perhaps impossible) explanations of the means of doing so. The portal (whether or not it is called this) is thus used as a black box (Latour, 1996) capable of almost magical transformations. In many ways, a Web portal can also be considered as a black box that achieves its purpose of taking a user to some interesting or useful place on the Web without them needing to know how this is done. For most people, other than those involved in their design or construction, the technology of the Web portals is irrelevant. All they want to know is that it provides a convenient means of taking them to some Web location where they want to go. Just because a portal exists, however, there is nothing automatic about organisations or individual people wanting to adopt or use it. A portal will only be adopted if potential users make a decision to do so, and such decisions are not as simple as one might naively think. Adoption of a technological innovation, such as a portal, occurs for a variety of reasons, and this is a significant study in itself. The first step to researching the use of a portal by an organisation (or individual), though, is to investigate why it was adopted. The remainder of this article will consider the portal as a technological innovation and consider portal adoption through the lens of innovation theory.

the portal as a technologIcal InnovatIon Many people use the words invention and innovation almost synonymously, but for any academic discussion of technological innovation an important distinction needs to be made between these terms. Invention refers to the construction of new artefacts or the discovery of new ideas, while innovation involves making use of these artefacts or ideas in commercial or organisational practice (Maguire, Kazlauskas, & Weir, 1994). Invention does not necessarily invoke innovation and it does not follow that invention is necessary and sufficient for innovation to occur (Tatnall, 2005b). Clearly the portal can be seen as an invention, but the point here is that it will not be used unless it is adopted, and that means looking at it also as a technological innovation. Of course, the application of innovation theory to the adoption of a technological innovation assumes that the potential adopter has some choice in deciding whether or not to make the adoption. In the case of an organisation or individual considering the adoption and use of a portal, however, it is difficult to see any reason why they would not have a large measure of choice in this adoption decision. This makes the application of adoption theory quite appropriate when considering the use of Web portals.

adoptIon of technologIcal InnovatIons There are a number of theories of technological innovation, diffusion of innovations (Rogers, 1995) probably being the best known. Other innovation theories include the technology acceptance model (Davis, 1989; Davis, Bagozzi & Warshaw, 1989) and innovation translation (Callon, 1986b; Latour, 1996; Law, 1991), informed by actor-network theory (ANT).

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Innovation diffusion Innovation diffusion is based on the notion that adoption of an innovation involves the spontaneous or planned spread of new ideas, and Rogers defines an innovation as: “... an idea, practice, or object that is perceived as new” (Rogers, 1995, p. 11). In diffusion theory the existence of an innovation is seen to cause uncertainty in the minds of potential adopters (Berlyne, 1962), and uncertainty implies a lack of predictability and of information. Diffusion is considered to be an information exchange process among members of a communicating social network driven by the need to reduce uncertainty (Rogers, 1995). Rogers elaborates four main elements in innovation diffusion: characteristic of the innovation itself, the nature of the communication channels, the passage of time, and the social system through which the innovation diffuses (Rogers, 1995). Innovation diffusion has had considerable success in explaining large scale movements and adoptions, but has been found less successful when considering adoption by individual organisations and people.

Technology Acceptance Model The technology acceptance model (TAM) is a theoretical model that evaluates “… the effect of system characteristics on user acceptance of computer-based information systems” (Davis, 1986, p. 7). It was developed from the theory of reasoned action (Fishbein & Ajzen, 1975). TAM assumes that a technology user is generally quite rational and uses information in a systematic manner to decide whether to adopt a given technology. Davis’s (1986) conceptual framework proposed that a user’s motivational factors are related to actual technology usage, and hence act as a bridge between technology design (including system features and capabilities) and actual technology usage. Davis (1986) posits that perceived useful-

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ness and perceived ease of use are major determinants of technology acceptance. Like innovation diffusion, TAM places considerable importance on the “innate” characteristics of the technology and so is based on an essentialist position (Grint & Woolgar, 1997).

Innovation translation An alternative view of innovation is that of innovation translation proposed in actor-network theory (ANT), that considers that the world is full of hybrid entities (Latour, 1993) containing both human and nonhuman elements. ANT developed around problems associated with attempts to handle socio-technical “imbroglios” (Latour, 1993) like electric cars (Callon, 1986a), scallop fishing (Callon, 1986b), Portuguese navigation (Law, 1987), and supersonic aircraft (Law & Callon, 1988) by regarding the world as heterogeneous (Chagani, 1998). ANT offers the notion of heterogeneity to describe projects such as the adoption of portal technology, which involves computer technology, the Internet, the Web portal, broadband connections, Internet service providers (ISP), and the individual or organisation considering the adoption. More specifically though, ANT makes use of a model of technological innovation which considers these ideas along with the concept that innovations are often not adopted in their entirety but only after “translation” into a form that is more appropriate for the potential adopter. The core of the actor-network approach is translation (Law, 1992), which can be defined as: “... the means by which one entity gives a role to others” (Singleton & Michael, 1993, p. 229). Rather than recognising in advance supposed essential characteristics of humans and of social organisations and distinguishing their actions from the inanimate behaviour of technological and natural objects (Latour, Mauguin, & Teil, 1992, p. 56), ANT adopts an antiessentialist position

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in which it rejects there being some difference in essence between humans and nonhumans. ANT makes use of the concept of an actor (or actant) that can be either human or nonhuman, and can make its presence individually felt by other actors (Law, 1987). It is often the case that when an organisation (or individual) is considering a technological innovation they are interested in only some aspects of this innovation and not others (Tatnall, 2002; Tatnall & Burgess, 2002). In actor-network terms it needs to translate (Callon, 1986b) this piece of technology into a form where it can be adopted, which may mean choosing some elements of the technology and leaving out others. What results is that the innovation finally adopted is not the innovation in its original form, but a translation of it into a form that is suitable for use by the recipient (Tatnall, 2002). Innovation Translation can be considered to proceed through several stages. In the first stage, the problem is redefined, or translated, in terms of solutions offered by these actors (Bloomfield & Best, 1992) who then attempt to establish themselves as an “obligatory passage point” (Callon, 1986b) which must be negotiated as part of its solution. The second stage is a series of processes which attempt to impose the identities and roles defined in the first stage on the other actors. It means interesting and attracting an entity by coming between it and some other entity (Law, 1986). If this is successful, the third stage follows through a process of coercion, seduction, or consent (Grint & Woolgar, 1997) leading to the establishment of a solid, stable network of alliances in favour of the innovation. Finally, the proposed solution gains wider acceptance (McMaster, Vidgen, & Wastell, 1997) and an even larger network of absent entities is created (Grint & Woolgar, 1997) through some actors acting as spokespersons for others.

researchIng the adoptIon of Web portals Both innovation diffusion and the technology acceptance model suggest that adoption decisions are made primarily on the basis of perceptions of the characteristics of the technology concerned (Davis 1989; Rogers 1995). Using an innovation diffusion approach, a researcher would probably begin by looking for characteristics of the specific portal technology to be adopted, and the advantages and problems associated with its use. They would think in terms of the advantages offered by portals in offering a user the possibility of finding information, but would do so in a fairly mechanistic way that does not allow for an individual to adopt the portal in a way other than that intended by its proponent; it does not really allow for any form of translation. If using TAM, this researcher would similarly have looked at characteristics of the technology to see whether the potential user might perceive it to be useful and easy to use. A researcher using an innovation translation approach to studying innovation, on the other hand, would concentrate on issues of network formation, investigating the human and nonhuman actors and the alliances and networks they build up. They would attempt to identify the actors and then to follow them (Latour, 1996) in identifying their involvement with the innovation and how they affect the involvement of others. The researcher would then investigate how the strength of these alliances may have enticed the individual or organisation to adopt the portal or, on the other hand, to have deterred them from doing so (Tatnall, 2002; Tatnall & Burgess, 2006; Tatnall & Gilding, 1999).

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conclusIon Web portals are now quite ubiquitous, and researching their use in organisations and by individuals is an important aspect of information systems research. It is useful to consider the portal as a technological innovation and to research it using an approach based on innovation theory. The question is, which innovation theory is most appropriate? Both innovation diffusion and the technology acceptance model rely on the idea that the technology involved, in this case the Web portal, has some underlying immutable characteristics or essences that a potential user takes into consideration when making adoptions decisions. Innovation Translation, informed by actor-network theory, offers instead an antiessentialist socio-technical approach. In this article, I have put the view that it is this approach that is most useful when researching the adoption and use of portals. The innovation translation approach is particularly useful in considering that topic, people, and technology are intimately involved with each other and their individual contributions to the innovation decision are difficult to differentiate . The question of whether “ideas portals,” or the metaphorical entrance ways to new ideas, new knowledge or new ways of doing things, could usefully be researched using actor-network theory is unanswered. ANT could perhaps investigate which of these have had a beneficial affect on society and which have had a detrimental affect. This could involve an interesting topic for another research paper.

references Adams, D. (1979). The hitch-hikers guide to the galaxy. London: Pan Books.

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Berlyne, D. E. (1962). Uncertainty and epistemic curiosity. British Journal of Psychology, 53, 2734. Bloomfield, B. P., & Best, A. (1992). Management consultants: Systems development, power and the translation of problems. The Sociological Review, 40(3), 533-560. Callon, M. (1986a). The sociology of an actornetwork: The case of the electric vehicle. In M. Callon, J. Law, & A. Rip (Eds.), Mapping the dynamics of science and technology (pp. 19-34). London: Macmillan Press. Callon, M. (1986b). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. In J. Law (Ed.), Power, action & belief: A new sociology of knowledge? (pp. 196-229). London: Routledge & Kegan Paul. Chagani, F. (1998). Postmodernism: Rearranging the furniture of the universe. Irreverence, 1(3), 1-3. Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral thesis, MIT, Boston. Davis, F. D. (1989, September). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 318340. Davis, F. D., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading: Addison-Wesley. Grint, K., & Woolgar, S. (1997). The machine at work—Technology, work and organisation. Cambridge: Polity Press.

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Latour, B. (1993). We have never been modern. Hemel Hempstead: Harvester Wheatsheaf. Latour, B. (1996). Aramis or the love of technology. Cambridge, MA: Harvard University Press. Latour, B., Mauguin, P., & Teil, G. (1992). A note on socio-technical graphs. Social Studies of Science, 22(1), 33-57. Law, J. (1986). The heterogeneity of texts. In M. Callon, J. Law, & A. Rip (Eds.), Mapping the dynamics of science and technology (pp. 67-83). London: Macmillan Press. Law, J. (1987). Technology and heterogeneous engineering: The case of Portuguese expansion. In W. E. Bijker, T. P. Hughes, & T. J. Pinch (Eds.), The social construction of technological systems: New directions in the sociology and history of technology (pp. 111-134). Cambridge, MA: MIT Press. Law, J. (Ed.) (1991). A sociology of monsters. Essays on power, technology and domination. London: Routledge. Law, J. (1992). Notes on the theory of the actornetwork: Ordering, strategy and heterogeneity. Systems practice, 5(4), 379-393. Law, J., & Callon, M. (1988). Engineering and sociology in a military aircraft project: A network analysis of technological change. Social Problems, 35(3), 284-297. Maguire, C., Kazlauskas, E. J., & Weir, A. D. (1994). Information services for innovative organizations. San Diego, CA: Academic Press. McMaster, T., Vidgen, R. T., & Wastell, D. G. (1997). Towards an understanding of technology in transition: Two conflicting theories. In Proceedings of the Information Systems Research in Scandinavia, IRIS20 Conference, Hanko, Norway, University of Oslo.

Shakespeare, W. (1595). Richard II. The complete works of Shakespeare (pp. 358-384). London: Spring Books. Singleton, V., & Michael, M. (1993). Actornetworks and ambivalence: General practitioners in the UK cervical screening programme. Social Studies of Science, 23, 227-264. Tatnall, A. (2002). Modelling technological change in small business: Two approaches to theorising innovation. In S. Burgess (Ed.), Managing information technology in small business: Challenges and solutions (pp. 83-97). Hershey, PA: Idea Group Publishing. Tatnall, A. (2005a). Portals, portals everywhere…. In A. Tatnall (Ed.), Web portals: The new gateways to Internet information and services (pp. 1-14). Hershey, PA: Idea Group Publishing. Tatnall, A. (2005b). To adopt or not to adopt computer-based school management systems? An ITEM research agenda. In A. Tatnall, A. J. Visscher, & J. Osorio (Eds.), Information technology and educational management in the knowledge society (pp. 199-207). New York: Springer-Verlag. Tatnall, A., & Burgess, S. (2002). Using actornetwork theory to research the implementation of a B-B Portal for regional SMEs in Melbourne, Australia. In Proceedings of the 15th Bled Electronic Commerce Conference—‘eReality: Constructing the eEconomy’. Bled, Slovenia: University of Maribor. Tatnall, A., & Burgess, S. (2006). Innovation translation and e-commerce in SMEs. In M. Khosrow-Pour (Ed.), Encyclopedia of e-commerce, egovernment and mobile commerce (pp. 631-635). Hershey, PA: Idea Group Reference.

Rogers, E. M. (1995). Diffusion of innovations. New York: The Free Press.

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Tatnall, A., & Gilding, A. (1999). Actor-network theory and information systems research. In Proceedings of the10th Australasian Conference on Information Systems (ACIS). Victoria University of Wellington.

key terMs Actor (Actant): An entity that can make its presence individually felt by other actors. Actors can be human or nonhuman. Nonhuman actors include such things as computer programs, portals, organisations, and other such entities. An actor can be seen as an association of heterogeneous elements that constitute a network. This is especially important with nonhuman actors, as there are always some human aspects within the network. Actor-Network Theory (ANT): An approach to socio-technical research in which networks, associations, and interactions between actors (both human and nonhuman) and are the basis for investigation.

Black Box: A concept whereby some object or idea is considered only in an external manner in relation to the affect it produces, without reference to what goes on inside it. This simplification enables the study of complex entities without worrying too much about their internal working details when this is not entirely necessary. Innovation Diffusion: Is considered to be an information exchange process among members of a communicating social network driven by the need to reduce uncertainty. Innovation Translation: An innovation is often not adopted in its original form, but as a “translation” of this original into a form that is found to be suitable for use by the recipient. Invention: Refers to the construction of new artefacts or the discovery of new ideas. Technological Innovation: Involves making use of these artefacts or ideas in commercial or organisational practice. Technology Acceptance Model (TAM): Considers that adoption decisions are determined primarily by a consideration of perceived usefulness and perceived ease of use.

This work was previously published in Encyclopedia of Portal Technologies and Applications, edited by A. Tatnall, pp. 10401044, copyright 2007 by Information Science Reference (an imprint of IGI Global).

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Chapter 1.4

The Ubiquitous Grid Patricia Sedlar Johannes Kepler University, Austria

vIsIon Grid computing is an emerging technology providing the possibility to aggregate resources for the solution of computation- or data-intensive scientific tasks. Taking the evolution of mobile computing into consideration, new Grid concepts are conceivable, fully exploiting the advantage of mobile devices and ubiquitous access. By decoupling resource availability from the core grid infrastructure and hardware, the user has always the same computational power, data or storage available, regardless of a device or location. Thus restricted capabilities of thin clients can be extended and new fields of application can be made accessible. The key concept is “The invisible grid” – the grid environment should just be there for the use of applications in science, business, health care, environment, or culture domains. Having this concept in mind, the following scenario is conceivable: Equipped with your mobile phone,

which you always have with you, you are walking around and are taking a picture of an object you are interested in. You are sending the picture to the grid, where the visual information is extracted. After the analysis, information about the captured object is sent to you. Thus you have a search engine on a visual base at your permanent disposal, information captured as seen by your eyes – without the need of textural translations or the need to know the object’s name or ID in order to retrieve information about it. Realizing the scenario above, the user obtains a smart tool, easing information retrieval considerably by making use of ubiquity in combination with grid computing. But the scenario has even more potential in terms of pervasiveness. The use of mobile devices can provide a user with additional location bound information. With a portable device the user is able to access location-based services or to collect environmental information to be processed within a grid. At this stage research activities in the field of pervasive computing come

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The Ubiquitous Grid

into play. Pervasive computing pursues the goal to enhance the environment with sensors and smart objects in order to provide the user with suitable context-based and/or location-based services. Expanding the introduced setting with the capabilities from pervasive computing, the following scenario is conceivable: You are an invited speaker on a conference and you are moving through the rooms of the venue. All rooms are equipped with cameras covering all perspectives of view. You are looking at a person from whom you want to know the research interests. You flick with your finger, to capture the camera picture from your perspective. The picture is processed within the grid and the ambient display next to you shows the requested information.

net. By using a grid of computers, it is possible to aggregate computational power to generate a huge virtual multi-computer ready for processing, storage, and communication. Since a grid can be made up of a set of geographically separate networks, enormous computational power can be made available for solving complex or data intensive problems. Grid computing is still at its early stages of evolution. Anyhow it is no longer the exclusive realm of researchers aiming to solve sophisticated scientific tasks (Gentsch, 2004). Alike the evolution of the Internet, main grid initiatives aim to successively establish a global grid, providing users with infinite resources, just by plugging the computer.

Pervasive Computing IntroductIon The scenarios described in the foregoing section aim to combine strengths of three main disciplines: grid computing, pervasive computing, and mobile computing.

Grid Computing The term “grid” was coined in the mid-1990s to refer to a proposed distributed computing infrastructure for advanced science and engineering (Foster & Kesselman, 2004). A grid is an infrastructure of geographically distributed resources, comprising hardware components such as processors, memory media, or scientific instrumentation and software components such as services, applications, licenses, and so forth. Its infrastructure consists of hard- and software elements to aggregate and to coordinate resources. The first grid that has been developed, for the European Organization for Nuclear Research (CERN) to support the research of the particle physics laboratory (Colasanti, 2004), uses a large scale distributed system by taking the advantage of the rich infrastructure provided by the Inter-

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Pervasive computing was inspired by Mark Weiser in 1991, when he introduced his vision of the computer of the 21st century with the central statement: “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it” (Weiser, 1991). His assumption that, “we are trying to conceive a new way of thinking about computers in the world, one that takes into account the natural human environment and allows the computers themselves to vanish into the background” has fertilized the embedding of ubiquitous computing technology into a physical environment which responds to people’s needs and actions (Ferscha, 2003). To bring interaction “back to the real world” (Wellner et al., 1993) was the second historical vision impacting the evolution of pervasive computing. Instead of interacting with digital data via keyboard and screen, physical interaction with digital data, for example, via “graspable” or “tangible” interfaces, was proposed (Ferscha, 2003). Present research activities in the field of pervasive computing aim to enhance the human

The Ubiquitous Grid

with embedded, intelligent “smart objects,” detecting user needs and initializing all necessary processes. The “home of the future” is a popular field of application. You wake up in the morning, the sensors of the coffee machine detect that you urgently need a coffee and starts brewing. The refrigerator prints a shopping list of items missing; the sensors of your pajama send an e-mail to your wardrobe, recommending the colour best complementing your mood in order to select the most suitable clothing for today, and so forth.

Mobile Computing The foregoing section introduced the objectives of pervasive computing. Its aim is to establish an enhanced environment, autonomously detecting user needs and reacting on them. In order to fulfil this goal, pervasive computing applications have to provide ubiquitous access, context awareness, intelligence, and natural interaction (Ferscha, 2003). At this early stage of research, we are far from global concepts allowing users to interact with arbitrary pervasive computing environments. In order to interact with smart objects, personalization is frequently done at the base of profiles saved on mobile devices. Due to this fact, and our aim to build the ubiquitous grid on the strengths of existing technologies, mobile computing will be part of the initial concept.

Interaction Model The proposed ubiquitous grid is an intersection of grid computing and pervasive computing. With the mobile device, the user may initialize interaction between these domains (see Figure 1). Referring to the scenarios introduced at the beginning of the chapter, the user is taking a picture, and sends it to a grid application using his mobile device. The information is processed within the grid, and the result is then returned to the user directly or handled to the pervasive computing environment.

Figure 1. Interaction model

grId coMputIng Grid Applications In the following, we are going to give a brief survey of traditional fields of grid computing, which require extensive computational power and/or storage, and show some examples of high performance applications that can be provided by PC clusters: Medicine: Medical simulations and visualizations are a large field of grid applications. They typically require computational power usually not available in a hospital. An example of such application is a virtual vascular surgery on the grid, allowing pre-treatment planning through real-time interactive simulation of vascular structure and flow. The system – set up on a computational grid – consists of a distributed real-time simulation environment, in which a user interacts in virtual reality (IFCA, 2004). Physics: An example of an application using a data grid can be found in the field of high energy physics. One of its main challenges is to answer questions about fundamental particles and the forces acting between them. To that purpose a grid can host a powerful particle accelerator, which will provide data related to these interactions at a tremendous output rate and the data grid provides the solution for storing and processing such a huge amount of data (IFCA, 2004). Computer Graphics: The rapid development and low price of personal computers make it an

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interesting choice to convey ideas through visualization. As real-time 3-D graphics are adequate for many applications, a single computer is often insufficient if photo-realistic images are required. With grid-based distributed computing it is possible to produce fast, cheap photo-realistic images, using the processing power of office computers being idle (Pennanen & Ylikerälä, 2004). Education: Another field of interest for grid computing is education. Providing a semantic data grid for human learning, the implementation of future learning scenarios based on ubiquitous, collaborative, experimental-based and contextualized learning can be supported (Ritrovato & Gaeta, 2004).

Mobility and ubiquity in the context of Grid Computing Compared to traditional (wired) grids, mobile grids have the advantage of exploiting the concepts of mobility and ubiquity in terms of being available anytime, anywhere, and by all means. Making use of wireless connection, the mobile grid environment is available anytime and anywhere. Nevertheless there is a lack of applications that can be attributed to the crucial disadvantage that a mobile grid cannot rely on stable basic conditions. It has to cope with variable resource availability and different types of user terminals. As each combination of resources and user devices brings its specific restrictions and challenges, the selection of an adequate grid integration and an appropriate application will be dependant on the following criteria: Type of job processing: Job processing of a computational grid can be done in two different ways, either sequential, by a narrow set of repetitive tasks or in a true parallel, distributed fashion in order to execute a complex job (Ault & Tumma, 2004). In the first case, jobs can be processed by a single terminal; in the second case jobs may be processed faster, but implies

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coordination between cooperating terminals. The usage of parallel computing for mobile grids poses a big challenge, because it requires a very robust, possibly performance consuming resource management taking into account log-offs and location changes of mobile nodes. Type of network: Another requirement that need to be taken care of while designing a mobile grid is the type of network. A fix, wired network allows a prediction of computational power and storage space. Resources of an ad-hoc grid can vary strongly according to the current network topology and cannot offer QoS. Type of connection: As for the type of network, physically distributed terminals offer the advantage of predictable and mostly reliable connections. However wireless connections allow ubiquitous access to services. Application requirements: Central aspects to choose an adequate grid integration are the applications requirements. Parameters to be considered are: •

•

•

Data size: This refers to the amount of data to be computed, transmitted, stored and/or visualized. QoS: This parameter includes all requirements concerning quality of service and refers to performance and/or reliability of services. Ubiquity: The last parameter identifies the accessibility of services (catch phrase: anytime, anywhere).

Type of user device: As mobile devices often have restrictions in regard to their capacities, it might be necessary to check, if a portable device fulfils an applications requirements in terms of battery capacity, display size and memory size.

Potential for Mobility Support In the context of the AGrid project1, we conducted

The Ubiquitous Grid

a survey of the potential for mobility support for scientific grid applications. The objective of the AGrid project is to promote and to develop grid technologies within Austria. To meet this objective, technology-oriented work packages act in strong cooperation with application-oriented work packages in order to be able to adequately address the needs of the users. For the elicitation of application of application demands, structured interviews have been done within the mobility work package with all application work packages. The questionnaire used for these interviews addressed the following issues: 1) Description of the application, 2) execution time and real-time requirements, 3) data, 4) availability, 5) security, 6) user interface and data input, 7) software and hardware architecture, 8) network, 9) performance measures and 10) mobility. These interviews covered the following spectrum of applications: • • • • • • • • • • • • •

Distributed Heart Simulation Virtual Lung Biopsy Virtual Eye Surgery Medical Multimedia Data Management and Distribution Virtual Arterial Tree Tomography and Morphometry High-Energy Physics Distributed Scientific Computing: Advanced Computational Methods in Life Science Computational Engineering High Dimensional Improper Integration Procedures Astrophysical Simulations and Hydrodynamic Simulations Federation of Distributed Archives of Solar Observation Meteorological Simulations Environmental Grid Application

Through the interviews with all application groups, we came to the following summary: Most

of the groups have special application needs in terms of data intensity, real-time interaction, and/or visualization. As a consequence, main interaction processes can only be done with the aid of desktop computers. As tasks performed within grids typically take several hours, some groups indicated interest in mobility support for observing and monitoring or for job relaunch upon failure. Additionally, a couple of groups could imagine using lightweight devices for mobile data collection. Analyzing these results, we came to the conclusion that mobility support for typical grid applications is of minor relevance. It can serve to support interaction processes carried out on desktop machines but has little added value for scientific grid applications (Sedlar & Kotsis, 2007).

pervasIve approach for grId coMputIng Dealing with grid computing, people frequently face issues hindering practical usage of grid. The core grid infrastructure is set up of a network of high performance nodes while resources remain largely unused. Frequently given reasons are the missing of maturity of grid technologies in terms of: •

•

Security: Providing data and/or computational power across administrative domains entails severe security hazards. Storage of sensitive data and the execution of foreign code is still a delicate issue. Legal aspects: Numerous legal aspects need to be defined in order to regulate interactions within grids; for example, how to handle environmental data designated to calculate transnational meteorologic models, not being allowed to be handled outside of country frontiers. Or who is to be called for account

39

The Ubiquitous Grid

•

•

•

when a grid is flooded by hacking attacks or DOS-attacks? Usability: Grid applications are far from being plug and play applications. They often have a tremendous administrative overhead and complexity overchallenging non-computer scientist end users. Performance guarantees: As the goal of grid computing is to manage dynamically changing resources, performance cannot be guaranteed. Billing mechanisms: This aspect goes inline with missing performance guarantees. A provider of a time-critical application cannot charge his clients, when the performance of the grid cannot be guaranteed.

Having a look in the field of pervasive computing, you find numerous applications at the first sight. The vision, to be surrounded by smart objects, detecting your needs, and initializing all necessary processes, is very tempting and we estimate pervasive computing to be a seminal field of research. Anyhow those smart objects are still not part of our every day life. Research activities in this area primarily concentrate on feasibility studies of futuristic nice-to-have-things. They currently don’t deliver ready to use technologies and factor out usability aspects (Truong et al., 2004). In terms of mobility the inclusion of lightweight devices is often not taken into consideration due to restrictions concerning storage and performance capabilities, screen size, input modalities, battery size, and the additional complexity in order to cope with log-on, log-offs and location changes of mobile user terminals. The above research results and arguments justifying the lack of applications lead us to the following conclusions:

40

1.

2.

3.

A typical approach for developing mobile applications is to concentrate on the tasks at hand rather than on how these tasks are implemented. Pursuing this method, we face restrictions from lightweight devices and furthermore supplementary mechanisms need to be included in order to cope with major drawbacks. Anyhow tasks cannot be completed with the traditional convenience of desktop computing and hence applications are thought to be dissatisfying. Pervasive computing concentrates on tasks, we usually do without the aid of computers. At this early stage of research, there are no mature, easy to use technologies and additionally we are not used to them. But we expect that the future will bring a lot of technologies that will make this vision possible. In consideration of the fact that there is a lack of “ready to use” applications and technologies leveraging mobile users in both fields, we suggest to choose a more user centered approach and to concentrate on tasks we typically do with the aid of mobile devices. In order to design a satisfactory application, the following factors can enhance the user’s acceptance: • Technologies: There are a lot of mature technologies. Making use of them may enhance the user’s convenience and prevents from technical infirmity reducing the user’s acceptance. • Interaction: The use of proved, empirically tested interaction modes enhances the usability of systems significantly (Sedlar, 2004). • Usage trends: Taking usage trends into consideration means to respond to user needs and thus provide a proper user centering.

The Ubiquitous Grid

dIscussIon of approaches A ubiquitous grid can be realized in two different ways:

Implicit Approach The following approach is based on pervasive computing infrastructure, providing an environment enhanced with smart objects, able to communicate. These entities are designed to initiate interaction upon matching with the user profile(s). Taking up this concept, smart objects could be activated upon visual recognition. For example, a user can take a picture of an entity of interest and send it to the grid in order to be processed. Having extracted the visual information, the corresponding smart object is activated. This approach has the advantage that no user profiling is needed in order to retrieve contextbased information (Schmidt, 2000). Instead of providing accurate filters for push-information based on complex user profiles, the system offers content on-demand. Furthermore it profits from technical advances in the field of pervasive computing, aiming natural device independent interaction and thus serves as a base to detach interaction from mobile devices. Pursuing this concept, the main disadvantage is that all objects need to be enhanced for information retrieval. Due to the fact that environments equipped with smart objects are rare at this early stage of research in the field of pervasive computing, we envisage the following approach in order to realize a first prototype of a ubiquitous grid.

User Controlled Approach This approach is designed to work independently from the pervasive computing environment. Thus no smart objects are involved to deliver context- or location-bound information (Schmidt, 2000). Considering the example scenario as outlined in the introduction, an information request is

conveyed through pictures. Referring to MPEG7 research results it becomes obvious that visual content extraction without the provision of context information is not accurate enough yet (Lew et al., 2006). Having a concept in mind, which is neither restricted to a specific context nor needs to be adapted to a particular application, we propose to make use of the information of context a user personally has. For example a user is taking a picture of a leaf in order to retrieve the name of the tree the leaf is from. The user doesn’t need a content extraction telling her that the item on the picture is a leaf. Incorporating the user’s knowledge about context corresponds to the functionality of an Internet search engine. Here the user also enters all known keywords in order to retrieve suitable information. Following this methodology, three approaches can be conceived: 1. 2.

3.

Visual information is annotated with additional keywords. Visual information is sent to the appropriate application. Referring to the example introduced above, the picture is sent to an application providing information about leaves. The user chooses keywords to obtain a list of available applications. For example, the user enters the keyword “leaf” and the system returns a list with the applications “leaf recognition (tree)” and “leaf recognition (flower).”

The third approach is preferred over the other two because it best supports the user needs and will deliver the most accurate results.

applIcatIon scenarIo Analyzing the SWOT2 attributes one can envision the following setting and scenario:

41

The Ubiquitous Grid

As most people are in the possession of a mobile phone with at least average capabilities, the focus will be on these devices. They can communicate and handle data over wireless connections, have networking capabilities, send and receive SMS, and are able to take reasonable pictures. As for the interaction modes we will use SMS and the handhelds capability of taking pictures. SMS was conceived as add-on for mobile phones and nobody believed that someone could use this feature instead of a short phone call. Actually it became a worldwide hype by providing discrete, anonymous and ubiquitous communication. In order to preserve these characteristics, we chose the handhelds capability of taking pictures, complementing the communication with SMS as a discrete, accepted and natural interaction mode. In line with user demands, we took up the trend one step further towards business networking as a base for the application scenario below. Aside from presenting own results and getting up to date with other ongoing activities, a main aim of a conference is to get in touch with other similar minded researchers to discuss current topics of interest. This task can be supported by providing you with professional background information of a participant when requested and can be realized as follows: During the coffee break you see a person you are interested in. You are taking a picture and you process it within the grid. Promptly you get an SMS with the affiliation and research interests of the participant.

conclusIon and future prospects In this chapter we have studied the potential of the application and lessons learned from mobile computing and ubiquitous computing in the realm of grid computing. The project from which this paper stemmed aims to promote and to develop

42

grid technologies within Austria; in which a questionnaire and interviews have been conducted to determine the feasibility of a spectrum of grid applications. This chapter goes one step further to identify the application domains and the requirements of these applications in the context of mobile and ubiquitous computing. These requirements are both intrinsic and external to the nature of mobile and ubiquitous computing, such as network requirements, service requirements and application requirements. The main contribution of this research is to identify the integration patterns of the mobile grid and to propose a theoretical and practical approach on how to realize it. In the future the architecture of such approach will be proposed and the application scenarios will be extended in a way that fill the gap between the technology promises and user expectations, including issues such as security, legal aspects, or usability.

references Ault, M., & Tumma, M. (2004). Oracle 10g grid & real application clusters. Rampant Techpress. Colasanti, F. (2004). Grids: A crucial technology for science and industry. ERCIM News, (59), 3. Ferscha, A. (2003). What is Pervasive Computing? Peter Rechenberg Festschrift zum 70 (pp. 93-108). Geburtstag, Universitätsverlag Rudolf Trauner. Foster, I., & Kesselman, C. (2004). Grid2: Blueprint for a new computing infrastructure (2nd Ed.). Morgan Kaufmann. Gentsch, W. (2004). Grid: Defining the future of the Internet. GRIDtoday, 3(33). IFCA Institute of Physics of Cantabria. (2004). Grid projects. Retrieved from http://grid.ifca. unican.es/dissemination/Grid\_Projects\_home. htm.

The Ubiquitous Grid

Lew, M.S., Sebe, N., Jjeraba, C., & Jain, R. (2006). Content-based multimedia information retrieval: State of the art and challenges. New York: ACM Press. Pennanen, M., & Ylikerälä. (2004). Photorealistic visualization with grid-based technology. ERCIM News, (59), 51-52. Ritrovato, P., & Gaeta, M. (2004). The European learning grid infrastructure integrated project. ERCIM News, (59), 24-25. Schmidt, A. (2000). Implicit interaction through context. Personal Technologies, 4(2). Sedlar, P., & Kotsis, G. (2007). The ubiquitous grid. In Proceedings of the 5th Annual Conference on Advances in Mobile Computing and Multimedia, MOMM 2007, Jakarta, Indonesia (pp. 113-125). Sedlar, P. (2004). Dialoggesteuerte Schnittstellen für mobiles Lernen. Master’s thesis, Fachhochschule Hagenberg, Austria. Truong, K.N., Huang, E.M., Stevens, M.M., & Abowd, G.D. (2004). How do users think about ubiquitous computing? Extended Abstracts of ACM Human Factors in Computing Systems: CHI 2004 (pp. 1317-1320). Weiser, M. (1991). The computer of the 21st century. Scientific American, pp. 94-100. Wellner, P., Mackay, W., & Gold, R. (1993). Computer augmented environments: Back to the real world. Communications of the ACM, 36(7).

Grid Computing: A grid is an infrastructure of geographically distributed resources, comprising hardware components to aggregate and to coordinate resources. By using a grid of computers, it is possible to aggregate computational power to generate a huge virtual multi-computer ready for processing, storage, and communication. MPEG 7: Is formally called “Multimedia Content Description Interface.” It is a multimedia content description standard developed to describe content itself and thus allow fast and efficient searching for material that is of interest to the user. Pervasive Computing: Pervasive computing aims to develop interaction paradigms, where information processing has been thoroughly integrated into everyday objects and activities, allowing computers to vanish into the background. Smart Objects: Intelligent artefacts, embedded in a pervasive computing environment, detecting user needs and initializing all necessary processes. Usability: Is an equivalent to “user friendliness” and denotes the ease with which people can employ a tool or an object in order to achieve a particular goal. User Profile: Or simply “profile” is a collection of personal settings enabling the personalization of a system.

endnotes key terMs

1 2

Context: Context comprises relevant information about a service’s situation of use, for example information about the user’s interest, device’s display capabilities, or geographic location of service invocation.

http://www.austriangrid.at/ Strengths, Weaknesses, Opportunities and Threats

This work was previously published in Handbook of Research on Mobile Multimedia, Second Edition, edited by I. Ibrahim, pp. 66-75, copyright 2009 by Information Science Reference (an imprint of IGI Global). 43

44

Chapter 1.5

RFID Technologies and Applications Christian Kaspar Georg-August-Universität Göttingen, Germany Adam Melski Georg-August-Universität Göttingen, Germany Britta Lietke Georg-August-Universität Göttingen, Germany Madlen Boslau Georg-August-Universität Göttingen, Germany Svenja Hagenhoff Georg-August-Universität Göttingen, Germany

IntroductIon Radio frequency identification (RFID) is a radiosupported identification technology that typically operates by saving a serial number on a radio transponder that contains a microchip for data storage. Via radio waves, the coded information is communicated to a reading device (Jones et al., 2005). RFID does not represent a new development; it was devised by the American military in the 1940s. Since the technology’s clearance

for civil use in 1977, RFID has been successfully used for the identification of productive livestock, for electronic immobilizer systems in vehicles, or for the surveillance of building entrances (Srivastava, 2005). Due to decreasing unit costs (especially for passive transponders), RFID technologies now seem increasingly applicable for the labeling of goods and semi-finished products. By this, manual or semi-automatic data entry, for instance through the use of barcodes, can be avoided. This closes the technical gap

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

RFID Technologies and Applications

between the real world (characterized by the lack of distribution transparency of its objects) and the digital world (characterized by logically and physically unambiguous and therefore distribution-transparent objects). In addition, RFID facilitates fully automated simultaneous recognition of more than one transponder without direct line of sight between reader and transponders.

execution system [MES], supply chain management [SCM], or e-commerce applications). The processor sends commands to the reader and receives its replies. The reader is connected to the processor through either a serial interface or a network connection. It contains a so-called “coupling unit,” which allows the reader to modulate coded commands onto a magnetic or electromagnetic alternating field. The size and form of this coupling unit may vary, and its dimension determines the design of the reader. The transponder has to be attached to the object to be identified. It is the actual information carrier. All transponders in the reader’s field receive commands and send back their response data. A transponder usually consists of a microchip and a coupling unit. There are various transponder designs; most common, however, are small spools attached to adhesive film.

2.

confIguratIon of rfId systeMs 3. A typical RFID system consists of three basic components (Jones et al., 2005): (1) a computer, (2) a reader, and (3) a transponder, as depicted in Figure 1. 1.

The computer runs an application that requires real world data (for instance enterprise resource planning [ERP], manufacturing

Figure 1. Logical RFID system architecture (Bitkom, 2005; Thiesse, 2005) Target application (1)

ERP

MES

Monitoring Middleware (5)

SCM

Reporting

Storage

Enterprise Application Integration

Events & Alerts Edgeware (4)

Command Tag business layer Data management layer Device management layer

Raw data RFID-Hardware

E-Commerce

Config. data

RFID devices: Transponder, reader, printer, sensors… Reader (2)

Transponder (3)

Reader

Transponder

Transponder

45

RFID Technologies and Applications

An application system that receives real world data through RFID technology has to take into account several factors for the processing of this data (Thiesse, 2005): it must be capable of filtering out erroneous messages; it needs to aggregate received data into complex events; it must support the syntactic and semantic transformation of received data and save it for analytical purposes. In addition to the three basic components, the RFID system’s technical architecture consists of two more elements (also see Figure 1): 4.

5.

RFID hardware has to contain control software that both transforms the raw data of radio communication into events compatible with the application and that reformats the application commands into data legible for the transponder. This type of software is referred to as “edgeware.” It controls the used data’s format and its tagging; it also monitors the connected RFID devices. Middleware systems pass on relevant events to the connected applications in the individual syntax and semantics. Middleware is mainly used for the simplification of configuration and for the alignment of RFID systems to the requests of various application fields and target applications.

technIcal standards for rfId systeMs Efforts to standardize systems on the basis of RFID technologies occur in three fields: standardization of transponder technology, of reader technology, and of RFID middleware. They are discussed consecutively in the following. First, transponder technologies can be classified using three criteria (Flörkemeier, 2005): according to the individual reading distance, there are close-coupling, remote-coupling, or long-range-coupling systems; according to the energy supply, there are passive, semi-active,

46

and active transponders; with respect to the storage structure there are so-called “writeonce/read-multiple” (WORM), read-write, and complex data structures. Unrelated to its reading distance, energy supply, and storage structure, a transponder usually saves at least one 96 bit (max) identification number (Flörkemeier, 2005). This identification number may be formatted according to the widely used number formats, for example, the universal product code (UPC), the European article number (EAN), or the serialized shipping container code (SSCC). The electronic product code (EPC) comprises a new development for RFID technology-based product identification. This code was specified by the Auto-ID Center (and its successor organization EPCglobal), a collaboration of producers and research facilities. In the future, several branches (not just retail) are to use EPC as a universal identification code for object identification (Bitkom, 2005). Four radio frequency bands are open for the radio transmission between transponders and readers worldwide: low frequency (LF), with a frequency band between 100-135 kHz; high frequency (HF), with a frequency band around 13.56 MHz; ultra high frequency (UHF), with a frequency band between 868-956 MHz; and the microwave range at 2.45 and 5.8 GHz. Ever since the first standardization of RFID technologies for the labeling of productive livestock in 1996 (ISO 11785) which used low frequency technology, both the International Organization for Standardization (ISO) and EPCglobal published several different system specifications concerning the four frequencies. LF and older HF systems make possible data transmission of 5 kbit/s and recognition rates of up to 10 transponders per second. Newer HF systems, however, allow for data transmission of 100 kbit/s and recognition rates of 30 (HF) or up to 500 (UHF) transponders per second. With regard to their configuration, there are four different types of readers: stationary gatereaders (used, for instance, at loading gates); compact readers and mobile readers, which

RFID Technologies and Applications

combine antennae and reading/writing appliances in compact, portable bodies; and vehicle-bound readers used solely stationary, for instance, in the cold room of a cooling transporter (Bitkom, 2005). EPCglobal specified a so-called readerinterface-protocol—an XML-based communication protocol—for the communication between the reader and the external target application (Flörkemeier, 2005). Third, RFID middleware is used for the data processing and aggregation into complex events, for the control and synchronization of these events, and for the simplified configuration of the RFID system for the target application (Bitkom, 2005). EPCglobal specified four technical components for the realization of RFID middleware systems (see Figure 2), which are described in more detail next: • Savant represents a normed interface between commercial RFID middleware and its target application. Savant is used for the

aggregation of RFID identification events into custom-designed events (e.g., converging a number of transponder identifications into the event of arrival of a single good). • The Physical Markup Language (PML) includes attributes of objects, processes, and environment. The PML Core vocabulary specifies the semantics for the exchange of context information on the basis of sensor data. PML Core is mainly used in connection with the reader-interface-protocol. • The EPC Information Service (EPC-IS) sends data to the individual transponderlabeled objects. The EPC-IS does not only use data sources of the individual RFID system, but can also access information of external sources. • The Object Naming Service (ONS) is a simple index service that is used for the translation of EPC object identifiers into customary Internet (DNS-) resource-addresses. The ONS receives the EPC from the reader,

Figure 2. Standard components of RFID middleware (Flörkemeier, 2005)

External software applications

Object Naming Service (ONS)

DNS Protokoll

PML EPC Information Service

PML

PML Savant Reader-interfaceprotocol & PML Core Reader

RFID transponder

RFID transponder

RFID protocols UHF Class 0/1 & HF Class 1

47

RFID Technologies and Applications

assigns it a specific EPC-URI, and translates this URI (Uniform Resource Identifier) into DNS-name.

applIcatIon fIelds for rfId technologIes RFID technologies permit the automatic identification of objects equipped with transponders. Transponders, which have sufficient storage capacities, make possible the object-linked transport of data about the object or about transport history. RFID technologies are useful in the electronic support of goods and turnover logistics. Potential areas of use of RFID in electronic logistics processes include the automatic processing of transport and transaction processes (especially for automatic stocking), the (semi)automatic decentral controlling of delivery chains (especially concerning goods traceability), the localization of object holders and containers, the control of production processes, as well as the configuration of security applications. In contrast to conventional identification strategies, RFID technologies offer four advantages (Strassner & Fleisch, 2005): the object identification does not rely on line of sight; several objects can be identified simultaneously (bulk reading); the data belonging to an object can be saved directly at the object (there is no need for a central database); and RFID transponders are generally more resistant than conventional identifiers (for example barcodes). The effects of RFID use in electronic logistics processes can be assessed from three different perspectives (Alt, 2004): • From the technical perspective, RFID transmits real time information, which is made available to all involved in these processes. On the other hand, the interface between operator and machine and between machine and machine has been improved. This enables a flow of information that is

48

free of disruption (EDI applications come to mind). The margin of error when entering and processing data is reduced. • With regard to the process, RFID decreases the margin for error for object identification and increases time and cost efficiency (including chaotic processes). On the one hand, fixed costs can be lowered. On the other hand, the economically reasonable information intensity can be increased with regard to the carrying out of processes (Lietke, 2005; Schumann & Diekmann, 2005). • From a strategic perspective, an increased object related information intensity enables a decrease in inter-company transaction costs and the creation of a basis for increased (and economically reasonable) independence between cooperating business partners. This produces potentials for outsourcing of tasks. Multilateral clearing-centers can be responsible for the accounts of services rendered. Taking the current development status of RFID technology into consideration, four problem areas exist (Mattern, 2005): bulk-reading, radio signal disruptions, transponder costs in open-loop systems, and safety concerns. Even if we want to bulk-read several transponders, these transponders need to be in close proximity to the reader because RFID technology operates from a distance of a few centimeters (passive transponder) to a few meters (active transponder). When identifying objects, errors can occur due to radio signal disruptions. Environmental factors (such as shadowing effects, reflections, etc.) or transponder effects are possible factors for radio signal disruptions. Cost-benefit relations appear unclear with regard to using the transponder to identify B- and C-goods/products. When marking single boxes or bulk (closed-loop product systems) that are reusable, the costs for the transponder are unproblematic because they can be used multiple times. Open-loop systems in which the transpon-

RFID Technologies and Applications

der is only used once are not as cost effective, and even the target cost of five U.S. cents per piece is too high (Tellkamp, 2005a). There are safety related concerns particularly with regard to unauthorized scanning of the data, unauthorized manipulation of data, or willful destruction of transponder data through mechanical, chemical, or electromagnetic forces (Srivastava, 2005). At this point in time, the usage of RFID technology is found in different applications, including using RFID technology in commerce, manufacturing industry, and service sectors. In the following, the potential as well as problems connected to using the RFID technology in these three areas will be clarified. In the area of commerce, RFID transponders offer a number of advantages particularly with respect to the placement of goods in the sales room (Tellkamp, 2005a): in real time, with the help of the automatic identification of goods, one can easily determine when stocks are running low on a particular product or whether there is a surplus of another product that has not been selling well. Additionally, in real time, RFID enables one to verify the target and actual deviation or rather the verification of stored goods, or the location of goods that are not at their designated place. Furthermore, RFID offers the potential of avoiding theft in the stores. Also, radio frequency identification tags with their stored data form the basis of improved consumer protection. The close adherence to protective regulations such as the continuous refrigeration of fresh produce can be controlled and proven through the supply chain (Thiesse, 2005). Because there is a lack of industry standards as well as a lack of complete solutions for RFID infrastructures, the diffusion of RFID technologies in retail businesses remains problematic. Moreover, the production costs of passive transponders are currently at 20 Euro cents and too expensive for smaller products in an open-loop system where transponders are not being re-used. Additionally, using the RFID

technology generates new and often unsolved challenges when coordinating the supply chain as well as questions concerning data protection. Pilot RFID projects include the cooperation between Kaufhof and Gerry Weber in the areas of logistics of clothes where production, storage/ distribution facility, and consumer market is concerned (Tellkamp, 2005b), the logistics of the retailer Wal Mart, or the so-called “Future Store” of the Metro AG. Because information availability is increased using the RFID technology, coordination costs are lowered for the manufacturing industry. Therefore, by employing procurement processes, a just-in-time production can target C-goods as well (Schumann & Diekmann, 2005). In the area of production planning and managing, we can abandon a complex (and therefore expensive) centralized planning in favor of a decentralized coordination when dealing with object-linked data transfer. Automatic blockage of stocks/goods in storage facilities and automatic order release for computer-controlled manufacturing sites are both examples of a decentralized coordination. Furthermore, the quality control of finished or semi-finished products can be relinquished to the buyer, and as a result liability risks decrease for businesses that further process the product. RFID systems are also used in the car industry, for example, BMW with its object accompanying transponders in the motor vehicle manufacturing process as well as VW with its radio supported object localization (Strassner & Fleisch, 2005). In the service sector, RFID technology is used to develop electronic or electronically supported admissions tickets. One well-known example for this development is the ticketing for the Soccer World Championship 2006 in Germany. Less prominent, but well established through daily usage, are RFID applications in the area of tourism such as radio supported ski passes or parking tickets. As opposed to conventional paper tickets, the RFID supported solution has a multitude of advantages (Flor, Niess, & Vogler, 2003):

49

RFID Technologies and Applications

• By distributing electronic tickets, the distribution logistics can be reduced significantly and in extreme cases becomes dispensable. • The seller of electronic tickets has the opportunity to decrease or end the distribution of tickets. • Electronic tickets can be made safe against counterfeit with the help of cryptographic techniques, electronically checked for their authenticity, and if lost, the tickets can easily be redistributed. Apart from being used for the distribution of tickets, RFID technology is also reviewed as a potential carrier of advertising information. For example, the Finnish cell phone manufacturer Nokia recently introduced a cell phone with an integrated reader. According to a showcase, the data for a song that was hidden within an advertisement could be downloaded (Nokia, 2004).

future challenges Even though RFID technology offers many opportunities to automate current processes and to generate new processes, the adaptation of this technology has evolved slower than anticipated by most supporters. The challenges that RFID technology faces are in the areas of technology, customer acceptance, and cost, as illustrated in the following. The technological challenges are particularly apparent in attempts to standardize the operation. Having clear technological standards, the smooth integration of RFID technology in existing systems would be enabled. In this context, particularly the multitude of used frequencies poses a problem. Due to this, so-called multimode readers that are capable of using different frequencies are growing in popularity (Garfinkel & Rosenberg, 2006). An additional challenge in the area of technology is the

50

processing of enormous amounts of data that are produced when using the RFID technology. There is a great need for efficient algorithms to analyze the data (Thiesse, 2005). Additionally, efficient encryption techniques have to be employed to protect the data so that it cannot be read by third parties (Spiekermann & Berthold, 2005). RFID technology is not as popular with customers as expected (Günther & Spiekermann, 2005). Mainly, this lack of acceptance is due to the following reasons: concerns over data protection, little knowledge about the technology used (Kern, 2006), and the fact that RFID is still in the early stages of innovation and thus not very developed. The existing technology (in this case the barcode) is seen as sufficient. Therefore customers typically remain skeptical of new radical technological innovations (Sheffi, 2004). Additional customer education and availability of information could increase customer acceptance of RFID technology (Boslau & Lietke, 2006). The cost of implementing RFID technology on a larger scale remains the most significant barrier for its usage (Thorndike & Kasch, 2004). The RFID transponders are the most expensive component in the development of the technology. EPCglobal is trying to work on this problem by seeking to reduce the costs of the transponders when developing a standard for RFID (low-cost RFID). Small transponders, easy data exchange protocols, and simple data structures are cornerstones of a strategy that enables RFID technology to be used more widely (Garfinkel & Rosenberg, 2006). However, the initial target of five cents per tag has not been realized so far. In this context, the polymer technology has come to our attention. In this case, RFID chips are not made from silicon anymore but from plastic. These polymeric tags can be directly stamped onto the product (Tellkamp, 2005a). RFID technology has not been proven to be economic, which poses another problem (Strassner, Plenge, & Stroh, 2005). Another question arises when looking at the value chain: how does one split the costs for this technology

RFID Technologies and Applications

Figure 3. Development trend for RFID technology use (Strassner & Fleisch, 2005)

Integration depth

Metro example

Products Integration depth Packages C resources B resources Carrier

VW example Closed systems

A resources Open systems

(Tellkamp, 2005a)? So far, the manufacturer was responsible for financing RFID transponders. However, the subsequent levels of the value chain profited the most from the technology (“essential paradox of RFID,” METRO Group, 2004, p. 26). Manufacturers of goods now have to make a decision as to how much they want to invest in this new technology in order to profit from it on the one hand and to satisfy the demands of retailers on the other (Thorndike & Kasch, 2004). Because “it is most likely that barcodes and RFID systems will coexist” (McFarlane & Sheffi, 2003, p. 5), a transitional period where both technologies are used has to be financed. Generally, there is a trend in the IT market that an increase in the depth of the integration (e.g., from A goods to C goods) supports an increase in the overall range of the integration (from single functions and departments to businesses and networks). RFID technologies not only promote this trend, but the development of RFID technology itself suggests this trend: with respect

Integration range

to the depth of the integration, one trend is that carriers (such as containers), single palettes, and the product itself are easily identifiable. When looking at the overall range of the integration, a trend that shows a move from closed to open systems can be identified. This trend seems to be dependent on the reduced prices for passive radio transponders (see Figure 3).

references Alt, R. (2004). E-Business und Logistik. In P. Klaus & W. Krieger (Eds.), Gabler Lexikon Logistik—Management logistischer Netzwerke und Flüsse. Retrieved September 28, 2006, from http:// www.alexandria.unisg.ch/publiscations/23760 Bitkom. (2005). RFID—Technologie, Systeme und Anwendungen (White Paper). Berlin: Bundesverband Informationswirtschaft, Telekommunikation und neue Medien e.V.

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RFID Technologies and Applications

Boslau, M., & Lietke, B. (2006). RFID is in the eye of the consumer—Survey results and implications. In N. Papadopoulos & C. Veloutsou (Eds.), Marketing from the trenches: Perspectives on the road ahead (pp. 1-18). Athens: Atiner. Flörkemeier, C. (2005). EPC-Technologie—vom Auto-ID center zu EPCglobal. In E. Fleisch & F. Mattern (Eds.), Das Internet der Dinge— Ubiquitous Computing und RFID in der Praxis (pp. 87-100). Berlin: Springer. Flor, T., Niess, W., & Vogler, G. (2003). RFID: The integration of contactless identification technology and mobile computing. In D. Jevtic & M. Mikuc (Ed.), Proceedings of the Seventh International Conference on Telecommunications ConTEL (Vol. 2, pp. 619-623). Piscataway: IEEE Press. Garfinkel, S., & Rosenberg, B. (2006). RFID applications, security, and privacy. Upper Saddle River, NJ: Addison-Wesley. Günther, O., & Spiekermann, S. (2005). RFID and the perception of control: The consumer’s view. Communications of the ACM, 48(9), 73-76. Jones, P., Clarke-Hill, C., Comfort, D., Hillier, D., & Shears, P. (2005). Radio frequency identification and food retailing in the UK. British Food Journal, 107(6), 356-360. Kern, C. (2006). Anwendung von RFID-systemen. Berlin: Springer. Lietke, B. (2005, May). Supply chain management in a new institutional framework. Paper presented at European School on New Institutional Economics (ESNIE), Cargèse, Corsica. McFarlane, D., & Sheffi, Y. (2003). The impact of automatic identification on supply chain operations. The International Journal of Logistics Management, 14(1), 1-18.

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METRO Group. (2004). RFID: Uncovering the value.RetrievedSeptember28,2006,fromhttp://cachewww.intel.com/cd/00/00/22/34/223431_223431. pdf Nokia. (2004). Nokia unveils the world’s first NFC product, Nokia NFC shell for Nokia 3220 phone. Retrived December 12, 2004, from http://press. nokia.com/PR/200411/966879_5.html Schumann, M., & Diekmann, T. (2005). Objektbegleitender datentransport entlang der industriellen wertschöpfungskette. In Arbeitsberichte der Abt. Wirtschaftsinformatik II, Georg-AugustUniversität Göttingen, No. 6. Sheffi, Y. (2004). RFID and the innovation cycle. The International Journal of Logistics Management, 15(1), 1-10. Spiekermann, S., & Berthold, O. (2005). Maintaining privacy in RFID enabled environments— Proposal for a disable-model. In P. Robinson, H. Vogt, & W. Wagealla (Eds.), Privacy, security and trust within the context of pervasive computing (pp. 137-146). New York: The Kluwer International Series in Engineering and Computer Science, Springer. Srivastava, L. (2005, April). Ubiquitous network societies: The case of radio frequency identification. Paper presented at ITU Workshop on Ubiquitous Network Societies, Geneva, Switzerland. Strassner, M., & Fleisch, E. (2005). Innovationspotenzial von RFID für das supply-chain-management. Wirtschaftsinformatik, 47(1), 45-54. Strassner, M., Plenge, C., & Stroh, S. (2005). Potenziale der RFID-Technologie für das Supply Chain Management in der Automobilindustrie. In E. Fleisch & F. Mattern (Eds.), Das Internet der Dinge—Ubiquitous Computing und RFID in der Praxis (pp. 177-196). Berlin: Springer.

RFID Technologies and Applications

Tellkamp, C. (2005a). Automatische Produktidentifikation in der Supply Chain des Einzelhandels. In F. Mattern (Ed.), Total vernetzt. Szenarien einer informatisierten Welt (pp. 225-249). Berlin: Springer. Tellkamp, C. (2005b). Einsatz von RFID in der Bekleidungsindustrie. In F. Mattern (Ed.), Total vernetzt. Szenarien einer informatisierten Welt (pp. 143-159). Berlin: Springer. Thiesse, F. (2005). Architektur und Integration von RFID-systemen. In E. Fleisch & F. Mattern (Eds.), Das Internet der Dinge—Ubiquitous Computing und RFID in der praxis (pp. 101-117). Berlin: Springer. Thorndike, A., & Kasch, L. (2004). Radio Frequency Identification—RFID in Handel und Konsumgüterindustrie: Potenziale, Herausforderungen, Chancen. IM, 19(4), 31-36.

key terMs Bar Code: An automatic identification technology that encodes information into an array of adjacent varying width parallel rectangular bars and spaces, which are scanned by a laser. Coupling Unit: Allows the modulation of coded commands onto a magnetic or electromagnetic alternating field; can vary in size and form. Edgeware: Control software that transforms the raw data of radio communication into events compatible with the respective application and also reformats application commands into transponderlegible data.

Electronic Product Code (EPC): 64- or 96-bit code based on current numbering schemes (Global Trade Item Number [GTIN], etc.) containing a header to identify the length, type, structure, version, and generation of the EPC, the manager number, which identifies the company or company entity, the object class, similar to a stock keeping unit (SKU), and a serial number, which uniquely identifies a specific item of the object class. Middleware: Software residing on a server between readers and enterprise applications to filter data and pass on only useful information to applications. Some middleware is able to manage readers on a network. Radio Frequency Identification (RFID): A radio-supported identification technology typically operating by saving a serial number on a radio transponder that contains a microchip for data storage. Reader: Reading device or interrogator communicating with both the transponders (reading/ writing) and the external target application; format can be stationary (gate or vehicle-bound), compact, or mobile. Savant: Normed interface between commercial RFID middleware and its target application; used for aggregating RFID identification events into custom-designed events. Transponder: Mobile information carrier consisting of microchip, antenna, and coupling unit, which can be attached to an object and store data identifying the object or its (transport) history. Term originated from both transmitter and responder.

This work was previously published in Encyclopedia of Multimedia Technology and Networking, Second Edition, edited by M. Pagani, pp. 1232-1239, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 1.6

Understanding RFID (Radio Frequency Identification) Susan A. Vowels Washington College, USA

IntroductIon RFID, also known as radio frequency identification, is a form of Auto ID (automatic identification). Auto ID is defined as “the identification of an object with minimal human interaction” (Puckett, 1998). Auto ID has been in existence for some time; in fact, the bar code, the most ubiquitous form of Auto ID, celebrated its 30th year in commercial use in 2004 (Albright, 2004). Barcodes identify items through the encoding of data in various sized bars using a variety of symbologies, or coding methodologies. The most familiar type of barcode is the UPC, or universal product code, which provides manufacturer and product identification. While barcodes have proven to be very useful, and indeed, have become an accepted part of product usage and identity, there are limitations with the technology. Barcode scanners must have line of sight in order to read barcode labels. Label information can be easily compromised by dirt,

dust, or rips. Barcodes take up a considerable footprint on product labels. Even the newer barcode symbologies, such as 2D, or two-dimensional, which can store a significant amount of data in a very small space (“Two dimensional…,” 2005) remain problematic. RFID proponents argue that limitations of barcodes are overcome through the use of RFID labeling to identify objects.

hIstory of rfId Jeremy Landt (2001) wrote a history of RFID published by AIM, The Association for Automatic Identification and Data Capture Technologies, explaining that in the 20th century, the invention of radar took advantage of the electromagnetic energy that some postulate to have been present at the creation of the universe. By broadcasting and analyzing the reflection of radio waves, radar can identify two important characteristics about an

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Understanding RFID (Radio Frequency Identification)

Figure 1. Barcode examples

object, its position and its velocity. This application of radio waves was a precursor to the use of radio waves in radio frequency identification. During the 1950s, transponders were developed and improved, becoming increasingly more sophisticated and allowing for long-range determination of the identification of aircraft (Landt, 2001). Through the decades of the 1960s, 1970s, and 1980s, inventors, academicians, commercial enterprises, and governmental agencies explored a plethora of opportunities related to the use of early RFID devices, using radio transmissions, “shortrange radio-telemetry,” microwave technology, and radar beams (Landt, 2001). Landt states that RFID was first used commercially in the 1960s by companies that developed security related devices called “electronic article surveillance (EAS) equipment.” Although EAS could only present the detection or absence of a tag, the tags were low cost and provided valuable deterrents to theft. EAS is still an important application of RFID today. Work continued through the 1970s and in the 1980s, as companies began offering a variety of RFID related business solutions, primarily aimed at transportation, controlled access, and animal tracking applications (Landt, 2001). Of primary importance, in 1973 the United States government determined that there was no need for a national standard for electronic vehicle identification. This was serendipitous because it meant that individual firms, researchers, and others could

have the freedom to develop new uses of RFID without being constrained by a governing body (Landt, 2001).

rfId technology radio frequency Electromagnetic waves are comprised of a continuum of emanations, including visible light waves, and invisible frequencies such as television and radio waves, which are lower frequency than light, and x-rays and gamma rays, which are higher frequency than light. Frequencies are measured in Hertz (Hz), kilohertz (kHz), megahertz (MHz), or gigahertz (GHz), and represent the rate of oscillation of the waves. The portion of the electromagnetic spectrum used by radio frequency identification includes LF (low frequency), HF (high frequency), and UHF (ultra high frequency), which are all portions of the radio wave frequency bands, hence the term “radio frequency identification.” An advantage of radio waves over visible light is that radio waves can penetrate many substances that would block visible light. Radio waves range from 300 kHz to 3 GHz (Hodges et al, 2003). Specific frequencies use is controlled by governmental agencies. Some of the concerns relating to RFID are inherent to the technology upon which it is based. For instance, the range over which devices using

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Understanding RFID (Radio Frequency Identification)

radio waves can consistently communicate is affected by the following factors: 1.

The power contained in the wave transmitted The sensitivity of the receiving equipment The environment through which the wave travels The presence of interference (Hodges et al., 2003)

2. 3. 4.

Hardware Components The radio frequency transmissions in RFID travel between the two primary components, the RFID reader and the RFID tag. The reader can be mobile or stationary and is the proactive component. It consists of an antenna and a transceiver, is supplied with power, and generates and transmits a signal from its antenna to the tag and then reads the information reflected from the tag (Hodges et al., 2003). The antenna is used to send and receive signals; the transceiver is used to control and interpret the signals sent and received (“What is…,” 2005). The tag, a transponder, is affixed to the object being identified, such as an automobile, a shipping pallet, or a tagged marine mammal. Thus, we can see a major benefit in using RFID since the data is exchanged using radio waves; it is not necessary to have line of sight between a reader and a tag, such as is required with barcodes.

This permits a great deal more flexibility in the use of the RFID. Although all readers have an external power source, tags may be completely without power or with some degree of power, and fall into three categories1: (1) Passive tags are inert; they do not have any power source and must use energy from the radio wave that is transmitted from the reader. This reduces the costs of the tags, but also reduces their performance. (2) Semi-active tags incorporate a battery which powers the electronic circuitry while the tag is communicating with a reader. Although the power is not used to produce radio waves, the power source improves the performance of the tag, most commonly by increasing the transmission range. (3) Active tags are fully powered by battery; they are able to generate radio waves autonomously, without the need for a reader to first transmit a radio wave. As power sources are added to these tags, we can see that their utility increases, but at the expense of the cost per tag, and so active and semipassive tags are generally reserved for higher-value items (Angeles, 2005; Hodges et al., 2003). The tags consist of three components: antenna, silicon chip, and substrate or encapsulation material (Want, 2004). The antenna is used for receiving and transmitting radio frequency waves to and from the reader. The chip contains information pertaining to the item tagged such as part number and manufacturer. Chips can be either read-only or read-write; the costs are higher

Figure 2. The electromagnetic spectrum (Adapted from Hodges, 2003) TV FM Radio

AM Radio LF

MF

HF

Low frequency Long wavelength

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VHF

Microwaves FM Radar UHF

Infrared

Visible Light

Ultraviolet

X-rays; Gamma Rays High frequency Short wavelength

Understanding RFID (Radio Frequency Identification)

Figure 3.

SAMPLE PAPER LABEL WITH EMBEDDED RFID TAG (Source: http://www.suppliersystems.com/rfid.htm)

RFID TAG MICROCHIP ANTENNA (headings supplied by author)

for the read-write chips. There is a crucial difference between read-only chips and read-write chips. Read-only chips are essentially electronic barcodes. Once the data has been encoded onto the chip, it cannot be modified and, therefore, cannot transmit information about the product as it moves through a sequence of events (“The Write Stuff…,” 2003). A twist on the read-only vs. read-write chips is the EEPROM (electrically erasable programmable read-only memory chip). While individual pieces of information on EEPROM chips cannot be modified, the entire existing data on these chips can be replaced by new data (Angeles, 2005; Want, 2004).

ated through RFID. Traditional barcodes require just the addition of the UPC code to the existing database of items. RFID, by virtue of its use of a computer chip, has the capability of not only storing static data, such as UPC codes, but also of storing dynamically created information, such as movement of the product through a supply chain. Early adopters of RFID are using current database and application systems and modifying them to accommodate the currently modest amount of additional data. However, the industry understands that RFID technology brings with it the promise of huge amounts of data to be managed, stored, and communicated.

software rfId standards The RFID reader and tags represent just part of the entire RFID story. RFID is feasible only due to advances in database management and information technology which have allowed the storing, processing, and analysis of the data gener-

electronic product codes Just as barcodes encoded universal product codes, RFID tags encode electronic product codes,

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Understanding RFID (Radio Frequency Identification)

known as EPCs. The EPC is a unique standard in that it is being systematically developed well in advance of its use rather than being cobbled together on the fly as has happened with other standards. The Auto-ID Center at MIT began the thought process to develop EPC. The original concept was that using RFID and EPC would permit an “Internet of things,” a universe of identified objects that could be tracked, interrogated, and provide added value to supply chains by enabling granular information at the item level for every item, big or small, high value or low value (“EPC: The End of Bar Codes?,” 2003). The Auto-ID Center has completed its pioneering work and passed the torch on to EPCGlobal. The labs of the Auto-ID Center are still in existence under the aegis of Auto-ID Labs (www.autoidcenter.org). EPCGlobal’s focus is not only on the creation of EPC standards, but also on the creation of a global community of EPC users spanning a multiplicity of supply chains around the world (“RFID Implementation Cookbook,” 2006).

frequency allocations The frequencies used by RFID devices are dictated by the governments of the countries in which the technology is deployed. The management and allocation of specific frequencies for use by commercial and governmental agencies has been seen as a responsibility which should devolve to the government to control. As an example, Hall and Schou (1982) argued that what they called the electromagnetic spectrum was essentially a finite resource with strategic importance to national and international communication and to the national economy. With respect to RFID frequency allocations, governments are beginning to realize that it is also important to “harmonize radio communication systems” with other countries in order to benefit most fully from international trade. Developers and users of RFID need to become familiar with the legislation pertaining to RFID for their

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areas. It should be noted that even if there are conflicting frequencies assigned, in some cases it is possible for developers to obtain waivers to test RFID systems outside of assigned spectrums if the system being created is intended for use in another country (Hodges, 2003).

governing bodies/testing and research facilities Two primary governing bodies are involved with development of RFID standards. As mentioned earlier, EPCGlobal has taken up the development of standards from the Auto-ID Center. In addition, AIM Global (the Association for Automatic Identification and Mobility) is working with CompTIA, the Computing Technology Industry Association to develop an RFID certification that will be vendor-neutral and cover such items as “radio frequencies, interference, terminology, and standards” (“CompTIA…,” 2005). Independent testing and research facilities are also moving along the development of RFID technology. Examples include the nonprofit RFID Alliance Lab, based at the University of Kansas (Swedberg, 2006) and the University of Arkansas’ RFID Research Center (“Researcher…,” 2006).

Technology Development Current research is concentrating on tags that are not affected by liquids or metals, and that permit the reading of closely packed tags such as would be used when tagging individual items in a retail situation. Early testing has indicated that Generation 2, known as Gen 2, UHF tags successfully fill these criteria. UHF, or ultra high frequency, permits near-field reading, in which the reader is close to the tag being read, in addition to far-field reading of tags. When transmitting in a near-field situation, the UHF tags transmit through the magnetic field; when transmitting in a far-field situation, the UHF tags transmit through the electromagnetic field. However, others, par-

Understanding RFID (Radio Frequency Identification)

ticularly in the pharmaceutical industry, believe that HF, high frequency, tags are more efficient. The possibility of multiple standards looms as a concern for companies involved in multiple industry sectors (O’Connor, 2006).

rfId successes Transportation Industry EZPASS is a well-known and well-accepted area of RFID technology use deployed in the eastern part of the United States. The EZPASS system was developed for a consortium of states wishing to automate the toll-collecting process on major highways, tunnels, and bridges. With the EZPASS system, motorists are issued a transponder which can be mounting on the inside of the front windshield. When the vehicle passes an EZPASS toll collection point, a stationary reader recognizes the serial number encoded in the transponder. This serial number is used to identify the motorist’s EZPASS account, in which funds are held in escrow. The amount of the toll is automatically deducted from the fund. When the fund balance falls below a minimum amount, it is automatically replenished by charging a credit card account furnished by the motorist. Initially, motorists were induced to participate through discounts offered on tolls collected by EZPASS vs. tolls collected manually. EZPASS has reduced the number of toll takers and, in some instances, increased the throughput of vehicle traffic by allowing drivers to pass the toll collection station at speed. Although the transponders are bulky, they are easily mounted. While there is no charge for the transponders, motorists are responsible to return them when leaving the program; unreturned transponders are subject to a fee (Vavra et al., 1999).2

Supply Chains Supply chains consist of manufacturers and retailers working together to provide product to the end consumer. Supply chain partners rely on close working relationships in order to increase the efficiency and lower the costs of moving product from the raw material stage through manufacturing to the final retailing of the merchandise. RFID is proving to be a valuable tool to this end. Wal-Mart Corporation has been a major force in the deployment of RFID within supply chains. In 2003, Wal-Mart dictated that its top 100 suppliers must implement RFID to identify cases and pallets being shipped through the Wal-Mart distribution system by 2005 (Seideman, 2003). Early examination of the impact has shown that RFID has reduced Wal-Mart’s replenishment cycle and increased inventory accuracy (“Study of Wal-Mart…,” 2004). Another retailer that has made significant contributions to the evaluation of RFID is Metro Group, a German supermarket firm. In its Future Store near Dusseldorf, Metro Group has tested products ranging from razor blades to cream cheese (Tarnowski, 2005). Marks and Spencer, a clothing retailer in the United Kingdom, has been successful in using RFID garment-tagging to increase its inventory accuracy (“Marks & Spencer,” 2005).

Other Applications RFID has already proven to be a valuable technology in a number of industries. Some other applications include asset tracking, condition monitoring, and fleet management. The mining industry was an early adopter of RFID technology. One use of RFID tags in mining was in validating the correct movement of haulage vehicles. These vehicles have read only tags permanently attached to the vehicles dump bed. As a vehicle approaches a dump area, a reader validates that it is approaching the correct dump

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Understanding RFID (Radio Frequency Identification)

bed. Information concerning the transaction, including the vehicle number, the weight of the vehicle before and after the dump, and the time, are recorded automatically. Using RFID obviates the need for human interaction between the scales operator and the vehicle driver (Puckett, 1998). Asset tracking is an important application for RFID. Scottish Courage, a major UK brewer, used RFID to track beer kegs. In the brewing industry, reusable beer kegs constitute a major expense; when kegs are lost or not returned, these costs escalate. By tagging kegs with RFID labels, Scottish Courage was able to cut its keg losses in half, defer the need for purchase of new containers, and improve the visibility of the kegs and products (Wilding, 2004). Because the chips embedded in RFID tags can record environmental information, RFID is very useful in monitoring the condition of tagged items. The United States military uses this feature to monitor the physical condition of munitions, which are very sensitive to heat, humidity, and physical shocks (IDTech Ex Ltd, 2003).

rfId concerns technical Issues RFID users have encountered limitations with its usage. Liquids and metals impede the transmission of radio frequency waves (Leach, 2004). These materials, known as dielectrics, cannot conduct electricity (O’Connor, 2006). Another technical issue concerns the possibilities of collisions. Collisions can occur in two ways. First, if two readers are in close physical proximity, their signals can overlap and interfere with each other. Second, if tags are in close proximity to each other, a similar collision problem can arise. Anticollision schemes to address the first problem include programming readers not to read tags at the same time and setting up an additional system to delete duplicate codes. The

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second problem can be addressed by setting up a query and response requirement between tag and reader that will only permit a matching condition to proceed (Angeles, 2005).

privacy Issues A major concern surrounding RFID is a potential loss of privacy on the part of consumers who purchase items which have been tagged using RFID technology. Consumer privacy advocates question the possibility of firms being able to track consumers by the RFID tags embedded in clothing. This concern has led the EPC to dictate that tags must be equipped, at a minimum, with at least one method for nullifying the transmission of data (Ohkubo et al., 2005). It has been noted that the adoption of item-level tagging in retail supply chains has been slowed at least in part by this privacy concern.

conclusIon RFID is a very exciting technology with huge potential in many applications. As the technology’s cost decreases and it becomes more efficient, the use of RFID is expected to grow exponentially. With that growth, important questions need to be answered to address privacy and other social concerns about the information being provided by these very smart chips.

references Albright, B. (2004). The UPC Turns 30. Frontline Solutions. Vol. 5 Issue 9 (pp. 44-48). Angeles, R. (2005). RFID Technologies: SupplyChain Applications and Implementation Issues. Information Systems Management. Vol. 22 Issue 1 (pp. 51-65).

Understanding RFID (Radio Frequency Identification)

“Automated Order Filling & Warehouse Management Systems from Supplier Systems Corporation,” Supplier Systems Corporation. Retrieved December 17, 2006, from http://www.suppliersystems.com/rfid.htm “Barcode Label Printing Software – Barcode Creator,” Naxter, Inc. Retrieved December 17, 2006, from http://www.naxter.com/format.htm

www.aimglobal.org/technologies/rfid/resources/ shrouds_of _time.pdf Leach, P. T., (2004). Ready for RFID? The Journal of Commerce, Vol. 5 Issue 42 (pp. 12-14). Marks & Spencer Expands RFID Trial, (April 2005). Frontline Solutions, Vol. 6 Issue 3 (pp.1112).

“CompTIA and AIM to Develop RFID Certification,” (2005). Certification Magazine, Volume 7 Issue 2 (pp. 10-10).

O’Connor, M. C. (2006). Wal-Mart Seeks UHF for Item-Level. RFID Journal, Mar. 30, 2006. Retrieved on March 31, 2006 from http://www. rfidjournal.com/article/articleprint/2228/-1/1/

EPC: The End of Bar Codes?, (2003) The Association for Automatic Identification and Mobility, Retrieved December 17, 2006, from http:// www.aimglobal.org/technologies/rfid/resource/ articles/April03/EPCpart1.htm

Ohkubo, M., Suzuki, K. & Kinoshita, S. (2005). RFID Privacy Issues and Technical Challenges. Communications of the ACM, Vol. 48 Issue 9 (pp. 66-71).

“Free Barcode Font Code 39 TrueType Download,” ID Automation.com. Retrieved on April 30, 2006 from http://www.idautomation.com/fonts/free/ Hall, C. & Schou, K. (1982). Management of the Radio Frequency Spectrum in Australia. Australian Journal of Management. Volume 7 Issue 2 (pp. 103-116). Hodges, S. & Harrison, M. (2003). Demystifying RFID: Principles & Practicalities. Cambridge, United Kingdom: Auto-ID Centre, Institute for Manufacturing, University of Cambridge. Retrieved March 5, 2006, from http://www.autoidlabs.org/whitepapers/cam-autoid-wh024.pdf Holmes, T. J. (2001). Bar Codes Lead to Frequent Deliveries and Superstores. The RAND Journal of Economics. Vol. 32 Issue 4 (pp. 708-725). IDTech Ex Ltd, (2003). Smart Labels USA 2003 Conference Review, Smart Label Analyst, Issue 27, April 2003 Landt, J. (2001). Shrouds of Time: The history of RFID. Pittsburgh, USA: The Association for Automatic Identification and Data Capture Technologies. Retrieved March 4, 2006, from http://

Puckett, D & Patrick, C. (1998). Automatic Identification in mining. Mining Engineering. Vol. 50 Issue 6 (pp. 95-100). Researcher to Reveal EPC’s Further Impact at Wal-Mart (2006). RFID Journal, March 27, 2006. Retrieved March 31, 2006, from http://www.rfidjournal.com/article/articleprint/2221/-1/1 “RFID Implementation Cookbook (2nd Release – Sept. 2006),” (2006). EPCGlobal. Retrieved December 17, 2006, from http://www.epcglobalinc. org/what/cookbook/ Seideman, T. (December 1, 2003). The race for RFID, The Journal of Commerce, Vol. 4 Issue 48 (pp.16-18). Study of Wal-Mart reveals first benefits of RFID, (December 2004). Healthcare Purchasing News, Vol. 29 Issue 12 (pp. 6). Swedberg, C. (2006). University of Kansas’ Tag for Metal, Liquids. RFID Journal, April 19, 2006. Retrieved April 22, 2006, from http://www.rfidjournal.com/article/articleprint/2275/-1/1

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Tarnowski, J. & Longo, D. (2005). RFID driving future of tech, Progressive Grocer, Vol. 84 Issue 3 (pp.7-9). The Write Stuff: Understanding the Value of Read/Write RFID Functionality (2003). Intermec. Retrieved March 4, 2006, from http://www.aimglobal.org/technologies/rfid/resources/RFID%20 Read%20write%20WhitePaper.pdf Two dimensional (2D) Bar Code Symbologies (2005). Association for Automatic Identification and Mobility. Retrieved December 27, 2005, from http://www.aimglobal.org/technologies/ barcode/2d_symbologies.asp Understanding Gen 2: Key benefits and criteria for vendor assessment. Symbol White Paper. Retrieved March 7, 2006, from http://promo.symbol. com/forms/gen2/RFID_WP_0106_ final.pdf Vavra, T. C.; Green, P. E.: Krieger, A. M., (1999). Evaluating EZPass. Marketing Research. Vol. 11 Issue 2 (pp. 4-16). Want, R. (2004). The Magic of RFID. ACM Queue. Vol. 2 Issue 7 (pp. 40-48). What is Radio Frequency Identification (RFID)? (2005). Association for Automatic Identification and Mobility. Retrieved December 27, 2005, from http://www.aimglobal.org/technologies/rfid/ what_is_rfid.asp Wilding, R. & Delgado, T. (2004). RFID Demystified: Supply-Chain Applications. Logistics & Transport Focus. Vol. 6 Issue 4 (pp. 42-48). www.autoidcenter.org, Retrieved on March 5, 2006, at http://www.autoidcenter.org

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other resources

www.aimglobal.org www.autoidlabs.org www.epcglobalinc.org

key terMs Active Tag: Type of RFID tag that contains a battery power source that is used for all of its functioning. Active tags can autonomously produce radio waves without the presence of a reader. Air Interface: Air, the medium through which radio waves are transmitted. Alignment: How the reader is oriented to the tag. Auto ID: Auto ID, also called “Automatic Identification,” is a form of ICT that enables identifying information about objects to be gathered through the agency of scanners and readers. Auto ID encompasses barcodes, RFID, and similar tagging technology which can be read and interpreted automatically by a mechanical device. A bar code reader can interpret a printed bar code; an RFID reader can interpret an RFID tag. Bar Code: Automatic identification technology that generally employs a series of black vertical bars separated by vertical white spaces as a method of encoding numeric and alphanumeric data. Commonly used forms of barcodes are placed on consumer goods to identify universal product codes (UPCs). Newer technologies of barcodes include 2D (two dimensional) which allow more information to be encoded using a smaller area. There must be a line of sight between barcodes and barcode scanners in order for information to be read.

Understanding RFID (Radio Frequency Identification)

Dielectrics: Materials, such as liquids, that are not able to conduct electricity. These materials interfere with the transmission of radio frequency waves. EAN (13-digit UPC code): Provides more flexibility than original UPC code. EAS (Electronic Article Surveillance): The use of an RFID tag to identify valuable property in order to reduce theft. EDI (Electronic Data Interchange): The electronic transmission of business information from one supply chain member to another, using a standard data format and standard transaction codes. An EDI transaction, known as Advance Shipment Notice, or ASN, is being coordinated with data tracked by RFID tags on products moving through supply chains. EPC (Electronic Product Code): Encoded on RFID tags and tied to a multiplicity of data concerning the object tagged. Standards are still being developed and proposed for EPC by EPCGlobal, an international organization devoted to creating a community of supply chain firms cooperating to consistent end-to-end partnerships of product movement and tracking. Frequency: Frequencies constitute the rate at which electromagnetic waves, such as light, television, and radio waves, oscillate. Electromagnetic waves are comprised of a continuum of emanations, including light waves which are visible, and invisible frequencies such as television and radio waves which are lower frequency than light, and x-rays and gamma rays which are higher frequency. Frequencies are measured in Hertz (Hz), kilohertz (kHz), megahertz (MHz), or gigahertz (GHz), and represent the rate of oscillation of the waves. Gen 2: Second generation tags. Uses ultra high frequency range of radio waves.

HF (High Frequency): The original range of frequencies used with case and pallet level tagging in supply chains. The signals exchanged between HF tags and readers are subject to attenuation due to dielectrics. Passive Tag: Type of RFID tag that does not have a battery incorporated and therefore must rely on power contained in the radio wave transmitted by the reader. Passive tags are the lowest cost tags, but also perform at the lowest level. Reader: One of the two components of an RFID system. The reader generates and sends a radio wave signal to the tag, and captures and decodes the reflected signal from the tag in order to identify the object to which the tag is attached. All readers have some power source. Also known as an interrogator. RFID: RFID, or radio frequency identification, is a form of Auto ID in which radio waves are used to gather information from electronic tags attached to items such as vehicles, merchandise, or animals. Semi-active Tag: Type of RFID tag with a built-in battery power source which provides power to the electronic circuitry while the tag is communicating with the reader. Semi-active tags do not have enough power to autonomously generate radio waves. Tag: One of the two components of an RFID system. The tag, a radio frequency transponder, is affixed to the product that needs to be identified and is actuated by receiving a radio wave sent to it by the reader. See passive tag, semi-active tag, and active tag. Transceiver: An electronic device which is both a TRANSmitter and a reCEIVER. Transponder: Also known as a tag, a transponder electronically TRANSmits and resPONDs.

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Understanding RFID (Radio Frequency Identification)

UHF (Ultra High Frequency): UHF is being tested with Gen 2 chips as a means of overcoming problems with RF tags because the UHF emanations are not affected by dielectric materials in the same way in which RF is affected. UPC (Universal Product Code): UPCs are used to identify consumer goods and consist of a manufacturer’s number combined with a product number. The manufacturer’s identification number is assigned by the Uniform Code Council; the manufacturer can then assign its own product number.

endnotes 1

2

Some sources simplify the types of tags into two categories: active and passive. See “What is Radio Frequency Identification (RFID)?, 2005 Information also based on the author’s personal experience with EZPass.

This work was previously published in Encyclopedia of Information Communication Technology, edited by A. Cartelli & M. Palma, pp. 782-790, copyright 2009 by Information Science Reference (an imprint of IGI Global).

64

65

Chapter 1.7

Radio Frequency Identification History and Development Chin-Boo Soon The University of Auckland, New Zealand

abstract

IntroductIon

This chapter describes the history and development of Radio Frequency Identification (RFID). Key information on RFID such as the ratification of the RFID standards and important regulations on frequency usage is presented. As businesses move towards the convergence of information, RFID technology provides a step closer to the reality of connecting the real world and the digital world seamlessly. This is possible as RFID communication does not require the line of sight as barcodes do. Thus, is the continued existence of the barcodes technology under threat? Before RFID makes its way into the mainstream, there are teething issues to be sorted out. The immediate attention for a global uptake of RFID is the adoption of a frequency standard that is accepted internationally. This chapter provides an understanding of the RFID technology, its background and its origin

Radio Frequency Identification (RFID) is an Automatic Identification and Data Capture (AIDC) technology. Its application can be found in most industries, offices and even homes. The application ranges from electronic article surveillance (EAS) in retails, electronic toll collection in transportations, to building access control in offices. RFID is fundamentally a radio technology and its history can be traced back to the 1930s (Bhuptani & Moradpour, 2005). The underlying principle of RFID is the transmitting and receiving of data in a form of electromagnetic energy. The primary components are tags and readers. Together these components form a coupling relationship where communication becomes possible. This chapter revisits the history of RFID development and looks at other forms of AIDC. This helps to form an epistemology of what RFID is and its origin, so that we could relate to the various aspects of RFID characteristics when planning on a RFID

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Radio Frequency Identification History and Development

project. The emergence of RFID has raised the question of barcodes’ continued existence (Allen, 1991; Atkinson, 2004). It is therefore inevitable to know the characteristics of RFID and barcodes, and examine their future existence, particularly in the supply chain. The discussion in this chapter is motivated by the activities of RFID surrounding supply chain management. The suppliers’ mandates to use RFID in the supply chains have significant impact on businesses (C. B. Soon & J. A. Gutierrez, 2008). This has created interest in RFID by businesses around the world. It is thus an appropriate topic to introduce RFID. Although this chapter is focused on RFID applications in the supply chains, the technical aspects are common across application areas. This chapter is arranged as follows. First, the development of RFID is summarised with key events identified from the history of RFID. Second, the various concepts of AIDC are discussed. Third, the RFID system is discussed with particular attention to the tag classification and frequency allocation. Fourth, a comparison between RFID and barcodes is made. The continued existence of barcodes and the future of RFID are discussed in the conclusion.

hIstory: the developMent of rfId Electromagnetic theory was developed in the 1800s. Michael Faraday discovered that light and radio waves are part of electromagnetic energy and James Clerk Maxwell demonstrated that electric and magnetic energy travel at the speed of light in transverse waves (Landt, 2001). The discovery led to consequential experiments. In 1896, Guglielmo Marconi successfully transmitted radio waves across the Atlantic (Landt, 2001). Marconi’s demonstration was followed by more innovations. In 1922, radar was developed. The

66

transponder (or tag) and interrogator (or reader) were then bulky and heavy. Radar was extensively used by the Allies during World War II to identify friendly military aircraft. Radar was further developed into a commercial air traffic control system in the late 1950s following the invention of integrated circuits (IC), which greatly reduced the size of RFID components. The 1960s marked the start of RFID development as scientists and commercial businesses started to show interest in the technology. The first concept of RFID for commercial use was probably thought of by Mario Cardullo in 1969 when he worked with an IBM engineer on a car tracking system using barcodes for the railroad industry (Shepard, 2005). Most RFID applications were identified in the 1970s. The use of RFID for EAS began in early 1970s (Bhuptani & Moradpour, 2005). EAS is a simple anti-theft measure for use in retail stores. It is the first and most widely used RFID application commercially (Landt, 2001). Further interest in the adoption of RFID extended to areas such as vehicle tracking, access control, animal tagging, and factory automation. The use of RFID cards for controlling access to office building by Westinghouse (Mullen & Moore, 2005) is an example of access control. Further development improved the reading speed and enabled a longer read range. The advanced RFID systems were utilised to identify railroad cars and track animals in the 1980s, and for electronic toll collection in the 1990s (Bhuptani & Moradpour, 2005). RFID applications became more widespread in the 1990s. The success of electronic toll collection kicked off large scale deployments throughout the United States, Europe and Asia (Landt, 2001). There are two basic systems employed in road toll collection. One uses a contactless card or proximity card and the other uses a transponder fitted into the vehicle. The latter does not require the vehicle to halt at a barrier unlike the proximity card model where, the driver has to stop and hold the proximity card close to a reader at the bar-

Radio Frequency Identification History and Development

rier or toll plaza. Standards for contactless smart cards were developed between 1992 and 1995. Contactless smart cards are now widely used in retail electronic payment, access control, transport fare payment, and airlines ticketing. It is not until late 1999 that RFID made its way into supply chains. Sanjay Sarma, a professor at MIT, started a project called the Distributed Intelligent Systems Center to work on ubiquitous object identification (Sarma, 2005). The centre also developed Electronic Product Code (EPC), Object Naming Service (ONS), Physical Markup Language (PML), and the Savant system. Together these components form the fundamental mechanism in the RFID system known as the EPC network. Sarma and his team developed a microwave prototype installed with a RFID reader. The reader read the tag information on a packet food, retrieved the cooking instructions from a server using the tag identity or EPC, and started cooking with the downloaded instructions. Having

successfully demonstrated the EPC concept using the microwave prototype, Sarma and his team were eager to secure commercial support as well as sponsorships to further develop the technology. After some convincing selling, they finally launched the Auto-ID Center with sponsorship from Gillette and Procter & Gamble on September 30, 1999 (Sarma, 2005). The Center continued its research work, and by 2003 there were six laboratories and more than a hundred sponsors. The increasing demand and interest triggered the Auto-ID Center to spin-out and hence EPCglobal was formed. EPCglobal is a not-for-profit organization jointly administered by Uniform Code Council (UCC) and European Article Numbering (EAN) International, or GS11. Under the GS1 umbrella, EPCglobal’s membership now reaches the entire globe with more than 100 member organizations (Smucker, 2006). A turning point for RFID in supply chain (RFID/SC) widespread use came when WalMart joined the Auto-ID Center in 2001 (Sarma,

Figure 1. The history of RFID 1800s Fundamentals of EM

1922 Radar invented

1896 Radio invented

1970 EAS in use

1960s Research on RFID in laboratory

1958 IC developed

1937 IFF System used in WWII

1980s GM use RFID for commercial purposes Railroad in US Farm animal in Europe

1972 Access control in use UPC developed

1950s Commercial air traffic control system

1999 MIT Auto-ID Center formed EPC developed

1992 - 1995 Contactless Smartcard standard developed

1969 First RFID concept developed

2002 Gillette order 500 million tags 2006

2001 Wal-Mart joined Auto-ID

2003 EPCglobal formed

1990 Toll collection

67

Radio Frequency Identification History and Development

2005). A major field trial was conducted which involved forty companies across eight states and ten cities in the United States. The trial was not only successful, it demonstrated the practicality of RFID/SC and its economic benefits. This prompted Gillette to order 500 million tags in late 2002 and Wal-Mart to announce the mandate for its suppliers in 2003. Both events proved to be the catalysts of RFID/SC adoption. Figure 1 shows the development of RFID described above in a time chart. Other recent RFID applications are location sensing or real-time locating systems (RTLS), content management, electronic pedigree (epedigree), and in the sports for time tracking. The use of RFID for location sensing applications has some successful implementation such as the WhereNet RTLS infrastructure used to track shipping containers at APL terminals (Violino, 2006). Other location sensing innovation using radios includes LANDMARC (Ni, Liu, Lau, & Patil, 2004), RADAR (Bahl & Padmanabhan, 2000), and SpotON (Hightower, Vakili, Borriello, & Want, 2001). The use of RFID for content management includes authenticating and monitoring the content of a desired inventory. Examples of such applications are the e-pedigree used in the healthcare industry (Swedberg, 2008b) and tanker monitoring systems used by petroleum company to ensure the correct type of oil is delivered (Swedberg, 2008a). The use of RFID in the sport arena has several applications such as ticketing and recording the lap time of the NASCAR races (Edwards, 2008).

autoMatIc IdentIfIcatIon and data capture (aIdc) This section describes the various concepts of AIDC and traces the historical context of these technologies in the attempt to draw comparisons to RFID and put in perspective the development

68

of RFID technology. AIDC is a collective of technologies that capture or collect data using automated mechanism without the need for manual input. Finkenzeller (2003) highlights five types of AIDC systems; (1) Barcode, (2) Optical Character Recognition, (3) Biometric, (4) Smart Card, and (5) RFID. Figure 2 illustrates his AIDC diagram. Magnetic Stripe and Magnetic Ink Character Recognition (Mullen & Moore, 2005) has been added to the diagram to illustrate AIDC more fully. The barcode, magnetic stripe, and RFID technologies emerged between the 1930s and the 1940s. Barcode was first patented in 1949 to Norman Woodland and Bernard Silver (Shepard, 2005). Woodland used ancient movie soundtrack encoding schemes and the dot and dash patterns in Morse code to create the first barcode. He extended the dots and dashes vertically to form linear pattern of thick and thin lines. He later realised that the linear pattern had to be scanned from a particular direction. Woodland replaced the linear pattern with a circular centric pattern resembling a bull’s-eye. This design could be read generally from any direction. However, the machine he designed to read the barcode was huge and therefore was not suitable for grocery checkout as it was originally intended. Nevertheless, it did find its way to tracking rail cars on the United States national railroad system in the late 1960s after some modification to the barcode pattern. Meanwhile, barcodes continued to evolve around the grocery industry in the United States. The bull’s-eye system was eventually replaced by the Universal Product Code (UPC) due to the difficulty of printing concentric circles on products. As such, linear barcode was adopted as it was easily printed, and with advanced scanner using laser technology, the linear barcode can be read from different angles. UPC was adopted on April 3, 1973 (Shepard, 2005), and was first scanned in commercial transactions in 1974 (Jilovec, 2004). As its popularity increased, international bodies started to ratify their own standards. EAN Inter-

Radio Frequency Identification History and Development

Figure 2. Overview of AIDC

Barcode system Optical Character Recognition (OCR)

Fingerprint procedure Biometric MM Voice identification

AutoID Magnetic Smart cards

RFID

Figure adapted from RFID Handbook – Fundamentals and Applications in Contactless Smart Cards and Identification (2nd Ed.), Finkenzeller (2003). Copyright John Wiley & Sons Limited. Reproduced with Permission.

national and Japanese Article Numbering (JAN) are the other widely adopted systems. Barcodes became globally adopted in manufacturing, production, and distribution. There are now advanced barcodes with more data storage capacity such as the two-dimensional (2D) barcodes introduced in 1988 (Anonymous, 2006; Man, 2007). The 2D barcodes use the matrix symbol technology to achieve higher data density than the linear barcode while utilising lesser space. Barcode, as referred to in this chapter, is the one-dimensional, linear barcode widely used in retail, manufacturing, and supply chain. Magnetic stripe is another AIDC widely used in the banking industry. Its standard was established in 1970 (Anonymous, 2007a). It is commonly used on credit and debit cards, and access control cards. The magnetic stripe when run past a reader produces an electromagnetic signal recordable by the reader. Another version of magnetic AIDC is the Magnetic Ink Character

Recognition (MICR). Similarly, MICR is widely used in the banking industry. It is being used for bank cheque authentication. Both technologies were adopted by the American Banking Association (Mullen & Moore, 2005) and banks around the world. Optical Character Recognition (OCR) was introduced in the 1960s, almost twenty years after barcode’s emergence. Like the MICR, special characters that are legible to both humans and machines are used to present a series of unique codes. OCR is also used in the banking industry, production, and service and administrative fields (Finkenzeller, 2003). More recently, biometric and smart cards have attracted interest. There are two main forms of biometric identification, one is voice recognition and the other is fingerprinting. A highly sophisticated system converts voice into digital signals to process the authentication of a subject. Voice identification is now being implemented in sup-

69

Radio Frequency Identification History and Development

ply chain management to aid in picking orders (Allen, 1991; Kondratova, 2003). Fingerprinting recognises the unique finger patterns of individuals. As such, its application is commonplace around security and access control. A common application of fingerprinting is the employee time tracking system. Smart cards first gained publicity as a pre-paid telephone card launched in 1984. By 1995, 600 million smart cards were issued (Finkenzeller, 2003). Smart card is a secured data storage device widely used in Global System for Mobile communications (GSM) devices and as cash cards for micro-payments. It has a galvanised input/ output connection with processing capability. An external power source is required to operate the smart card. The card needs to be placed in contact with a reader in order to transfer data. Wear and tear to the smart card processor is inevitable with frequent usage and contact. Another version of the smart card, known as the contactless smart card, uses radio frequency (RF) technology. Data can be transferred without the need to slot the card into a reader, thus no contact with the reader is necessary. The International Organization for Standardisation (ISO) standard for contactless

smart card was developed between 1992 and 1995 (Finkenzeller, 2003). A contactless smart card works in close proximity to a reader. It is thus suitable for applications where masses flow, such as, a high traffic channel. Contactless smart cards are for that reason widely used in public transport ticketing, allowing a quicker, smoother commuter flow through train station barriers or buses doors (Finkenzeller, 2003). The above section shows that AIDC has evolved into a well-received technology for use in electronic payment, access control, production, and distribution. Barcodes and RFID are the two AIDC technologies utilised in supply chains where products are being identified at different stages. The rest of the AIDC technologies are primarily used in security, banking, and public transportation domains. Figure 3 shows the development of AIDC in a time chart.

rfId systeM A RFID system is made up of two main hardware components: tags and readers (Grasso, 2004). The tags or transponders consist of a memory

Figure 3. The development of AIDC

1930s – 1940s Emergence of barcode, magnetic stripes, & RFID.

1949 Barcode patented.

70

1970 Magnetic stripes standard established.

1988 2D Barcodes introduced.

1973 UPC adopted.

1960s Barcode used in railroad system. OCR developed. Magnetic stripes & MICR adopted by the American Banking Association.

1999 2000s EPC developed. RFID for Supply Chain Real time locating 1992 - 1995 systems Contactless card (RTLS). standard developed.

1984 Smart card first used in pre-paid mobile phone.

1995 600 millions of Smart card issued.

2003 Voice Inventory Management System prototype developed.

Radio Frequency Identification History and Development

chip and have a built-in antenna. The memory, depending on its size, can store up to 64K of data. The antenna receives and transmits data using radio waves. There are three basic forms of tag: passive, active, and hybrid or semi-passive. A passive tag does not have an internal power source to process nor transmit signals. An active tag has an integrated battery as the power source. An active tag can broadcast signals and transmit at a longer range than a passive tag. In contrast, a passive tag is only operational when it receives RF signals from an authenticated reader or source. The tag uses the RF as a source of power to transmit data back to the reader, a process called inductive coupling (Weinstein, 2005). A semi-passive tag has an on-board power source and yet behaves like a passive tag. It has a switch that turns on the internal power source when it receives RF signals from a reader. A semi-passive tag overcomes the short range limitation of a passive tag and the complexity of an active tag response method (Jones et al., 2006). Tags can be read only, write once and read many times, or read and write many times. There are six classes of tags: Class 0 to Class 5. A Class 0 tag is a factory programmed read-only passive

tag. Once programmed, the data in the tag cannot be altered. A Class 1 tag is similar to a Class 0 tag except that it can be programmed by the user. It contains minimum features to keep the cost low. A Class 2 tag is a read-write passive tag with a longer communication range than a Class 1 tag. It has extended memory and authenticated access control features not available in Class 1 tags. A Class 3 tag is a semi-passive read-write tag. It has an on-board power source and thus has a longer communication range and higher transmission reliability than Class 2 tags. A Class 4 tag is an active ad-hoc read-write tag with the functionalities of a Class 3 tag. It is capable of communicating with other Class 4 tags within range of its ad-hoc network. A Class 5 tag is an autonomous active read-write reader tag. It has the features of a Class 4 tag and is capable of communicating with all classes within its subsets. As you see, each successive class “is a superset of the functionality contained within, the previous class, resulting in a layered functional classification structure” (Engels & Sarma, 2005, p. 3). Figure 4 shows the layered classification structure of tags. Class 0 is not shown in the figure as it could be classified as Class 1 due to their similar features. Class 1 is

Figure 4. Auto-ID center RFID class structure – layered hierarchy

Reader (Class 5) Active Ad Hoc (Class 4) Semi-Passive (Class 3) Higher Functionality (Class 2) Identity (Class 1) Source: ©2005 Engels & Sarma. Reproduced with Permission.

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Radio Frequency Identification History and Development

Table 1. Frequency Allocation for RFID in the UHF Spectrum Country

Frequency (MHz)

Technique

Regulator Website

Australia

920 to 926

NA

www.acma.govt.au

Brazil

902 to 907.5

FHSS

www.anatel.gov.br

China

840.5 to 844.5 920.5 to 924.5

FHSS

www.mii.gov.cn

Finland

865.6 to 867.6

LBT

www.ficora.fi

France

865.6 to 867.6

LBT

www.arcep.fr

Germany

865.6 to 867.6

LBT

www.bundesnetzagentur.de

Hong Kong

865 to 868 920 to 925

NA

NA

India

865 to 867

NA

www.trai.gov.in

Italy

865.6 to 867.6

LBT

www.agcom.it

Japan

952 to 954

LBT

www.soumu.go.jp

Korea, Rep.

908.5 to 910 910 to 914

LBT FHSS

www.kcc.go.kr

Malaysia

866 to 869

NA

www.cmc.gov.my

New Zealand

864 to 868

NA

www.med.govt.nz

Singapore

866 to 869 920 to 925

NA

www.ida.gov.sg

South Africa

865.5 to 867.6 917 to 921

LBT FHSS

www.icasa.org.za

Spain

865.6 to 867.6

LBT

www.mityc.es

Sweden

865.6 to 867.6

LBT

www.pts.se

Switzerland

865.6 to 867.6

LBT

www.bakom.ch

Taiwan

922 to 928

FHSS

NA

Thailand

920 to 925

FHSS

www.ntc.or.th

Turkey

865.6 to 867.6

LBT

www.tk.gov.tr

United Kingdom

865.6 to 867.6

LBT

www.ofcom.org.uk

United States

902 to 928

FHSS

www.fcc.gov

Vietnam

866 to 869

NA

www.mpt.gov.vn

Source: ©2007 Barthel. (FHSS – Frequency Hopping Spread Spectrum, LBT – Listen Before Talk). Reproduced with Permission.

established as the foundation of the RFID class structure (Engels & Sarma, 2005). Besides the classes of tag, the use of a tag is controlled by the radio frequency spectrum. RFID utilises the Industrial, Scientific, and Medical (ISM) band available worldwide. There are four

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categories of spectra available for commercial use: 125 to 134 KHz in the low frequency category, 13.56 MHz in the high frequency category, 433 MHz and 868 to 928 MHz in the ultra high frequency (UHF) category, and 2.45 GHz in the microwave category (Walker, 2003). There may

Radio Frequency Identification History and Development

be some variation in the classification of spectra due to the different regulatory on the use of ISM band in different parts of the world. In the effort to ensure global interoperability of RFID tags for global roaming applications, EPCglobal has been advocating the use of RFID in the 860 to 960 MHz spectrum. This is the spectrum used in the EPC Class 1 Generation 2 tags that aims at covering a wide group of countries that can use the same tags. As of September 2007, there are a total of 54 countries with regulations in place for the use of RFID within the 860 to 960 MHz spectrum (Barthel, 2007). These countries represent about 92 per cent of the world gross national income. Table 1 shows the allocated frequencies for RFID use in the UHF spectrum by countries. The other component of a RFID system is the reader. A reader sends and receives RF signals. It may be portable or fixed in a position and is linked to a computer. In a proprietary system, readers usually read only proprietary tags. The readers and tags must be programmed within the same range of a spectrum to communicate. Thus, one of the biggest challenges is harmonising the frequency for RFID use in the UHF spectrum, particularly, in Europe where available UHF spectrum is limited (Wasserman, 2007). For example, France, Italy, Spain, and Turkey were using the UHF spectrum for military equipment before the ratification of RFID use in the UHF spectrum (Barthel, 2006). The UHF spectra shown in Table 1 are the approved frequency slots for RFID use in the respective countries. This means that a RFID reader has to be tuned to the approved frequency slots for operation in that country. Therefore, the EPC Class 1 Generation 2 tags can roam internationally among these countries thus allowing a worldwide supply chain visibility in these countries. A reader broadcast its signal within a specific spectrum depending on its power and frequency. The distance a tag and a reader can transmit is relative to the size of the antenna. In a samefrequency band, the larger the antenna, the longer

the transmission range is. The orientation or shape of the antenna is equally important in its role of picking up electromagnetic signals, particularly, when the tag is used on a material that attenuates the electromagnetic signals. Thus the “surface area and the shape of the tag antenna have to be optimised for not only backscattering the modulated electromagnetic wave but also harvesting energy for the microchip to function” (Ukkonen, Schaffrath, Kataja, Sydanheimo, & Kivikoski, 2006, p. 111). There are now many shape and sizes of antennas designed for use on different materials and environments. There are also various transmission methods. The frequency hopping spread spectrum (FHSS) method switches channels at a sequence for a more reliable transmission. The FHSS method allows the efficient use of the bandwidth. The other method used mostly in Europe is the listen before talk (LBT) method. In the LBT, a reader has to listen for other transmitters using the same channel before communicating with the tags through an unused channel (Eeden, 2004). This method is derived due to the restriction on the amount of energy emission in Europe set by the European Telecommunications Standards Institute (ETSI). A LBT reader is allowed to transmit signals for a period of four seconds and then stop the transmission for a least 0.1 second (Anonymous, 2007b; Roberti, 2004). The disadvantage of the LBT method is the slower data transfer rate, which is about thirty percent of the FHSS data rate (Roberti, 2004).

barcodes and rfId RFID is generally thought of as a replacement for barcodes (Atkinson, 2004; Lazar & Moss, 2005; Sheffi, 2004). Barcodes have been around since 1949 when they were first patented. It took almost thirty to forty years for barcodes to gain wide adoption. This is evident in the late 1980s to early 1990s when there were numerous articles on

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Radio Frequency Identification History and Development

the application and implementation of barcodes; Walter (1988), Carter (1991), Lacharite (1991), Ekman (1992), and Burkett (1993) are examples of barcodes application in the various industries, to name a few. By the 2000s, the barcode is already an established technology. This is also evident in articles claiming barcode is still “alive” amidst the emergence of RFID (Katz, 2006) and proclaiming success stories of barcode implementations (Heinen, Coyle, & Hamilton, 2003; May, 2003). The announcement by the Food and Drug Administration (FDA) in the United States about the use of barcodes for the labeling of medications further strengthen the barcode’s position in the industry (Heinen, Coyle, & Hamilton, 2003). A recent survey by Venture Development Corporation shows that the demand for barcode scanners is strong (Mason, 2005). Therefore the general preconception of RFID replacing barcode needs to be refined. It is important to understand the difference between RFID and barcodes in order to

successfully implement a RFID system especially in a barcode dominant environment. Table adapted from RFID Handbook – Fundamentals and Applications in Contactless Smart Cards and Identification (2nd Ed.), Finkenzeller (2003). Copyright John Wiley & Sons Limited. Reproduced with Permission. Undoubtedly, RFID has far more capability offering more advantages than barcodes. Table 2 shows the differences between the two technologies. Barcode readers use optical technology to capture the patterns of a barcode label. It therefore requires a line of sight within a short distance to read the label. This inevitably calls for the need to locate a barcode label by either pointing a scanner directly at the label or by positioning the label such that it can be read by a fixed scanner. Either way involves labour. By contrast, RFID uses RF or electromagnetic waves as a means of data collection. RF works in omni-direction ally up to a few yards. This attribute enables a

Table 2. RFID and barcode comparison System parameters

Barcode

RFID

Typical data quantity (bytes)

1-100

16-64k

Content

Specific (SKU level)

Dynamic

Machine readability

Good

Good

Readability of people

Limited

Impossible

Line of sight requirement

Yes

No

Influence of dirt

Very high

No influence

Influence of covering

Total failure

No influence

Influence of direction and position

Low

No influence

Influence of metal and liquid

Very low

High

Degradation/wear

Limited

No influence

Reading speed

Low (one label at a time)

Very fast (multiple tag)

Reading distance

0-50cm

0-5m (microwave)

Cost of label/tag

Inexpensive

Expensive

Standards

Defined

Being defined

Stage of maturity

Mature

Evolving

Table adapted from RFID Handbook - Fundamentals and Applications in Contactless Smart Cards and Identification (2nd Ed.), Finkenzeller (2003). Copyright John Wiley & Sons Limited. Reproduced with permission.

74

Radio Frequency Identification History and Development

RFID reader to communicate with a tag without the need to be in the line of sight, which has an added advantage of having a reader reading multiple tags simultaneously. This feature of RFID presents the reality of connecting the real world “with its representation in information systems” (Strassner & Schoch, 2002, p. 1). Strassner & Schoch (2002) suggest that the media break, a break between physical and its information, can be avoided with automation, awareness of smart objects, and mobility. Figure 5 shows the progress towards the convergence with the use of RFID (Fleisch, 2001). The capability of reading multiple tags at a given time increases the throughput to a level that barcode cannot achieve. This also eases the bottleneck of scanning each item at a time with a faster reading speed. However, this capability has its downside. The fact that, in a RFID system, tags within range are read almost simultaneously also means that the exact sequence of the cartons is not picked up by the reader (Bednarz, 2004). In a conveyor setup, cartons are often required to be routed to different locations. It is therefore

important to know the order of the cartons in order to direct them to the right locations. Barcode systems have been successful in such scenario. A “bar-code-based system knows more about the order of packages moving along a belt” (Bednarz, 2004, p. 8). An advantage of RF is the ability to communicate in a harsh environment where the barcode label is worn or covered by dust. RF is able to penetrate most types of material. However, the signal is vulnerable to metal, liquid or material with high moisture content, particularly the high frequency range. More energy or power is needed to mitigate the loss of RF propagation through those materials. There are on-going projects to overcome this drawback (Collins, 2004). At a mature stage of development, barcode is relatively inexpensive to implement for a costeffective solution (Anonymous, 1993; Ekman, 1992). A record storage warehouse of 30,000 sq ft uses only two computers and two wand scanners to keep track of thousands of boxes of record files (Anonymous, 1993). Conversely, RFID technology is evolving steadily particularly in the supply

Figure 5. Avoidance of media breaks

Source: ©2001 Fleisch. Reproduced with Permission.

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chain industry. Setting up an RFID infrastructure is expensive and hence requires careful study on systems integration with existing business information systems. The RFID tags make up the main outlay. Each tag costs US$5 in early 2000 down to between US$1 to 50 cents in 2004 as reported by Atkinson (2004). The cost of a tag continues to drop and each tag is expected to cost no more than five cents per tag for mass adoption to take place. In 2006, the five cents per tag benchmark has been achieved at an order of 100 million pieces (Roberti, 2006). The self-regulating market forces necessitate that should prices fall to five cents a tag, the demand for tags will increase. This in turn may create a supply issue with production capacity lacking behind demand (Sarma, 2001). It may push prices back up, thus dwindling the prospect of an earlier mass adoption of RFID technology. Another drawback of RFID is the lack of a harmonised standard across this system. Barcode has clearly defined standards and the different standards are globally accepted. In terms of a unique numbering system for item identification, EPC is one of the standards for RFID. The use of RF is posing a challenge to a globally accepted standard. This is largely due to the different frequency allocation by the local governments. At present, different standards are adopted across different RFID systems complying with the local regulations. Manufacturers, importers and businesses are concerned that their products cannot be tracked across countries’ borders because of different regulations. This would warrant them to use different systems to cater for customers in different parts of the world (Atkinson, 2004). The 13.56 MHz spectrum is commonly used for RFID applications such as proximity access cards to premises, and smart cards. The 433 MHz spectrum is commonly reserved for supply chain use in most countries making it a suitable candidate for global supply chains. It was also considered the industry standard for supply chains (Li, Visich, Khumawala, & Zhang, 2006). The

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China State Radio Regulation Committee on 9th November 2006 approved the use of 433 MHz for RFID devices compatible with the ISO standard. This seems to solidify the 433 MHz spectrum as an international standard (Swedberg, 2006). New Zealand has also assigned the 433 MHz spectrum for RFID and other short range devices. Unfortunately, frequencies in this spectrum do not work reliably under supply chain conditions due to the short wavelength of about one metre (Anonymous, 2004). Thus a passive tag at 433 MHz might not adequately achieve the reading accuracy. The United States Department of Defense (DOD) has tested the interference of 433 MHz active RFID system and maintained there is no interference between the RFID system and radar equipments that they are aware of. However, the DOD is taking precaution not to deploy the RFID systems within forty kilometres of any military radar system (Collins, 2005). EPCglobal on the one hand has ratified various standards for RFID operations. It has ratified the Generation 1 tag in 13.56 MHz, 860 to 930 MHz, and 900 MHz. The latter is factory programmed tag or Class 0 while the others are writeable or Class 1. A notable improved standard of EPCglobal is the Class 1 Generation 2 UHF tags (Wessel, 2007). This standard allows interoperability across the 860 to 960 MHz spectrum making this standard more scalable and raising its tolerance to interference in a dense RF environment. The United States has adopted the 915 MHz frequency as the national standard for passive RFID systems (Porter, Billo, & Mickle, 2006) which is within the EPCglobal defined range for UHF tags. The Australian Communications and Media Authority has issued a license to GS1 for the use of 920 to 926 MHz in Australia. The New Zealand Government has allocated two short range device slots in the 902 to 928 MHz range for RFID experiments. Besides assigning the 864 to 868 MHz spectrum for RFID, the New Zealand Government has plans to freeze any further issuance of licenses in the band of 915 to 921 MHz to cater for future

Radio Frequency Identification History and Development

RFID uses (Anonymous, 2007c). The New Zealand Government is adopting generic frequency standards across the ISM spectrum as a strategy for keeping the use of RFID devices and licensing open. The Government is actively discussing and harmonising the frequency arrangements with key trading partners such as the Australia, Europe, and the United States. Such arrangement will make RF equipments easily available without incurring additional cost for modifying the equipment according to local standards. The European Commission on the other hand has planned to adopt the ultra-wide band frequency range of 3.4 to 4.8 GHz and 6 to 8.5 GHz among the European countries (Swedberg, 2007). Privacy is yet another issue with the use of RFID. While standards and costs are primarily technological, privacy is believed to be an educational one (Twist, 2005). RFID is just another data collection tool for keeping track of products. Sarma (2005) highlights that the electronic toll collection, a form of RFID applications with longer read range than the cheaper RFID/EPC tag, is already in use in many countries. The information in a RFID/EPC tag is encoded and does not contain information about the consumers. It adopts the Internet technology where the information in the tag is used as an address to more information about the content. The access to such further information is secured and authenticated. Another similar application widely used is the credit card.

conclusIon RFID has been in commercial use since the 1970s. As an AIDC technology using wireless communication, RFID enables simultaneous collection of objects data with no or little human intervention. Thus RFID is an important technology from the point of view of ubiquitous computing. RFID is also an emerging technology that performs better than barcodes in most aspects. Of note, RFID’s

unique characteristics offer more utilities than barcodes. An example is the tracking of items without the need for line of sight. Tagging at item level enhances supply chain visibility and security. Manufacturers and retailers can have tighter control over their products from unwanted spoilage to out-of-stock with more accurate and timely data capturing. Although efforts to improve the performance are evident, the impediment to RFID implementation is the lack of standards, the relatively high cost of tag per unit, and privacy (ABIResearch, 2006; C. B. Soon & J. Gutierrez, 2008; Vijayaraman & Osyk, 2006). By far, these are the three main hurdles, among a number of secondary obstacles such as frequency interference, which need to be resolved before the technology can be widely adopted. Unless solutions are found for the three hurdles, barcode is still the cheaper and easier option although the benefits are not as extensive as what RFID can yield. With this stance, it is not to say that RFID will totally replace barcode, at least not in the next twenty to thirty years until a RFID tag is possibly as cheap as a barcode label and when the technology has matured. This projection is in line with what Bhuptani (2005) has noted, namely, that a new technology typically takes twenty to thirty years to become commercialised and forty to fifty years to become fully mature. And so accordingly, RFID has just reached the commercial stage since its inception in the supply chain and thus has another twenty to thirty years before reaching its maturity. Blau (2006) suggests that it will take fifteen years before RFID replaces barcodes and be used to identify products at item level. RFID and barcodes may in fact be complementary to each other having merits in their respective applications. Woolworths has tested out the tracking of dollies and items using both RFID and barcode systems (Alexandra, 2003). It overcame the cost factor of tagging each item with RFID tags by utilising a barcode system and tagging only the dollies that hold the items with RFID tags. Woolworths reported full item-level

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visibility without the cost of item-level tagging. From a worst case scenario perspective, Jilovec (2004) suggests barcode as a backup for RFID in case of failure. This is sensible since most companies would already have implemented barcode systems. The use of RFID at the UHF 860 to 960 MHz spectrums is now an internationally accepted standard. With the ratification of this standard, there will be more hardware available for the uptake of RFID in supply chains. The remaining challenges would be to enhance the data transfer rate, enable communication in a dense environment, and educate users on privacy concerns. As the technology matures, the investment and operating costs should gradually reduce. RFID is becoming widespread not only in the supply chain, but also in other industries. The advancement of the technology has introduced RFID to many fields as innovators continue to explore the characteristics of RFID. It will continue to find innovation in the field of real time locating system, positioning system, product authentication, and in the sport arena. Like barcodes, RFID is an enabling tool for data capturing and identification. RFID is thus a smart technology that enables the convergence of information with its speedy event’s data capture. It closes up the gap between the physical world and its representation in information systems. The next phase in the field of RFID is to explore the adoption and diffusion of the technology as a business case.

references ABIResearch. (2006). RFID End-User Survey. ABIResearch. Retrieved November 13, 2007, from http://www.abiresearch.com Alexandra, D. (2003). Woolworths counts on RFID for security’s sake. Logistics Management, 42(9), 61.

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Allen, L. G. (1991). Automatic Identification: How Do You Choose It & Where Do You Use It? Automation, 38(7), 30-33. Anonymous. (1993). Bar code technology increases efficiency for off-site records storage firm. Managing Office Technology, 38(11), 65-67. Anonymous. (2004). Active RFID System Frequencies. Retrieved December 21, 2007, from http://www.idtechex.com Anonymous. (2006). How do 2D barcodes work? Who, What, Why? Retrieved April 20, 2008, from http://news.bbc.co.uk/ Anonymous. (2007a). Automatic Identification and Data Capture Technologies - An overview. Retrieved January, 29, 2007, from http://www. aimglobal.org/technologies/aidc_overview.asp Anonymous. (2007b). Electromagnetic compatibility and Radio spectrum Matters (ERM): European Telecommunications Standards Institution. Anonymous. (2007c). An Engineering Discussion Paper on Spectrum Allocations for Short Range Devices: New Zealand Ministry of Economic Development. Atkinson, W. (2004). Web-Based RFID: Hype or Glimpse of the Future? Apparel, 45(6), 24-28. Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building user location and tracking system. Paper presented at the Proceedings of the IEEE Infocom 2000. Barthel, H. (2006). Regulatory Status for Using RFID in the UHF Spectrum. Brussels: GS1. Barthel, H. (2007). Regulatory Status for Using RFID in the UHF Spectrum. Brussels: GS1. Bednarz, A. (2004). RFID joins wireless lineup at UPS. Network World, 21(38), 8.

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Bhuptani, M., & Moradpour, S. (2005). RFID Field Guide - Deploying Radio Frequency Identification Systems. NJ: Prentice Hall.

Grasso, J. (2004). The EPCglobal Network. EPCglobal Retrieved December 21, 2007, from http://www.epcglobalus.org

Blau, J. (2006). RFID on all goods is 15 years away, says Metro. Computerworld Retrieved November 16, 2006, from http://www.computerworld.co.nz

Heinen, M. G., Coyle, G. A., & Hamilton, A. V. (2003). Barcoding makes its mark on daily practice. Nursing Management, Oct 2003, 18-20.

Burkett, T. (1993). Bar code implementation. Quality, 32(3), 28. Carter, J. R., & Ragatz, G. L. (1991). Supplier Bar Codes: Closing the EDI Loop. International Journal of Purchasing and Materials Management, 27(3), 19. Collins, J. (2004). New Two-Frequency RFID System. RFIDJournal. Retrieved April 2, 2007, from http://www.rfidjournal.com Collins, J. (2005). Test Detect RFID-Radar Interference. Retrieved December 21, 2007, from http://www.rfidjournal.com Edwards, J. (2008). RFID Is a Winner in the Sports Arena. RFIDJournal. Retrieved April 26, 2008, from http://www.rfidjournal.com Eeden, H. v. (2004). Europe Needs New RFID Regulations. RFIDJournal Retrieved December 21, 2007, from http://www.rfidjournal.com Ekman, S. (1992). Bar Coding Fixed Asset Inventories. Management Accounting, 74(6), 58. Engels, D. W., & Sarma, S. E. (2005). Standardization Requirements within the RFID Class Structure Framework. MA: Auto-ID Labs. Finkenzeller, K. (2003). RFID Handbook - Fundamentals and Applications in Contactless Smart Cards and Identification (2nd ed.). Chichester: Wiley. Fleisch, E. (2001). Business Perspectives on Ubiquitous Computing (M-Lab Working Paper No. 4). St Gallen: University of St Gallen.

Hightower, J., Vakili, C., Borriello, G., & Want, R. (2001). Design and Calibration of the SpotON Ad-Hoc Location Sensing System. University of Washington Retrieved April 26, 2008, from http:// seattle.intel-research.net/people/jhightower// pubs/hightower2001design/hightower2001design.pdf Jilovec, N. (2004). EDI, UCCnet & RFID - Synchronizing the Supply Chain. Colorado: 29th Street Press. Jones, A. K., Dontharaju, S., Tung, S., Hawrylak, P. J., Mats, L., Hoare, R., et al. (2006). Passive active radio frequency identification tags. International journal of Radio Frequency Technology and Applications, 1(1), 52-73. Katz, J. (2006). Bar Codes: Alive and Well. Industry Week, 255(7), 14. Kondratova, I. (2003). Voice and multimodal access to AEC project information. Paper presented at the The 10th ISPE International Conference on Concurrent Engineering: The Vision for Future Generations in Research and Applications, Portugal. Lacharite, R. (1991). Rethinking Bar Coding: Turning Preconceptions into System Tools. ARMA Records Management Quarterly, 25(2), 3. Landt, J. (2001). Shrouds of Time. The history of RFID Retrieved 19 January, 2006, from http:// www.aimglobal.org/technologies/rfid/resources/ shrouds_of_time.pdf

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Lazar, L. D., & Moss, H. K. (2005). Radio Frequency Identification Technology: An Introduction. Paper presented at the Proceedings of the 2005 Southern Association for Information Systems Conference, Savannah. Li, S., Visich, J. K., Khumawala, B. M., & Zhang, C. (2006). Radio frequency identification technology: applications, technical challenges and strategies. Sensor Review, 26(3). Man, M. (2007). All About 2D Bar Codes. Socket Communications Technology Brief. Retrieved April 20, 2008, from http://www.socketmobile. com Mason, B. (2005). Bar Code Scanner Demand Remains Strong (Press Release). Massachusetts: Venture Development Corporation. May, E. L. (2003). The case for bar coding: Better information, better care and better business. Healthcare Executive, 18(5), 8-13.

Sarma, S. (2001). Towards the 5-cent Tag. MA: Auto-ID Labs. Sarma, S. (2005). A History of the EPC. In S. Garfinkel & B. Rosenberg (Eds.), RFID Applications, Security, and Privacy (pp. 37-55). NJ: Addison-Wesley. Sheffi, Y. (2004). RFID and the Innovation Cycle. The International Journal of Logistics Management, 15(1). Shepard, S. (2005). Radio Frequency Identification. NY: McGraw-Hill. Smucker, T. (2006). Making the GS1 Vision a Reality (Annual Report). Brussels: GS1. Soon, C. B., & Gutierrez, J. (2008, May 18-20). Where is New Zealand at with Radio Frequency Identification in the Supply Chain? - A Survey Result. Paper presented at the Proceedings of 2008 International Conference on Information Resources Management, Niagara Falls, Canada.

Mullen, D., & Moore, B. (2005). Automatic Identification and Data Collection: What the future holds. In S. Garfinkel & B. Rosenberg (Eds.), RFID Applications, Security, and Privacy (pp. 3-13). NJ: Addison-Wesley.

Soon, C. B., & Gutierrez, J. A. (2008). Effects of the RFID Mandate on Supply Chain Management. Journal of Theoretical and Applied Electronic Commerce Research, 3(1), 81-91.

Ni, L. M., Liu, Y. H., Lau, Y. C., & Patil, A. P. (2004). LANDMARC: Indoor Location Sensing Using Active RFID. Wireless Networks, 10, 701-710.

Strassner, M., & Schoch, T. (2002). Today’s Impact of Ubiquitous Computing on Business Processes. Paper presented at the First International Conference on Pervasive Computing, Zurich, Switzerland.

Porter, J. D., Billo, R. E., & Mickle, M. H. (2006). Effect of active interference on the performance of radio frequency identification systems. International journal of Radio Frequency Technology and Applications, 1(1). Roberti, M. (2004). New ETSI RFID Rules Move Forward. RFIDJournal. Retrieved December 21, 2007, from http://www.rfidjournal.com Roberti, M. (2006). SmartCode Offers 5-Cent EPC Tags. RFIDJournal. Retrieved April 26, 2008, from http://www.rfidjournal.com

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Swedberg, C. (2006). China Endorses ISO 18000-7 433 MHz Standard. RFIDJournal Retrieved July 29, 2007, from http://www.rfidjournal.com/ Swedberg, C. (2007). EC Spectrum Decision Expected to Boost UWB RFID Adoption. RFIDJournal. Retrieved December 29, 2007, from http://www.rfidjournal.com Swedberg, C. (2008a). RFID Fuels Gas Tank Sercurity. RFIDJournal. Retrieved April 26, 2008, from http://www.rfidjournal.com

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Swedberg, C. (2008b). U.S. FDA Seeks Research for Medical Device Tracking System. RFIDJournal. Retrieved April 26, 2008, from http://www. rfidjournal.com Twist, D. C. (2005). The impact of radio frequency identification on supply chain facilities. Journal of Facilities Management, 3(3), 226-239. Ukkonen, L., Schaffrath, M., Kataja, J., Sydanheimo, L., & Kivikoski, M. (2006). Evolutionary RFID tag antenna design for paper industry applications. International journal of Radio Frequency Technology and Applications, 1(1), 107-122. Vijayaraman, B. S., & Osyk, B. A. (2006). An empirical study of RFID implementation in the warehousing industry. The International Journal of Logistics Management, 17(1), 6-20. Violino, B. (2006). APL Reaps Double Benefits From Real-Time Visibility [Electronic Version]. RFIDJournal. Retrieved December 28, 2006 from http://www.rfidjournal.com. Walker, J. (2003). What You Need To Know About RFID in 2004. Forrester Research. Retrieved December 20, 2003, from http://www. forrester.com

key terMs AIDC: Automatic Identification and Data Capture. EPC: Electronic Product Code. EPCglobal: An international subscriberdriven organization aimed at enhancing RFID standards. GS1: Former Uniform Code Council (UCC) and European Article Numbering (EAN) International. ISM: Radio bands available worldwide reserved for use in the Industrial, Scientific, and Medical fields. ISM bands range from 6.765 MHz to 246 GHz. RF: Radio Frequency. RFID: Radio Frequency Identification. Supply Chain Management: The managing of all movements of products and materials from source to point of consumption including storage.

Walter, E. J. (1988). Bar Code Boom Extending thru Industry. Purchasing World, 32(2), 39.

Tag: A transponder with built-in memory chip and antenna encoded with an identifier. It can be passive (without battery) or active (with battery).

Wasserman, E. (2007). Europe Embraces EPC Slowly. RFIDJournal. Retrieved December 21, 2007, from http://www.rfidjournal.com

endnote

Weinstein, R. (2005). RFID: A Technical Overview and Its Application to the Enterprise. IT Professional Magazine, 7(3), 27-33.

1

Uniform Code Council and EAN International merged to form GS1.

Wessel, R. (2007). European EPC Competence Center Releases UHF Tag Study. RFIDJournal. Retrieved July 16, 2007, from http://www.rfidjournal.com This work was previously published in Auto-Identification and Ubiquitous Computing Applications: RFID and Smart Technologies for Information Convergence, edited by J. Symonds, J. Ayoade & D. Parry, pp. 1-17, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 1.8

Automated Data Capture Technologies: RFID

Vidyasagar Potdar Curtin University of Technology, Australia Chen Wu Curtin University of Technology, Australia Elizabeth Chang Curtin University of Technology, Australia

abstract In this chapter we provide an introduction to RFID technology. We discuss the main components of the RFID technology, which includes RFID transponders, RFID readers, RFID middleware, and RFID labels. A detailed classification and explanation for each of these components is provided, followed by the benefits and applications that can be achieved by adopting this technology. After discussing all possible applications, we describe

the business benefits and how stakeholders can benefit. This is followed by a detailed outline of the adoption challenges, where we discuss issues like the security, privacy, cost, scalability, resilience, and deployment and some existing solutions. Once the issues are discussed, we divert our attention to some successful RFID deployment case studies to describe the adoption of RFID technology that has already begun and how many big organizations across the world are showing interest in this technology. Since this chapter takes into consideration

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Automated Data Capture Technologies

a variety of audiences like researchers, business executives, business consultants, hobbyists, and general readers, we tried to cover material relevant to each target audience. For business executives and consultants interested in knowing who can offer complete RFID solutions, we allocated a dedicated section for RFID vendors where we provide a comprehensive list of RFID vendors across the globe. For researchers, we listed some open issues in the section of adoption challenges. For advanced users, in-depth technical details are provided in the section where we discuss security and privacy enhancing protocols.

IntroductIon Automated data capture is an important aspect in supply chain management and logistics. In the last decade, automated identification and data capture (AIDC) has revolutionized the overall supply chain management process. AIDC includes technology to identify objects, and automatically collects data about them and updates the data into software systems without human intervention. Some examples of AIDC technologies include bar codes, RFID, smart cards, voice and facial recognition, and so forth. An automated inventory control systems (AICS), which forms the backbone of the modern supply chain, is a software application used in a warehouse to monitor the quantity, location, and status of inventory. Modern AICS heavily relies upon barcodes, for automated data capture. A barcode basically is a machine-readable visual representation of information printed on the surface of objects. There are several different kinds of barcodes, for example, barcodes which store data in the widths and spacing of printed parallel lines, and those that store data within the patterns of dots, or concentric circles, or even hidden within images. This encoded data on the barcodes is read by barcode readers, which up-

date the backend ERP, SCM, or WMS systems. However there are some inherent issues with using a barcode, for instance, barcodes become ineffective in rain, fog, snow, dirt and grime, and so forth (Tecstra, n.d.). Since barcodes rely on optical sensors, any minor change on the barcode print can make it difficult to read. This can be commonly seen at point of sale (POS) in the supermarkets, where the POS operator scans the barcode several times because it is either wet or not aligned properly. To overcome these issues the industry is now looking at the possibility of using new generation AIDC technology like the RFID. A radio frequency identifier (RFID) system is basically composed of an RFID transponder (tag) and an RFID interrogator (reader). The RFID transponder or the RFID tag (which is how it is often called) is a microchip connected to an antenna. This tag can be attached to an object, which needs to be uniquely identified, for example, it can be used in a warehouse to track the entry and exit of goods. This tag contains information similar to the barcode, which stores the unique properties of the object to which it is attached. An RFID reader can access this information. The RFID reader communicates with the RFID tag using radio waves. The radio waves activate the RFID tag to broadcast the information it contains. Depending on the type of tag used, the information transmitted could be merely a number or detailed profile of the object. The data fetched from the reader can then be integrated with the backend ERP or SCM or WMS systems (Tecstra, n.d.). There are two fundamental differences between the conventional barcodes and the contemporary RFIDs. First, RFIDs do not require line of sightthat is, objects tagged with RFID can be sensed in a wide area, and there is no need to individually scan all the objects in front of an optical scanner. Second, RFIDs offer item-level taggingthat is, each item within a product range can be uniquely identified (e.g., “109839 is a bottle

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of orange juice manufactured by ABC Company”). However barcodes do not identify individual items; they can only identify that “this is a bottle of orange juice manufactured by ABC Company.” Some other points could also be considered. RFID has a longer read range compared to barcodes. The amount of data stored on barcodes is limited and often cannot be updated once it is printed. In comparison, RFID tags offer a considerably large amount of data storage capacity as well as reprogramming capabilities, which means the data on the tag can be updated effortlessly. These changes can be done without physically identifying the tag because the RFID reader can uniquely query the desired RFID tag and make the changes. From the security perspective RFID tags can be placed inside the objects, however barcodes have to be printed outside. Another issue with barcodes is the printing quality; substandard printing can result in reading errors (Gloeckler, n.d.; Tecstra, n.d.). There are several inherent advantages with RFID technology; however to achieve widespread adoption of RFID, issues like security, privacy, and cost should be addressed first. In this chapter we provide a detailed insight into RFID. The rest of the chapter is organized in the following manner. The next section introduces the RFID technology. We discuss RFID tags, RFID readers, and RFID middleware. We then discuss the benefits that stakeholders can achieve by adopting RFID technology and provide an RFID Deployment Roadmap. In this section we also describe the steps that an organization should consider prior to adopting RFID to streamline its business process. The fifth section discusses the RFID adoption challenges where we outline the existing issues, which are the major obstacles in widespread adoption. These issues are security, privacy, and cost. Finally, before concluding the chapter, we discuss three case studies in the supply chain domain that have adopted RFID to enhance their business process and gain a competitive advantage.

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The main objectives of this chapter are: 1.

To provide RFID novices with an introductory illustration of the contemporary data capture technologyRFID. This is covered in the next section of this chapter. 2. To offer RFID advanced users a comprehensive survey of RFID technology from both technical and business perspectives. This is covered in detail in the following sub-sections where we give an in-depth explanation of the different types of RFID tags, RFID readers, and RFID middleware. 3 . To illustrate the well-identified RFID adoption challenges and their corresponding deployment strategies for business consultants. This is covered later in the chapter, where we begin by highlighting the main challenges for RFID adoption which include cost, security, and privacy. We then provide a road map for RFID adoption where we provide a detailed deployment strategy for RFID adoption in a business environment. This can facilitate consultants, CEOs, and CIOs to make informed decisions. 4. To explain benefits of adopting RFID solutions to business executives using real-world proven case studies. We do this by highlighting the benefits that RFID offers to different stakeholders, and later in the chapter relating that to five successful RFID deployment case studies from supply chain domains. All these case studies provide an insight into how RFIDs can facilitate businesses across multiple domains.

technology overvIeW An RFID system is composed of three main elements: an RFID tag (inlay), which contains data that uniquely identifies an object; an RFID reader, which writes this unique data on the tags and,

Automated Data Capture Technologies

Figure 1. RFID architecture (Source: Potdar, Wu, & Chang, 2005)

when requested, can read this unique identifier; and an RFID middleware, which processes the data acquired from the reader and then updates it to the backend database or ERP systems (Weis, Sarma, Rivest, & Engels, 2004). A typical RFID system is shown in Figure 1. When the RFID tag comes in the range of the RFID reader, the reader activates the tag to transmit its unique information. This unique information is propagated to the RFID middleware, which appropriately processes the gathered information and then updates the backend database.

once, and read-many. Finally, the tags can also be classified based on the frequency in which they operateLF, HF or UHF.

Active vs. Semi-Active (or Semi-Passive) vs. Passive RFID Tags •

rfId tags An RFID tag is a microchip attached with an antenna to a product that needs to be tracked. The tag picks up signals from the reader and reflects back the information to the reader. The tag usually contains a unique serial number, which may represent information, such as a customer’s name, address, and so forth (RFID Journal, 2006a). A detailed classification is discussed next.

Classification RFID tags can be classified using three schemes. First, the tags can be classified based on their ability to perform radio communicationactive, semi-active (semi-passive), and passive tags. Second, the tags can be classified based upon their memoryread-only, read-write or write-

•

Active tags have a battery that provides necessary energy to the microchip for transmitting a radio signal to the reader. These tags generate the RF energy and apply it to the antennae and transmit to the reader instead of reflecting back a signal from the reader (Lyngsoe, n.d.). These batteries need to be recharged or replaced once they are discharged. Some tags have to be disposed off when the batteries run out of power (Gloeckler, n.d.; Tecstra, n.d.). These tags have a read range of several 100 meters and are very expensive (more than US$20), and hence are used for tracking expensive items; for example, the U.S. military uses these tags to track supplies at ports (Lyngsoe, n.d.; RFID Journal, 2006a). Semi-active tags (or semi-passive or batteryassisted) also contain a battery, which is used to run the circuitry on the microchip, however it still relies on the reader’s magnetic field to transmit the radio signal (i.e., information). These tags have a larger range because all the energy supplied by the reader

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Figure 2. Radio frequency spectrum (Adapted from http://en.wikipedia.org/wiki/Super_high_frequency)

Radio spectrum ELF

SLF

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LF

MF

HF

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30 MHz

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30 MHz

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300 GHz

•

can be reflected back to the reader (RFID Journal, 2006a), which means it can work at low-power signal levels as well. These tags have a read range up to 100 meters and may cost a dollar or more (Lyngsoe, n.d.; RFID Journal, 2006a). Some of these tags often are dormantthat is, they are activated by the presence of a reader’s magnetic field. Once activated, the battery runs the circuitry and responds back to the reader. This is a mechanism to save the battery power (RFID Journal, 2006a). Passive tags completely rely on the energy provided by the reader’s magnetic field to transmit the radio signal to and from the reader. It does not have a battery. As a result, the read range varies depending upon the reader used (Lyngsoe, n.d.). A maximum distance of 15 meters (or 50 feet) can be achieved with a strong reader antennae and RF-friendly environment (Sweeney, 2005).

Read-Write vs. Read-Only RFID Tags •

•

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Read-only tags: The reader can only read data stored on such tags. The data cannot be modified in any manner. The tag manufacturer programs the data on the tag. Such tags are comparatively very cheap (Tecstra, n.d.). Write-once read-many (WORM): The owner of the tag can program the data by

•

writing the content on the tag. Data stored on this tag can be written only once, however it can be read many times (Sweeney, 2005; Tecstra, n.d.). Read-write tags: Data stored on such tags can be easily edited when the tag is within the range of the reader. Such tags are more expensive and are not often used for commodity tracking. These tags are reusable; hence they can be reused within an organization (Tecstra, n.d.).

LF vs. HF vs. UHF vs. Microwave Frequency RFID Tags RFID systems are classified as radio systems because they generate and radiate electromagnetic waves. Hence the RFID systems should operate within certain frequency limits like the LF, HF, or UHF. Since some of the frequencies are already in use by police, security services, industry, medical, or scientific operations, there is a limited number of frequencies available for RFID systems to operate. Figure 2 shows the radio frequency spectrum (Beherrschen, n.d.). RFID systems operate in four frequency bands: LF, HF, UHF, and Microwave. The RFID tags designed to operate in this frequency have special characteristics. We now discuss each of these kinds of RFID tags in detail.

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Low Frequency (LF) In LF range, RFID tags operate at 125 kHz or 134.2 kHz frequency (Lyngsoe, n.d.; RFID Journal, 2006a; RMOROZ, 2004). Some important characteristics of these tags are as follows:

tags are as follows:

•

•

•

•

•

•

•

•

•

•

•

Tags in this range are not affected by metallic surroundings and hence are ideal for identifying metal objects like vehicles, tools, containers, and metallic equipments. The reading range varies from a few centimeters to a meter depending upon the size of the antennae and the reader used (RFID Journal, 2006a; RMOROZ, 2004). These tags can also penetrate through water and body tissues, and hence often used for animal identification (RMOROZ, 2004). These are often used at places where tagged objects are moving at a lower speed and very few objects are scanned per second (Lyngsoe, n.d.). Most car immobilizers rely on LF tags. The tag is embedded in the key while the reader is mounted in the ignition area (RMOROZ, 2004). Data transfer rate is low, for example, 70ms for read operation. This is because at low frequency the communication is slower. In an industrial setting, electric motors can interfere with LF RFID operation (RMOROZ, 2004). Most LF-based systems can only read one tag at a time—that is, they do not support reading multiple tags simultaneously (RMOROZ, 2004). These tags are more expensive ($2 or more) to manufacture than the HF tags because of the size of the antennae (RMOROZ, 2004). This frequency is used worldwide without any restrictions.

High Frequency (HF) In the HF range, RFID tags operate at 13.56 MHz (Lyngsoe, n.d.; RFID Journal, 2006a; RMOROZ, 2004). Some important characteristics of these

•

• • •

•

•

•

•

HF tags can penetrate through most materials including water and body tissues, however they are affected by metal surroundings (Gloeckler, n.d.; RMOROZ, 2004). The thickness of the tag is typically less than 0.1mm and comes with variable antennae sizes. Bigger antennae offer more communication distance. The reading range is normally less than a meter (100cm). HF tags are comparatively cheaper (less than $1) than the LF tags. The data transfer rate is higher compared to LF tags, for example, 20ms for read operation. This is because at high frequency the communication is faster. In an industrial setting, electric motors do not interfere with HF RFID operation (RMOROZ, 2004). The reader can read multiple tags simultaneously. This is termed as anti-collision. There are many anti-collision protocols to prevent the reader from reading the same tag more than once. Depending upon the reader used, at least 50 tags can be read simultaneously (RMOROZ, 2004). HF tags normally work best when they are in close range with the reader (around 90cm). Secondly the orientation of the tags with respect to the reader plays a major role. For optimum communication range, the tag and the reader should be parallel. If however they are perpendicular, the communication range may reduce dramatically (RMOROZ, 2004). In Canada, Shell uses HF RFID for its Easy Pay customer convenience program. In Hong Kong Octopus card is used in public transit service. In the Netherlands, the Trans Link System uses contact-less smart cards to offer contact-less ticket solutions. The World Cup in Germany used tickets embedded with HF

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•

•

tags (RMOROZ, 2004). This frequency is used globally without any restrictions. However in certain countries the power of the reader is restricted. Global standard: ISO 15693, 14442, 18000-3.

Ultra High Frequency (UHF) In the UHF range, RFID tags operate at 433 MHz, 860-956 MHz, and 2.45 GHz (Lyngsoe, n.d.; RFID Journal, 2006a; RMOROZ, 2004). Most research work is dedicated to RFID in the frequency range of 860-956 MHz. Some important characteristics of these tags are as follows: •

•

• •

•

• •

•

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Multiple tags can be read simultaneously, for example, at least 200 tags (theoretically 800 possible) (RMOROZ, 2004). UHF tags operating at 860-956 MHz frequencies offer better read range by using short antennas. The reading range is normally 3-6 meters (RMOROZ, 2004). UHF tags are normally less expensive than HF tags because of low memory capacity and simple manufacturing process (RMOROZ, 2004). Such tags are commonly used on objects that are moving at a very high speed, and a large number of tags are scanned per second in the business contexts such as supply chain, warehouse, and logistics. These devices may have a range of 7.5metres (or 25 feet) or more (Lyngsoe, n.d.; Tecstra, n.d.). UHF tags do not work well in liquid and in metal surroundings. Larger read range limits their use to banking and access control applications, because the access card may be scanned from a longer distance and some unauthorized person might gain entry in restricted premises on your behalf. One major hindrance for widespread adoption of UHF RFID is lack of globally accepted regulations. For example, in Australia UHF operates in the 918-926 MHz range, in North

•

America UHF operates at 902-928 MHz, in Europe it operates at 860-868 MHz, while in Japan it operates at 950-956 MHz. Secondly, it operates in a highly crowded frequency range because most of the ISM (industrial, scientific, and medical) applications operate in the same range.

Components There are four main components of a RFID tag: microchip, antennae, substrate, and in some cases an additional battery.

Microchip An RFID tag contains a small microchip, which has some computing capabilities limited to simple logical operations and is also used for storage. The storage can be read-only, read-write, write-once read-many (WORM), or any other combination. The storage memory is used to hold a unique identification number that can identify each tag uniquely. Current generation passive tags have a memory capacity of 96 bits (characters). Passive tags have enough space to hold the identification number, however the active tags can have some additional information like the content of the container, its destination or origin, and so forth (Sweeney, 2005; Tecstra, n.d.).

Antennae The tag antenna is the conductive element that enables the transmission of data between the tag and the reader (RFidGazzete, n.d.). Antennae play a major role in deciding the communication distance; normally a larger antenna offers more area to capture electromagnetic energy from the reader and hence provides a greater communication distance. There are several kinds of antennae, like the rectangular planar spiral antenna, fractal antennas,1 and microstrip patch antenna (monopole, dipole). Different types of tags have different

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kinds of antennas, for example, low-frequency and high-frequency tags usually have a coiled antenna that couples with the coiled antenna of the reader to form a magnetic field (RFidGazzete, n.d.). UHF tag antennas look more like old radio or television antennas because UHF frequency is more electric in nature (Sweeney, 2005). Recent advances in technology have even facilitated the deployment of printed antennas to achieve similar functionality like the traditional antennas. One possible way of printing antennas is to use silver conductive inks on plastic substrates or papers (Tecstra, n.d.). The main advantage of printed antennas is that they are cheap.

Substrate

Data Retention Time This attribute describes the time for which the data can be retained on the tag, for example, RII16-114A-01 from Texas Instruments has a data retention time of more than 10 years.

Memory This attribute describes the available memory on the tag. It can be classified as factory-programmed read-only memory and user-programmable memory.

Programming Cycles

This is a chemical (or material) that holds the antennae and the microchip together. It is something like a plastic film (Sweeney, 2005).

This attribute defines the number of times the tag can be reprogrammed. This programming cycle is normally measured at a standard temperature (25 degrees).

Battery

Antennae Size

Unlike passive tags, active RFID tags contain a battery to power the circuitry, and generate and transmit radio signals to the reader. Onboard power supply can enable long-distance communication, as long as 1 kilometer (Sweeney, 2005).

This attributes defines the size of the tag antennae, for example, RI-I16-114A-01 has an antennae of the following dimensions: 24.2 mm +0.1mm/-0.2mm.

attributes

This is the material used to join the antennae with the microchip. Normally Polyethylenetherephtalate is used as a substrate.

RFID tags can be differentiated based on several attributes. In this section we discuss seven main attributes, which should be considered when selecting RIFD tags.

Operating Frequency This describes at what frequency the tag is designed to operate. As discussed earlier RFID tags can either operate in LF, HF, or UHF range of the radio spectrum.

Base Material

Operating Temperature This attribute describes the range in which the tag can operate, for example, RI-I16-114A-01 operates within -25°C to +70°C.

rfId readers RFID readers send radio waves to the RFID tags to enquire about their data contents. The tags then

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respond by sending back the requested data. The readers may have some processing and storage capabilities. The reader is linked via the RFID middleware with the backend database to do any other computationally intensive data processing. There are two different types of RFID readers (Gloeckler, n.d.).

Classification RFID readers can be classified using two different schemes. First, the readers can be classified based on their location as handheld readers and fixed readers. Second, the tags can be classified based upon the frequency in which they operatesingle frequency and multi-frequency.

Fixed Readers vs. Handheld Readers •

•

Fixed RFID Readers are fixed at one location (e.g., choke point). In a supply chain and warehouse scenario, the preferred location of a reader can be along the conveyor belt, dock door antennae or portals, depalletization stations, or any other mobile location. Portable or Handheld RFID Readers are designed for Mobile Mount Applications, for example, vehicles in a warehouse or to be carried by inventory personnel, and so forth.

Single Frequency vs. Multi-Frequency •

•

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Single-frequency operation readers operate in one frequency zone, either in LF, HF, or UHF. Such readers become inconvenient if tags in a warehouse are operating in different frequencies. Multi-frequency operation readers can operate in multiple frequencies. Such readers can conveniently read tags, which operate in different frequencies (i.e., LF, HF, or UHF). Hence these are more useful from a practical perspective, however such readers come at a premium price.

Components There are two main components in a RFID reader: antennae and the input/output (I/O) controller.

Antennae Every RFID reader is equipped with one or more antennas. These antennas generate the required electromagnetic field to sense the RFID tags. There are many different kinds of antennas like linearly polarized, circularly polarized, or ferrite stick antennas.

I/O Controller The data that the reader collects needs to be sent to the organization’s information system like ERP or WMS. Such a communication can be achieved by using RS-232,2 RS-485, or Ethernet. Some new generation readers also provide the Power over Ethernet (PoE) and 802.11 wireless connectivity protocol. Mostly all the readers possess a serial port for programming and data transfer. The manufacturer of RFID readers normally supplies the controllers. These controllers typically operate on 120V AC or 24V DC current.

attributes RFID readers can be differentiated based on several attributes. In this section we discuss 12 main attributes that should be considered when selecting RIFD readers.

Weight Weight of the reader is an important factor if the reader is mobile or handheld because it should be handy and should not cause inconvenience for its user.

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Communication Interface

Time for Identifying Tags

Every reader has a communication interface (e.g., RS232 or Ethernet 10/100BaseT or Wireless 802.11g or infrared data connection) to transfer the data gathered from the RFID tags.

This refers to the time that is necessary to identify tags. The number of tags identified per second varies with the type of readers, for example, IP3 Intellitag Portable Reader (UHF) (Intermec, 2006b) can identity six tags per second.

Integrated Filtering Component

Read Rate & Write Rate The need to filter RFID tag information is vital and usually can be done using a separate server in the RFID middleware or at the place where the reader is mounted. However in order to increase efficiency, reduce cost, and decrease potential points of failure in the network, it would be desirable if the reader itself is equipped with some computing power to facilitate information filtering before propagating an excessive large amount of data to RFID middleware or backend systems (e.g., ERP). New generation readers do offer such a facility like the IF5 Intellitag Fixed Reader, which is built on Linux platform, which runs IBM’s WebSphere® Everyplace® Micro Environment (WEME) (Intermec, 2006a). Some handheld readers are equipped with 700 Series Color mobile computers to do a similar job (Intermec, 2006b).

Antennae (Type and Number) Several antennas can connect a single reader at the same time. There are different kinds of antennas that are equipped with standard readers, for example, the IP3 Intellitag Portable Reader (UHF) (Intermec, 2006b) comes with integrated circular polarized antennas. The advantage of such antennas is that it can read tags in any orientation.

Software Most of the industry standard-compliant RFID readers are equipped with software to setup and re-configure the reader to enhance the resilience of the RFID systems.

This term usually describes the number of tags that can be read within a given period of time. It can also represent the maximum rate at which data can be read from a tag. The unit of measurement is in bits or bytes per second (RFID Journal, 2006b). Write rate usually describes the rate at which information is transferred to a tag, written into the tag’s memory and verified as being correct (RFID Journal, 2006b). The unit of measurement is in number of bits or bytes written per second per tag.

Volatile Memory Some readers have inbuilt volatile memory to retain a certain number of tag IDs.

Frequency Range This term is used to describe the reader’s capability to read tags in different frequencies. Some readers only operate in the UHF frequency (Intermec, 2006b), while others operate in the LF or HF range.

Operating Temperature Depending upon the application, the operating temperature of the reader should be considered. For example, if you are going to deploy an RFID reader in the desert, it should function properly in the extreme temperature. On the other hand, if it is to be deployed in extreme freezing condi-

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tions, the same should also be considered. Many industry standard readers operate within the range of -40°C to 50°C. Apart from the operating condition the storage conditions should also be considered. Normally the storage temperate has a bit higher range.

Miscellaneous Factors Similar to temperature, humidity is also a factor that should be considered when selecting a proper reader. Many readers can operate in 10% to 90% humidity levels (Intermec, 2006c).

Shock Resistance In an industrial setting, shocks are almost inevitable. Hence the reader should be resistant against these conditions as well. Likewise, frequent vibrations in the industry setting give rise to vibration-resistant readers.

Legal Restrictions Some countries have restrictions on using some frequencies because they may be allocated to ISM applications, hence before buying a reader, the country’s legal restrictions should be checked.

rfId Middleware In a general, the RFID middleware manages the readers and extracts electronic product code (EPC) data from the readers; performs tag data filtering, aggregating, and counting; and sends the data to the enterprise warehouse management systems (WMSs), backend database, and information exchange broker. Figure 1 shows the relationship between tag, reader, RFID middleware, and backend database. An RFID middleware works within the organization, moving information (i.e., EPC data) from the RFID tag to the integration point of high-level supply-chain management systems through a series of data-related services. From

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the architectural perspective, RFID middleware has four layers of functionality: reader API, data management, security, and integration management. The reader API provides the upper layer of the interface interacting with the reader. Meanwhile, it supports flexible interaction patterns (e.g., asynchronous subscription) and an active “context-ware” strategy to sense the reader. The data management layer mainly deals with filtering redundant data, aggregating duplicate data, and routing data to appropriate destination based on the content. The integration layer provides data connectivity to legacy data source and supporting systems at different integration levels and thus can be further divided into three sub-layers as specified in Leaver (2005): application integration, partner integration, and process integration. The application integration provides varieties of reliable connection mechanisms (e.g., messaging, adaptor, or the driver) that connect the RFID data with existing enterprise systems such as ERP or WMS. The partner integration enables the RFID middleware to share the RFID data with other RFID systems via other system communication components (e.g., the Data Exchange Broker in Figure 3). The process integration provides capability to orchestrate the RFID-enabled business process. The security layer obtains input data from the data management layer, and detects data tampering which might occur either in the tag by a wicked RFID reader during the transportation or in the backend internal database by malicious attacks. The overall architecture of RFID middleware and its related information systems in an organization are depicted in Figure 3. The backend DB component stores the complete record of RFID items. It maintains the detailed item information as well as tag data, which has to be coherent with those read from the RFID. It is worth noting that the backend database is one of the data-tampering sources where malicious attacks might occur to change the nature of RFID item data by circumventing the protection of an organization’s firewall. The

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Figure 3. RFID middleware architecture

WMS integrates mechanical and human activities with an information system to effectively manage warehouse business processes and direct warehouse activities. The WMS automates receiving, put-away, picking, and shipping in warehouses, and prompts workers to do inventory cycle counts. The RFID middleware employs the integration layer to allow real-time data transfer towards the WMS. The data exchange broker is employed in this architecture to share, query, and update the public data structure and schema of RFID tag data by exchanging XML documents. Any update of the data structure will be reflected and propagate to all involved RFID data items stored in the backend database. From the standardization view, it enables users to exchange RFID-related data with trading partners through the Internet. From the implementation angle, it might be a virtual Web services consumer and provider running as peers in the distributed logistics network.

benefIts to supply chaIn stakeholders The main benefits for stakeholders of adopting RFID in their business processes are manifold. In this section, we summarize these benefits and categorize them based on different potential supply chain stakeholders (as shown in Figure 4) who would benefit from deploying the RFID technology. After reading this section, readers are able to foresee several major advantages from their own perspectives.

Benefits to Manufacturers For manufacturers, RFID is able to support quality control by querying components and subassemblies as they enter the facility. For example, it can ensure that the correct components are included in an assembled item by automatically checking

Figure 4. The stakeholders in the supply chain

Manufacturer

Distribution Center

Warehouse

Logistics Company

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that all items from the Bill of Material are in place. Moreover, if used with appropriate modeling tools, it can help to predict the demand and supply for the products, which in turn determines the manufacturing plan, which is an essential input for the modern ERP and MRP systems.

Benefits to Distribution Centers For distribution centers (DCs), RFID can improve its reception processthat is, a guard confirms the truck’s appointment time, barcodes the trailer, and assigns a parking spot or dock. In particular, such an improvement is achieved by: (a) the RFID reader, which confirms the arrival of the truck, trailer, and all the items, eliminating the need to check the driver’s Bill of Loading; (b) the RFID reader can ‘query’ (scan) the contents much quicker than barcoding the trailer manually; (c) the RFID system can enable the DC gate to communicate with the warehouse management system in a timely manner, thus attaining the steady ‘information synchrony’. In doing so, the productivity of DC is undoubtedly enhanced. The RFID solution is also seen to be beneficial when managing claims and deductions occurring at the DCs (Symbol Technologies, 2004).

Benefits to Warehouses For warehouses, RFID can first improve the product flow by: (a) increasing the Putaway Accuracythe measurement of the accuracy of the process that physically places inventory products into storage; and (b) removing the need for additional barcodes on the pallet. Furthermore, the RFID system can improve the temporary storage. For example, with the support of RFID, inventory items can be ‘scanned’ wherever they are placed, which enables a more flexible storage environment. The RFID reader can help to identify any potential inventory compatibility problems for the

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large warehouse and DC, which typically handle inventory for all industry sectors. For instance, an industry dealing with perishables definitely needs special inventory facilities. Last, from the cost angle, deploying RFID can reduce the cost by maintaining a reduced level of inventory, waste, manual checks, and other miscellaneous inventory handling and management costs.

Benefits to Logistics Company administration For company executives, RFID can improve administrative laboring. For example, RFID enables the fine-grained laboring productivity measurement by timing the uploading of the goods for particular workers. Moreover, RFID helps to avoid employee theft during the outbound shipping by identifying the nature of the items (goods or company property).

Benefits to Retailers For retailers, RFID can help to make the price strategy. This is achieved by employing the RFID readers that capture precise information on how much product was sold from each location by placing different RFID readers in different selling place (e.g., point-of-sale machines). Next, RFID can improve customer satisfaction since it provides the right information at the right time and in the right format. For instance, DHL and FedEx each use an RFID-enabled tracking service, which helps the customer to monitor its own consignment. Customer satisfaction is further improved when the RFID allows retailers to know exactly what products are available in stock and in what capacity, which prevents the customers from being disappointed under the circumstance that their desired products are out of stock.

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Benefits to the Whole Supply Chain For SCM strategists, RFID facilitates detailed data collection and statistical analysis, from gross to very fine-grained levels. For instance, RFID readers in a retailer’s store can capture data on product arrival, placement, and movement, which can present the cyclical patterns. This key capability of RFID allows the executives to link the date received with the date sold. Moreover, it can help to identify possible points of information leakage throughout the entire supply chain. As a result, RFID increases the visibility in the supply chain, which can be used to make strategic decisions to further increase competitive advantage.

adoptIon strategy In this section, we discuss the RFID adoption strategy, which facilitates the ultimate successful RFID deployment. In general, an adoption strategy provides a roadmap to implement the technology in a way that is consistent with an organization’s strategic vision and goal. It is our belief that such a road map comprises four essential fundamental principles for RFID to be thoroughly adopted in the organization. We formulate them as follows to guide the RFID adoption strategy.

shared understanding In an organization, each decision maker or potential stakeholder of the RFID adoption may have a considerably different understanding from his or her own perspective towards RFID. The likelihood of successful adoption of RFID in effect hinges on the capability of the organization to enforce a broader shared understanding. This calls for a harmonious integration of each individual’s opinion in a manner that benefits the organization’s core competency and strategic goal, rather than

that focuses on isolated areas of benefits. Hence, in the early stage, the main aim is to ensure that such a shared understanding is permeated at each level of the executive management and general administrative members of the organization.

goal setting No technology deployments can be successful unless the goals of the deployment program are aligned with the business goals. Hence, at the beginning of RFID adoption, the organization has to set a very clear unambiguous goal for RFID adoption. This goal is to be aligned with the organization’s existing business goal. With the goal, important stakeholders can thus be explicitly (rather than implicitly or potentially) identified. A definitive goal also helps to find the ‘buy-in’ among all the stakeholders of the RFID technologya key component in achieving organization change successfully due to the fact that support from senior management is exceptionally critical.

Justification It is well recognized that, in order to justify a new technology, a number of convincing business scenarios are of paramount importance. These scenarios must contribute to achieving the organization’s business goals. They can be substantiated by collecting and investigating all the operational and technical requirements in the organization. Moreover, a cost-benefit analysis (e.g., ROI estimation) to suffice the financial concern will significantly increase the possibility of RFID being accepted by those hesitant stakeholders. Last, potential challenges and issues during and after deploying RFID have to be clearly identified at the early stage. Each challenge should be addressed by possible solutions submitted to all involved stakeholders.

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Planning and Approaching Before starting the implementation, it is very important to define a detailed deployment plan with thorough consideration to various factors like schedule, impact, and usability. This facilitates a positive and quicker realization of the technology outcomes. A good project plan includes evaluation of technology option, standard-based deployment approach, measurements and optimization techniques, and a continuous improvement roadmap. During the implementation, an incremental approach shall be taken. Thus pilot tests and milestones can be used for checking the progress of the implementation and ensuring the desired outcome is achieved through the delta part implemented in the current adoption iteration. Based on these aforementioned principles, we then present a refined strategic step flow for the RFID adoption strategy. This is shown in Figure 5, where the elaborated steps (rectangles) of the roadmap are outlined one after another and are connected by directed edges and condition check (triangles). We explore each step one by one as follows.

Core Competency Reaffirmation The core competency (CC) is defined as one thing that an organization can do better than its competitors, and is crucial to its success. Before the organization adopts any candidate technologies, it has to assess them against the core competency. For example, if a new technology enables the organization to maintain its CC and even gain more CC (i.e., CC-aligned), then this technology is favorable to the organization and should be given further adequate consideration. Otherwise, such a technology needs more evaluation under current business contexts even if it appears very promising from the technical perspectives or from other organizations’ angles. It is highly desirable that the CC-aligned technology, once validated

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within the organization, receives unanimous support from senior management to the general staff. This undoubtedly paves the way for easier adoption that is less likely to be tangled with internal politics, funding difficulties, and some of the execution delays. For example, when retailer giant Wal-Mart realizes that the core competence lies in its dominant distribution channels, which can greatly benefit from RFID technology (as discussed above), it demands all of its top 100 suppliers to attach RFID tags onto their goods. On the contrary, if a company XYZ’s CC is manufacturing high-quality automobile engine assemblies, it should be very cautious in adopting RFID unless its primary retailers at the downstream urge it to do so, because the distribution is not XYZ’s core competencies and doing so will only distract itself from doing what they are good at. Therefore, in general, an organization should be wary of taking aggressive steps in implementing RFID solutions before the core competence has been thoroughly reaffirmed and evaluated.

Feasibility Analysis If the RFID technology is being considered to be aligned with the CC, it is time for the organization to perform the feasibility analysis, which deals with four major aspects: •

•

It is very important to estimate the capability of existing information systems. Since RFID will dramatically increase the amount of the data captured from instance level tags, the information systems have to capture, process, and analyze such a huge amount of data efficiently. Any insufficiency of information systems will definitely hinder the RFID technology to realize its full potential. RFID solutions could be quite costly; hence continuous and dedicated RFID funding support is essential.

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Figure 5. RFID adoption roadmap

Reaffirm the Core Competency (CC) of the business

P1 P2 P1

Prioritized Scenario List

For each candidate scenario RFID is aligned with CC?

Justification Review

Yes No Feasibility analysis 1. Information Sys. 2. Funding 3. Personnel 4. Time to deploy

Justified?

Adoption Issue Analysis

Feasible? Yes

Pilot Test and Result Analysis

Identify current candidate scenarios that might benefit from deploying

Yes Negative Impact? Continue

No Prioritize all the identified candidate scenarios against the estimated volume and rate of the return from RFID.

Implement this candidate scenario Milestone 1

Milestone 2

Milestone n

…… Prioritize candidate scenarios

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•

•

Personnel preparation is another feasibility issue needing consideration. Once implemented, the RFID might change the business process as well as fundamental information systems operations, which incur substantial IT training and adaptation study. The learning capability of involved working staff also determines the success of RFID adoption. Lastly, the organization has to choose the appropriate time to deploy RFID. Early adoption of RFID has advantages as well as risks. The feasibility of time should study whether the company is ready for RFID at that particular time, and moreover can bear the risk associated, even if the prerequisite conditions are all met.

Candidate Scenarios The next step is to identify candidate scenarios that would benefit from RFID and to measure the potential benefit in a self-defined scale. For example, for manufacturing units, RFID can be used to support quality control by querying components and subassemblies as they enter the facility. This is a typical candidate scenario that can be identified. It is recommended for the organization to enumerate all the potential RFIDinvolved candidate scenarios that can impact an organization’s core competences. One efficient way to accomplish such an enumeration would be to define several business areas based on the beneficiary summarized above (e.g., the manufacturing, distribution, warehouse management, etc.). For each business area, one representative candidate scenario is selected according to its relevance to the organization’s CC. Also, it is a good idea to break down a large scenario into a couple of smaller scenarios, and only one of which is chosen based on the stakeholders’ preferences (e.g., the one that promises the most effective impact towards the CC). In other words, as an emerging technology, RFID must be adopted in a selective and iterative manner so that the risk,

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cost, and organization changes can be controlled at the minimum level, whereas the positive impact to CC is pursued at the maximum level. A formal description is necessary to state the basic characteristics of each scenario. A typical tabular description of a sample candidate scenario (based on RosettaNet, 2001) is presented in Table 1.

Scenario Prioritization The previous step produces a list of candidate scenarios, each of which represents one particular business area in this organization. (Readers shall recall that one business area comprises a set of scenarios. For each iteration, we only choose one scenario from this set in each business area). In this step, we need to prioritize this list against a set of criteria such as estimated impacts to CC, the ROI, the cost, and so forth. Such a prioritizing process also needs to consider the preference from different RFID stakeholders. This is achieved by: 1. 2.

3. 4. 5.

6.

Organizing the criteria set into a hierarchical structure Performing the pair-wise comparison between any two candidate scenarios against one specific criterion Providing the pair-wise comparison between any two criteria for each rfid stakeholder Computing the stakeholders-aggregated preferences for each criteria Calculating the overall weight for each scenario allowing for all criteria with different preferences Ranking the scenario list against the weight value generated in #5, with the biggest weight value being positioned in the first rank. This step eventually generates a prioritized scenario list for further justification review.

Justification Review In this step, for each candidate scenario in the Prioritized Scenario List, the organization con-

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Table 1. A sample of candidate scenario Scenario Name: Involved Stakeholders:

Shipment Status Query 1.

A consignee, or third-party logistics firm, or shipper

2.

Consolidators, or warehousing entities, or freight forwarders, documentation personnel, carriers or custom clearing personnel

This scenario states a third-party logistics firm or shipper to query for the status

Scenario Description:

of one or more shipments and a transport service provider to respond to the query. Identifies the shipment for which status information is needed. The Shipper’s and Carrier’s Reference Numbers identify a particular shipment. Communicates

Detail Activities:

the status of a shipment. The Shipper’s and Carrier’s Reference Numbers identify a particular shipment. The response references geographical location, time, and customer’s entry number; it may include information about delivery exceptions. It does, if RFID technology is adopted, include order-level detail. Increasing the shipping data accuracy: • The transport service provider’s detailed shipping manifest ensures that the right number of cases is en route, thereby improving customer (e.g., consignee) service levels.

CC Impacts from RFID:

• The transport service provider reduces load times by eliminating manual order auditing. • There are fewer discrepancies between shipments and invoices, because the shipping manifest can automatically generate an invoice that includes case EPCs.

Business Process:

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ducts the justification review, a static analysis of RFID deployment only related to that particular candidate scenario. Readers are advised that several justification methods can be applied. For example, traditional ROI (return on investment) methods can be utilized to examine whether RFID technology should be deployed in this candidate scenario. However, they seem inadequate as RFID so far has a high level of uncertainty (e.g., tag prices, standards), and existing stories (eWeek. com) show that a number of possible applications are unfairly treated by existing ROI methods and tools. The appropriate justification tools are thus highly desirable and become a promising future research direction. For each candidate scenario, if the justification review produces negative results, it is removed from the prioritized scenario list, and the next scenario will be considered to perform the justification review. Otherwise, this scenario will be subsequently chosen for the following pilot test.

Adoption Issues Before the pilot test, a clear consensus of issues and their solutions is necessary among all the stakeholders. Hence in this step, the stakeholders need to unanimously identify adoption challenges specific to this scenario. The next section of this chapter will provide a comprehensive issue list that the organization should consider. However, we believe each scenario has its own unique set of adoption issues. Even for the same issue, it has different impact on various scenarios. It is suggested that the organization itemizes all the possible problems that it might encounter during the pilot test. More importantly, the solutions that address these challenges should be proposed and planned in this step. This ensures the smooth progress of the pilot test. Moreover, the solutions can also be validated and modified during the pilot test.

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Pilot Test Once the justification review is confirmed positively, the pilot implementation can be carried out in an experimental environment. It can be an RFID prototype in a smaller scope or scale of this candidate scenario. For example, a pilot deployment in one or two locations allows evaluation of RFID vendors, equipment, and software, and provides the opportunity for different stakeholders to gain experience with RFID. Furthermore, such a pilot is to produce the impact analysis for this candidate scenario. As many impacts can be associated with the RFID deployment, some of them are beneficial to some shareholders, while some are negative to other shareholders. Hence the pilot implementation can estimate such impacts brought by RFID deployment. If most of the results tend to be negative, the likelihood that the RFID to be implemented in this scenario appears to be very low. The organization might need to consider the next candidate scenario along the Prioritized Scenario List. In contrast, positive pilot test results with the pilot feedback suggest the start of the RFID implementation in this candidate scenario.

Implementation Once the pilot test is passed, the formal implementation can be carried out in an incremental manner through a set of iterations with a couple of milestones, which ensure that the implementation cost and risk can be controlled and mitigated to the minimal level. Each milestone has different focuses. Table 2 lists possible sequential tasks within one milestone. Particular attention is given for fostering the transition between milestones.

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Table 2. Sequential tasks within one milestone

Architecture Design

The architecture can follow some RFID reference model and must be extensible so that new architectural elements or scenarios can be added in the next milestones.

Edge Deployment

Assemble the RFID edge solution from selected vendors based on the drafted architecture rather than from one particular RFID system or product.

Verification and Validation

Measure the system performance and business impact.

Scale Deployment

Consider adding additional locations in terms of geography or business unit for next milestone.

Integration Deployment

Add the RFID data-integration middleware to enable the data sharing with other information systems, or even other partners. Consider adding business process integration infrastructure for the next milestone.

Evolve and Expand

Considering the adaptability of this milestone to ensure that it can expand and evolve to meet the changing needs of the business in the following milestones.

rfId adoptIon challenges Several key adoption issues will be discussed in this section. The main issues that we address in this section are cost associated with the deployment of RFID system, security and privacy concerns, and finally more technical issues in deployment of an RFID system.

cost A cost-estimation model for a full-fledged deployment of an RFID system in a supply chain environment should consider the following factors.

RFID Tags When deploying an RFID system, one should consider the cost of buying RFID tags. It is a good idea to consider renewable tags (if possible)

as a means to reduce cost. Normally the cost of active tags is more than the passive tags. The active tags are in the range of US$20 to $50 per tag (Lyngsoe, n.d.; RFID Journal, 2006a, 2006c) . The cost of the passive tags depends upon the frequency. Normally the LF tags are more expensive than HF or UHF because of the size of the antennae (RMOROZ, 2004). It normally costs more than $2 and goes as high as $10. The HF tags are cheaper than the LF tags and normally cost less than a dollar. Apart from the cost of the tags, companies should also consider the cost of testing the passive tags. The failure rate for the UHF EPC tags ranged from 0-20% in the year 2004. This might drop down once manufacturers start using sophisticated manufacturing techniques, however there is a cost associated to ensure that the tags are functioning properly. Finally there is cost associated with replacing the defective tags (RFID Journal, 2006c).

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RFID Printers

RFID Middleware

An RFID label has a similar functionality as an RFID tag. However it can be stuck on like a label. The RFID label can be printed using RFID printers. Hence if you are planning to use RFID labels, the cost of the RFID printer should be considered. In some applications RFID labels are much more preferred because of the environment and the products. For example applications like express parcel delivery, library book/video checkout, sensitive document tracking, ticketing (sports, concerts, ski lifts, etc.), and pharmaceuticals prefer RFID labels (Zebra, 2006).

RFID middleware contributes a major portion of RFID investment. Many vendors supply RFID middleware, and the cost can vary depending upon the capabilities of the middleware. Usually factors that contribute to cost include complexity of the application and the number of places the middleware would be installed. Apart from the middleware, the companies should also consider the cost of edge servers, which are normally deployed in the warehouse, distribution center, or production facility. The edge servers are simple servers, which are connected to the RFID reader using a universal serial bus (USB) port.

RFID Readers When deploying an RFID system, one should consider the cost of buying RFID readers. The fixed readers are normally cheaper than the portable readers. It is normally in the range of $500 to $5,000 depending on the features built into the reader (RFID Journal, 2006c). Dumb readers are usually cheaper, as they do not have any computing capability. On the other hand intelligent readers offer computing capability to filter data, store information, and execute commands. Agile readers can communicate with tags using a variety of protocols, while multi-frequency readers can read tags using different frequencies (RFID Journal, 2006c). All these features contribute to the cost of readers, and the organization should select a proper reader based on its application requirements.

RFID Antennas Almost all the readers are equipped with one or more antennas. However in some cases the need for additional high-power antennas cannot be ruled out, and hence this additional cost should be considered before deciding to deploy an RFID system.

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Training Existing Staff Introducing new technology in an organization introduces costs associated with training the concerned staff. For example an organization will need to train its employees, particularly engineering staff who will manage readers in manufacturing and warehouse facilities, and IT staff who will work on the systems that manage RFID data (RFID Journal, 2006c).

Hiring Technology Expertise Most of the companies, as of now, would not have the expertise to deploy a complete RFID system. This is partly attributed to the fact that RFID is a relatively new technology. Hence an organization would need to outsource this task to a third party who knows how to install the readers, decide the appropriate location for fixing the tag on the products, ascertain that the data gathered by the reader is properly propagated to the middleware in the right format, and so on. This is quite important because RFID systems can be sometimes difficult to install, as there are several factors that can affect the optimum performance of such a system. Hence a major portion of RFID investment has to be targeted to this area.

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Other Miscellaneous Costs

depending upon the number of distribution centers and warehouses in use. Here we assumed 100 readers and printers, and 50 edge servers. These numbers are used just as an example; it can vary depending upon each company.

The miscellaneous costs might include regular maintenance of the RFID readers or replacement of damaged tags or antennas.

Cost Estimation for RFID Deployment in Supply Chain Tracking

read-rate accuracy

When deploying an RFID system for inventory management and control in a supply chain, all the above-mentioned costs should be considered. According to Forrester Research, the estimated cost of middleware is around $183,000 for a $12 billion manufacturer looking to meet the RFID tagging requirements of a major retailer (RFID Journal, 2006c). In the same manner the estimated price of $128,000 could be spent for consulting and integration, $315,000 for the time of the internal project team, and $80,000 for tag and reader testing (RFID Journal, 2006c). A simple estimate is provided in Table 3. On the lower end an estimated cost of around $3 million should be invested in an RFID project for inventory tracking and management if a total of 10 million items are tagged. The number of readers, printers, and edge servers would vary

Achieving 100% read-rate accuracy is a major adoption challenge with RFID deployment. Supply chains and warehouse management solutions based on RFID are highly vulnerable to read-rate inaccuracy because of the number of RFID-tagged items that need to be scanned every second. Consider the scenario when a palette containing 1,000 RFID-tagged items is scanned at the warehouse exit. There is a high probability that the reader would not scan a few tags. It is difficult to list the main reasons that result in inaccurate readings because there are too many “ifs” and “buts”. Accuracy is dependent on so many unrelated variables that it is difficult to list the main factors behind the cause. However we attempt to outline some basic parameters, which results in inaccurate readings. Some of the main reasons for inaccurate readings include the environment in which RFID system works, material of the item

Table 3. Simple cost estimate Investment Area

No. of Units

Cost Per Unit (USD)

Total Cost (USD)

10,000,000

$ 0.15

$ 1,500,000.00

RFID Readers (Handheld, Fixed, Forklift)

100

$ 8,000.00

$ 800,000.00

RFID Printers

100

$ 3,000.00

$ 300,000.00

Edge Servers

50

$ 2,500.00

$ 125,000.00

RFID Tags

RFID Middleware

N.A.

$ 200,000.00

RFID Consulting

N.A.

$ 128,000.00

RFID Training

N.A.

Variable Cost

Tag Validation

N.A.

$ 80,000.00 Total Cost

$ 3,133,000.00

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being tracked, reader configuration, reader and tag placements, tag orientation, and so forth. To successfully deploy an RFID system, some key parameters should be considered to achieve accurate readings.

Tagged Material Maintain some consistency when tracking materials. It is not a good idea to standardize the reader configuration to track cartons, trolleys, pallets, glass materials, documents, or metal or plastic bins. This is because different materials behave differently to RF energy; some materials are RF friendly, while others are RF absorbent or RF opaque. A reader configured to read tags from RF-friendly material would definitely fail to give 100% read-rate accuracy if used to track RF-absorbent or RF-opaque items.

Preplanned Object Movement To assure good read rates, it is advised to move the tagged objects on a predefined route (or pattern). You cannot expect good rates if the cartons are moved through a forklift, people, metals trolleys, plastic trolleys, and so forth. There should be just one or two modes of transport well tested for 100% read-rate accuracy.

Tags from Different Vendors Read rate is also affected if tags are used from different vendors because the performance of such tags varies significantly. Another reason is the use of different standard-compliant tags (like EPC Gen1 & EPC Gen2). It is also difficult to configure the reader power level at an optimum level where it supports all different tags with 100% read rate. Ideally one should try to use a single standard and single vendor tag in one ecosystem. Nevertheless this may change as the standards improve.

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Tag Orientation Orientation is one of the big factors for providing good read rates. Even though dual dipole tags perform much better in all orientations, it is still advised to follow a policy on tag placement and tag orientation (TPTO). A standard policy on TPTO across an organization would definitely improve the read-rate accuracy.

security Security of RFID solutions in supply chain management is a major issue. Automated warehouse management and supply chain solutions based on RFID should leave no room for security loopholes. Security properties like confidentiality3, integrity4, availability5, authentication6, and anonymity7 need to be considered for the successful RFID adoption. Consider the warehouse management scenario: if a malicious reader can eavesdrop (spy) on the communication between the tags and the readers, confidentiality and anonymity in such communication is lost. A malicious reader may be placed by a competing organization to study the goods movement in your warehouse. Such tactics for gathering business intelligence can be addressed if security mechanisms are in place. Secondly, information stored on the RFID tag could also be tampered with by malicious readers, which could result in wrong items being loaded from the warehouse. For example, if the malicious reader changes the information on RFID tag from Orange to Apple, then a palette containing apples might be shipped when the intention was to ship oranges. Data tampering (or integrity) can raise issues like quality of service and trust in logistics and supply chain, and hence needs to be addressed thoroughly. Similarly, malicious entities can employ an active jamming approach to launch denial of service attacks, which would make the RFID network unavailable. In such an

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Figure 6. Major security issues with RFID adoption

Security Properties

Confidentiality

Availability

Authenticity

attack the RFID reader cannot query the tags, and hence the warehouse management system can stop working or real-time status of the warehouse cannot be made available (Engberg, Harning, & Jensen, 2004; Menezes, Van Oorschot, & Vanstone, 1996). In this section we discuss solutions from literature which can address these security issues. The details discussed in this section are explained from a security expert’s perspective. Hence if the reader is a business executive or management strategist, they may skip this section. All they need to know is that there are some security mechanisms in place (as shown in Figure 7) which can be used to guarantee security in communication between the tag and the reader. These security mechanisms are based on the assumption that expensive Gen-2 RFID tags are used.

Anonymity

Integrity

Access Control and Authentications Several approaches to access control and authentication are proposed in the literature. We will discuss some in greater detail in this section. Exclusive-OR Approach for Authentication A simple authentication scheme based on challenge-response protocol is presented in Juels, Rivest, and Szydlo (2003). It uses only simple bitwise exclusive-OR operations and no other complicated cryptographic primitives, hence it is suitable for RFIDs. However, the main issue with this approach, as pointed out by Dimitriou (2005), is that it involves the communication of four messages and frequent updates which would increase unnecessary traffic between the reader and the tag (Dimitriou, 2005; Wong & Phan, 2006). Feldhofer

Figure 7. Security solutions to address security issues with RFID adoption

Security Mechanisms

Access Control and Authentication

Tag Authentication

Encryption and Message Authentication

Tamper Detection

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(2004) demonstrated that it is possible to achieve authentication without making use of computationally intensive public-key cryptography, but instead used the advanced encryption standard (AES), which is a symmetric-key technique for encryption (Stallings, 1999; Vajda & Buttyan, 2003; Wong & Phan, 2006). Hash-based Approaches for Access Control A hash-based access control protocol is discussed in Weis (2003). Here the tag is first in a locked state. When the tag moves to the unlocked state, the reader can access the tag’s details. In order to change the state, the tag first transmits a meta ID, which is the hash value of a key. An authorized reader looks up the corresponding key in a backend system and sends it to the tag. The tag verifies the key by hashing it to return the clear text ID and remains only for a short time in an ‘unlocked’ state which provides time for reader authentication and offers a modest level of access security (Knospe & Pohl, 2004; Wong & Phan, 2006).

Tag Authentication Tag authentication is another security mechanism that authenticates the tag to the reader and protects against tag counterfeiting. There are several protocols proposed for this purpose. For example, Vajda and Buttyan (2003) propose and analyze several lightweight tag authentication protocols. Similarly, Feldhofer (2004) proposes the simple authentication and security layer (SASL) protocol with AES encryption and analyzes the hardware requirements (Feldhofer, 2004; Knospe & Pohl, 2004; Wong & Phan, 2006).

Encryption and Message Authentication Next-generation RFID systems like the ISO 14443 or MIFARE® offer encryption and authentication capability for data which is exchanged between the readers and the tags. In RFID systems the UID is the most important data, and this can be

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secured by encrypting the memory blocks at the application layer. The UID is usually read-only, and many RFID tags provide a permanent write lock of memory blocks. This can ensure data integrity, but of course, not message authentication (Knospe & Pohl, 2004; Wong & Phan, 2006).

Tamper Detection RFID tags carry data that represent the unique item identifier (UID) as well as product details to which it is attached. This data is very significant and, if tampered with, can have severe consequences. For example if data representing the “nature of good” is changed, it can have severe implicationthat is, instead of Lethal Weapons, the RFID could be modified to represent that the consignment carries Oranges. Such data tampering needs to be detected, as it can be a threat to national security. Potdar et al. (2005) presented a solution to address this security issue. They proposed a tamper-detection mechanism for low-cost RFID tags. The proposed algorithms for tamper detection works by embedding secret information (like a pattern) in the RFID tags. The pattern is embedded by manipulating the unique identifier in such a way that even after embedding the pattern, each RFID tag can be uniquely identified. To detect tampering, a tamper-detection component is introduced in the RFID middleware, which detects the embedded pattern. If the pattern is not present, it indicates data tampering, in which case the data from the RFID is quarantined and later processed appropriately (Potdar et al., 2005).

privacy The deployment of RFID in day-to-day life can raise several privacy concerns. The major concerns originate because of the inherent ability of RFID to track people who are using products that have RFID tags. Privacy experts say that marketers and retailers can gather detailed customer profiles, based on their transactions with that individual (Juels et al., 2003;

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Figure 8. Major privacy concerns with RFID adoption

Privacy Concerns

Analysing Consumer Behaviour

Cradle to Grave Surveillance

Discrimination

Kumar, n.d.) . The privacy concern originating by the use of RFID can be categorized into five areas as shown in Figure 8. If RFID is deployed in a full scale, it may result in many privacy concerns because RFID can be used to track consumer behavior, which can further be used to analyze consumer habits. It can even be used for hidden surveillance, for example, deploying secret RFIDs for tracking. With the size of RFIDs reducing day by day, it has now become possible to hide them within products without the owners’ consent. For example, RFID tags have already been hidden in packaging (Hennig, Ladkin, & Siker, 2005). A scenario of hidden RFID testing was discovered in a Wal-Mart store in Broken Arrow, Oklahoma, where secret RFID readers tracked customer action (Caspian, 2003). If RFIDs are embedded in money, it could result in discrimination. Consider this scenario: whenever a customer enters a shop equipped with RFID readers, they would easily know the purchasing power of the customer and can automatically manipulate pricing information to the customer’s credit worthiness. This results in automated prejudices (Hennig et al., 2005). Using RFIDs could even trigger anti-social activities. Criminals with RFID readers can look for people carrying valuable items and can launch selective attacks (Hennig et al., 2005). However most of these issues can be tackled by privacy enforcement laws, which can be incorporated into the nation’s legal framework. All these privacy issues

Terrorist Activities

Hidden Surveillance

have created a lot of fear in the consumer community. In order to address these issues, several approaches are proposed in the literature. We will discuss each of these techniques and then list the pros and cons of each approach.

Kill Tag Approach This is one of the simplest approaches to protect consumer privacy. According to this approach all the RFID tags would be killed or deactivated once they are sold to the customerthat is, when the products with the RFID tags pass through the checkout lane. Once a tag is killed, it can never be activated or used again. A password-protected ‘destroy’ command can also be integrated into the electronic product code (EPC) specifications, which would kill the tag permanently (Knospe & Pohl, 2004). However according to some privacy gurus, simple use of kill tag is sometimes inadequate. There are situations where the consumer would prefer the RFID tag to remain active even when the product is sold. For example, it would be wise to embed a tag in an airline ticket to allow simpler tracking of passengers within an airport. Consider embedding a tag in invoices, coupons, or return envelopes mailed to consumers; this can be used for ease of sorting upon return. Another example would be microwave ovens that read cooking instructions from food packages with embedded RFID tags (Kumar, n.d.). At the first instance, it seems that the kill tag approach would

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handle most of the privacy concerns, however as discussed above, in some situations it would be sensible to keep the tag active even after the product is sold. Hence this approach does not offer a satisfactory solution.

smartness of the tag. Many techniques to achieve privacy protection based on cryptographic protocols are proposed in the literature; some of these were already covered earlier in this chapter.

Physical Approach

case studIes

There are two basic approaches to achieve privacy protection using physical techniques. They are:

In this section we list a few RFID case studies from the supply chain and logistics domain.

•

Moraitis fresh

•

Faraday Cage Approach: A Faraday cage is an enclosure designed to exclude electromagnetic fields. As a result certain radio frequencies cannot penetrate through the enclosure area. It can address some privacy concerns, for example, if high-value currency notes start embedding a RFID tag, then using foil-lined wallets can guarantee privacy (Kumar, n.d.). This approach has limited application because a faraday cage cannot shield all items (mobile phones, clothing, etc). Active Jamming Approach: According to this approach the consumer can carry a radio device that would keep broadcasting radio signals in order to disrupt the normal operation of nearby RFID readers. Although this approach offers a way to protect privacy, it may be illegal in some countries. Broadcasting unnecessary radio signals can disrupt the operation of legitimate RFID readers where privacy is not concerned.

Smart Tag Approach This approach suggests making the RFID tags smarter so that they can manage the privacy concerns in much better manner. Adding extra functionality like introducing cryptographic capabilities would enable the tags to communicate in a secure environment and can increase the

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Moraitis supplies fresh fruits and vegetables to major supermarkets in Australia. The shelf life of such products is one or two weeks, which means the produce must be moved from the field to the supermarket within two to five days. Moraitis was looking at ways to boost the efficiency of its supply chain. IBM Business Consulting Services proposed an RFID solution for the above problem. Moraitis realized that RFID tags could offer a cost-effective solution to streamline supply chain functions, and help reduce the time and labor required. The technology was provided by Magellan’s StackTag technology, which can read and write to multiple tags moving on high-speed conveyors in any orientationeven when tags are overlapping or touching. The initial investment was around A$ 100,000. Automated inventory tracking improved the accuracy of Moraitis’ inventory management decisions (IBM, n.d.).

australia post Australia Post, the postal service in Australia, was looking at ways to improve its operational efficiency, focusing on a mail sorting problem. Lyngsoe Systems was approached to offer a solution. Australia Post has deployed Lyngsoe’s AMQM mail-quality measurement system, which contained more than 12,500 active tags (operating at 433.92 MHz frequency) and 400 RFID

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readers (Lyngsoe’s RD21 readers supporting 15 antennas) at several mail sorting and distribution locations. QSM software, which is an integral part of the AMQM system, is used to analyze RFID-generated mail-tracking data. The RFID readers will automatically read the tagged test envelopes as they pass through key sorting points in the network, and update the backend system (Collins, 2005).

australian Military The Australian Defense Force (ADF) had deployed an active RFID system for supply chain tracking to help forecast when shipments arrive at their destinations, and to ensure that material is accurately and efficiently ordered. Savi Technology deployed the RFID system, with an initial contract of US$10.1. The ADF deployed Savi’s SmartChain Consignment Management Solution (CMS), which is a suite of hardware and software components that uses barcode and RFID. ADF will now be able to make its logistics network more visible by using an in-transit visibility system (ITV) (O’Connor, 2005).

conclusIon Automated identification and data capture (AIDC) is a crucial technology in supply chain management as it forms the backbone of the modern supply chain. RFID, the emerging wireless AIDC technology, first appeared in tracking and access applications during the 1980s. It allowed for non-contact reading, and is very effective in manufacturing and other hostile environments where bar code labels could not survive. As a result the industry has looked into the possibility of embracing such a promising AIDC technology in a massive scale. Therefore, this chapter provided a comprehensive introduction to RFID technology and its application in supply chain

management from multi-level perspectives for various readersRFID novices, RFID advanced users, business consultants, business executives, scholars, and students. In particular, we first discussed fundamental RFID elements governed by specific RFID standards, which are further elaborated in detail. Having such basic knowledge, we identified RFID adoption challengesmainly cost, security, and privacywhich have become the biggest concern for those early RFID adopters. Bearing these big challenges in mind, company executives and management are seeking the roadmap of RFID deployment, which we offered as a dedicated section in this chapter. Before making substantial investment in RFID solutions, management would be interested in knowing existing successful case studies. We thus provided five RFID case studies to supplement this chapter for further references. All of these case studies are centered on logistics and supply chain domain, taking into consideration the overall theme of the book.

references Beherrschen, V. M. (n.d.). Frequency ranges. Retrieved February 2, 2006, from http://www. rfid-handbook.de/rfid/frequencies.html Caspian. (2003). Scandal: Wal-Mart, P&G involved in secret RFID testing. Retrieved June 13, 2006 from http://www.spychips.com/pressreleases/broken-arrow.html. Collins, J. (2005). Aussie track mail via RFID. Retrieved January 4, 2006, from http://www. rfidjournal.com/article/articleview/2014/1/1/ Dimitriou, T. (2005). A lightweight RFID protocol to protect against traceability and cloning attacks. In Proceedings of the Conference on Security & Privacy for Emerging Areas in Communication Networks.

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Engberg, S.J., Harning, M.B., & Jensen, C.D. (2004). Zero-knowledge device authentication: Privacy and security enhanced RFID preserving business value and consumer convenience. In Proceedings of the Conference on Privacy, Security & Trust (PST’04), Canada. eWeek.com. (n.d.). For many, RFID ROI still a dream. Retrieved June 10, 2006, from http://www. eweek.com/article2/0,1895,1756872,00.asp Feldhofer, M. (2004). A proposal for authentication protocol in a security layer for RFID smart tags. In Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (MELECON), Dubrovnik. Gloeckler, D. (n.d.). What is RFID? Retrieved September 1, 2005, from http://www.controlelectric.com/RFID/What_is_RFID.html Hennig, J.E., Ladkin, P.B., & Siker, B. (2005). Privacy enhancing technology concepts for RFID technology scrutinized. (Research Report # RVSRR-04-02) Bielefield: University of Bielefield. IBM. (n.d.). Moraitis fresh slices supply chain costs with IBM RFID solution. Retrieved January 8, 2006, from www.ibm.com/industries/wireless/ pdfs/moratis_final.pdf Intermec. (2006a). IF5 IntellliTag fixed reader. Retrieved June 14, 2006, from http://www.pointofsaleinc.com/pdf/Intermec/if5.pdf Intermec. (2006b). IP3 IntellliTag portable reader (UHF). Retrieved June 14, 2006, from http://www. pointofsaleinc.com/pdf/Intermec/ip3.pdf Intermec. (2006c). IV7 IntelliTag vehicle mount RFID reader. Retrieved June 14, 2006, from http:// www.pointofsaleinc.com/pdf/Intermec/iv7.pdf Juels, A., Rivest, R.L., & Szydlo, M. (2003). The blocker tag: Selective blocking of RFID tags for consumer privacy. In Proceedings of the 10th ACM Conference on Computer and Communications Security, Washington, DC.

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Knospe, H., & Pohl, H. (2004). RFID security information security technical report. Elsevier, 9(4), 30-41. Kumar, R. (n.d.). Interaction of RFID technology and public policy. Retrieved June 14, 2006, from http://www.wipro.com/distribution Leaver, S. (2005). Evaluating RFID middleware. Retrieved August 7, 2005, from http:// www.forrester.com/Research/Document/Excerpt/0,7211,34390,00.html Lyngsoe. (n.d.) Retrieved June 14, 2006, from http://www.lyngsoesystems.com Menezes, A., Van Oorschot, P., & Vanstone, S. (1996). Handbook of applied cryptography. London: CRC Press. O’Connor, M.C. (2005). Australia’s military to track supplies. Retrieved January 8, 2006, from http://www.rfidjournal.com/article/articleview/1838/1/1/ Potdar, V., Wu, C., & Chang, E. (2005, December 15-19). Tamper detection for ubiquitous RFID-enabled supply chain. In Proceedings of the International Conference on Computational Intelligence and Security, Xi’an, China. RFidGazzete. (n.d.). Tag shapes. Retrieved June 14, 2006, from http://www.rfidgazette. org/2005/10/tag_shapes.html RFID Journal. (2006a). Retrieved June 14, 2006, from http://www.rfidjournal.com/article/ articleview/208#Anchor-scanners-5989 RFID Journal. (2006b). Glossary results M-S. Retrieved June 14, 2006, from http://www.rfidjournal.com/article/glossary/3 RFID Journal. (2006c). RFID system components and costs. Retrieved February 13, 2006, from http://www.rfidjournal.com/article/articleview/1336/2/129/

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RMOROZ. (2004). Understanding radio frequency identification (RFID): Passive RFID. Retrieved June 14, 2006, from http://www.rmoroz. com/rfid.html RosettaNet. (2001). PIP™ specification— PIP3B4: Query shipment status. Retrieved June 14, 2006, from http://www.rosettanet.org Stallings, W. (1999). Cryptography and network security. Englewood Cliffs, NJ: Prentice-Hall.

Zebra. (2006). 13.56 MHz HF Zebra R-2844Z. Retrieved February 13, 2006, from http://www. zebra.com/id/zebra/na/en/index/products/printers/rfid.html

endnotes 1

2

Sweeney, P.J. (2005). RFID for dummies. Indianapolis, IN: Wiley Publishing. Symbol Technologies. (2004). Business benefits from radio frequency identification (RFID). Retrieved June 14, 2006, from http://www.symbol. com/products/rfid/rfid.html



3

Tecstra. (n.d.) RFID. Retrieved September 1, 2005, from http://glossary.ippaper.com/ Vajda, I., & Buttyan, L. (2003). Lightweight authentication protocols for low-cost RFID tags. In Proceedings of the 2nd Workshop on Security in Ubiquitous Computing (Ubicomp), Seattle, WA. Weis, S.A. (2003). Security and privacy in radiofrequency identification devices. Boston: Massachusetts Institute of Technology. Weis, S.A., Sarma, S.E., Rivest, R.L., & Engels, D.W. (2004). Security and privacy aspects of low-cost radio frequency identification systems. In Proceedings of Security in Pervasive Computing 2003. Wong, D.M.-L., & Phan, R.C.-W. (2006). RFID systems: Applications versus security & privacy implications. In G. Radhamani, & G.S.V. Radha Krishna Rao (Eds.), Web services security and ebusiness. Hershey, PA: Idea Group Publishing.

4

5

6

7

http://www.rfid-handbook.de/forum/read. php?f=4&i=81&t=81 RS-232 is a protocol for wired communication. Readers are connected using cables (max 30m) and the data transmission rate is very low. RS-485 is an improvement to RS-232, which still used cables, but can be large sized (1200m) and the data transmission rate is higher (2.5Mb per sec). Confidentiality refers to the confidentiality in communication between the tag and the reader. Integrity refers to the reliability of the information on the RFID tag. The RFID systems (in the UHF) work in a very congested frequency range; frequency jamming can easily attack such systems. Hence the availability of an RFID network is a security property which needs to be considered. One major security issue with the RFID tag is authentication. The data on the RFID tag like the unique identifier (UID) can be easily manipulated or spoofed, as these tags are not tamper resistant (Knospe & Pohl, 2004). Anonymity to undesired and anonymous scanning of items or people.

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Chapter 1.9

Contactless Payment with RFID and NFC Marc Pasquet GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie - CNRS), France Delphine Vacquez ENSICAEN, France Joan Reynaud GREYC Laboratory (ENSICAEN – Université Caen Basse Normandie - CNRS), France Félix Cuozzo ENSICAEN, France

IntroductIon The radio frequency identification (RFID) reading technology enables the transfer, by radio, of information from electronic circuit to a reader, opened up some interesting possibilities in the area of epayment (Domdouzis, Kumar, & Anumba, 2007). Today, the near field communication technology (NFC) opens up even more horizons, because it can be used to set up communications between different electronic devices (Eckert, 2005).

Contactless cards, telephones with NFC capacities, RFID tag have been developed in industry and the services (Bendavid, Fosso Wamba, & Lefebvre, 2006). They are similar, but, some major differences explain the specificity of these three applications and the corresponding markets. The label, or marker, is a small size electronic element that transmits, on request, its numerical identification to a reader. The RFID identification makes it possible to store and recover data at short distance by using

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Contactless Payment with RFID and NFC

these miniature markers or labels (see Figure 1) associated to the articles to identify. The cost of the label is only few centimes. An RFID system is made of labels, readers connected to a fixed network, adapted software (collection of information, integration, confidentiality...), adapted services, and management tools that allow the identification of the products through packing. Contactless smartcards (see Figure 2) contain a microprocessor that can communicate under a short distance with a reader similar to those of RFID technology (Khu-smith & Mitchell, 2002). The originality of NFC is the fact that they were conceived for the protected bilateral transmission with other systems. NFC respects the standarda ISO-14443 (Bashan, 2003) and thus, can be used as a contactless card. It can be used as a contactless terminal communicating with a contactless card or another NFC phone (ISO-18092). Services available through NFC are very limited today, but many experiments are in progress and electronic

ticketing experiences (subways and bus) started in Japanb. There are two types of NFC phones: •

•

The mono chip composed of only one chip for GSM services (called the SIM) and NFC services. In that case, an NFC service is dependent of the phone operator. The dual chip shows a clear separation of the two functions within two different chips. That completely isolates the operator and allows independent NFC services…

We define the technology standards, the main platforms and actors in the background section. The main trust develops some contactless payment applications, and analyses the benefits and constraints of the different solutions. The future trends section concerns the research and technology evolution in contactless payment applications.

background Figure 1. Some examples of RFI label The major interest of contactless cards is to facilitate access control, micropayment… Another interest refers to the usury of card; it is insensible to contact oxidation. We detail briefly the international standards that are involved in RFID and NFC.

standards ISO-14443 Figure 2. Example of a contactless bank card This standard is the international one for contactless smartcards operating at 13.56 MHz in close proximity of a reader antenna. This ISO norm sets communication standards and transmission protocols between a card and a reader to create interoperability for contactless smartcard products. Two main communication protocols are supported under the ISO-14443 standard: Type

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A and B. Other protocols were only formalized: Type C (Sony/Japan), Type D (OTI/Israel), Type E (Cubic/USA), Type F (Legic/Switzerland). This norm is divided in four parts and treats Type A and Type B cards: •

•

•

•

ISO-14443-1 defines the size and physical characteristics of the antenna and the microchip; ISO-14443-2 defines the characteristics of the fields to be provided for power and bi-directional communication between coupling devices and cards; ISO-14443-3 defines the initialization phase of the communication and anticollision protocols; ISO-14443-4 specifies the transmission protocol.

ISO-14443 uses different terms to name its components: • •

PCD: proximity coupling device (or reader); PICC: proximity integrated circuit card (or contactless card).

ISO-18092 NFC is a short-range (10 to 20 centimeters) wireless communication technology that enables the Figure 3. The two NFC communication modes

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exchange of data between devices over a short distance. Its primal goal is the mobile phones usage. This open platform technology is standardized in ISO-18092 norm NFC Interface protocol-1c. In NFC technology, two communication modes exist: passive and active communication modes of NFC interface protocol to realize a communication network using NFC devices for networked products and also for consumer equipments (see Figure 3).

ISO-21481 The ISO-21481 standard (NFC interface protocol2d) is derived from Ecma-356 (interconnection) standard. It specifies the selection mechanism of communication mode in order to not disturb communication between devices using ISO-18092, ISO-14443 (contacless interface - proximity), and ISO-15693 (contacless interface - vicinity).

Application Platforms and Major actors There are major actors in the field of contactless applications; we distinguish two important platforms using the contactless technology: Mifare and FeliCa. This chapter does not focus on more details about these platforms technology, but is more about their applications.

Contactless Payment with RFID and NFC

Figure 4. Payment with a contactless card

MaIn focus of the chapter The main focus of the chapter is an analysis of the benefits and limitations of RFID authentication for electronic payment (Tajima, 2007). This part deals with the particular constraints of banking (computation time, security…) for this kind of authentication process (Chen & Adams, 2004). The use of radio frequency and the small distance allows some security weakness that leads to security reinforcements.

contactless cards in banking Applications Current actors in payment applications, namely MasterCard and Visa, stay alert, and intend to play a major role in future payment applications. They have already joined the movement and launch many developments over contactless payments. They begin to agree to a common communications protocol for contactless payment devices. This is based on the MasterCard PayPass™ protocol. MasterCard made the first step with a contactless credit card (see Figure 4) (Olsen, 2007). The Visa PayWave technology is rather largely deployed within many European countries. They both intended the American market to future deployments (Turner, 2006). Visa and MasterCard technologies comply with the EMV (Europay Mastercard Visa) standard. This standard defines the interoperation between smartcards and terminals for authenticating credit and debit cards. It defines strong security measures and provides a strong authentication along the process. Mobile specifications are still in an early stage of development. Those who want to follow the development can do it at the EMVCo Web sitee. Contactless cards that define the EMV standard over contactless communication does not differ so much with contact cards. The differences will be in the usages and applications.

We have seen that MasterCard and Visa have an agreement to share a common transmission protocol and experimentation for the contactless payments by radio frequency in the points of sale. Contactless payments, as conceived in the programs MasterCard PayPass and Visa PayWavef, make it possible for the cardholders to carry out fast payments by a simple passage of their card in front of a terminal, thus, avoiding them giving their payment card to a merchant or handling cash. Contactless payments are much more practical for the consumers and are particularly adapted in environments of purchase where the speed is essential, like fast food, the gas station, but also theaters. They also offer new appropriate payments by using a card in unusual environments of purchase, like slot-machines or tolls. To make a payment, a user presents his/her card near the front of a terminal (a beep is emitted by the terminal). A request for an online authorization is sent. The payment is carried out. There exist two types of PayPass cards: • •

Contactless with a magnetic stripe ; Contactless with a chip that is EMV compliant (dual-use card).

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Contactless Payment with RFID and NFC

For the European market, Visa is planning on using RFID-enabled dual-use debit cards, based on its own Visa Contactless payment technology. It aims, in particular, at European countries already using EMV compliant cards. But, Visa is also understood to be in talks with mobile manufacturers to use NFC technology that will enable a phone to be used instead of a card. For the US market, the contactless PayPass is not EMV compliant, so, the target is to limit the authorization requests (see Figure 5). How does it work? •

•

•

For a small amount (as for illustration 268 Million)

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Product 24 bits (> 16 million)

Serial Number 36 bits (> 68 billion)

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structure, and thus, manufacturers should not have to worry about running out of EPC numbers for unique identifiers for each of their product types for many decades or more. The EPC data structure can generate approximately 33 trillion different unique combinations, which according to Helen Duce of Cambridge University, would be enough to label all of the atoms in the universe (cited in Anonymous, 2003, p. 39). In fact, according to projections from the National Research Council’s Committee on Radio Frequency Identification Technologies (2004), this will allow for each of the billions of people on earth to have billions of tags each. This can be contrasted with the 12-bit structure of the current UPC data structure. As can be seen referring back to Figure 2, there is a “memory” limitation on bar codes, as with their coding structure, they can identify “only” 100,000 products for each of 100,000 manufacturers. As Parkinson (2003) points out, this may simply not be enough for companies operating in the global, modern economy. The EPC framework outlines six classes of tags, with an ascending range of capabilities. These are outlined in Table 3. The EPC has advanced to a second generation of RFID technology, dubbed “Gen2.” The Gen2 standard allows for greater interoperability of

RFID tags and readers, less collision with wireless devices, increased read rates and enhanced security protocols (Borck, 2006).

What is an RFID Reader? RFID tags are read by a device known as an RFID reader. These readers have three essential components. These are the: • • •

Antenna Transceiver Decoder

RFID readers, which are also referred to as interrogators, can differ quite considerably in their complexity, form, and price, depending upon the type of tags being supported and the functions to be fulfilled. Readers can be large and fixed or small, hand-held devices. However, the read range for a portable reader will be less than the range that can be achieved using a fixed reader, as the effective read range is determined by the size of the antenna, the efficiency of that antenna, and the power of the transmitter. Readers can have a single antenna, but multiple antennas allow for: greater operating range, greater volume/area coverage, and random tag orientation. The reader, either continuously (in the case of a

Table 3. EPC tag classes EPC Tag Class

Tag Class Capabilities

Class 0

EPC number is factory programmed onto the tag and is read-only

Class 1

Read/write-once tags are manufactured without the EPC number (user programmable)

Class 2

Class 1, plus larger memory, encryption, and read/write capabilities

Class 3

Class 2 capabilities, plus a power source to provide increased range or advanced functionality (such as sensing capability)

Class 4

Class 3 capabilities, plus an active transmitter and sensing

Class 5

Class 4 capabilities, plus the ability to communicate with passive tags (essentially a reader)

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fixed-position reader) or on demand (as with a hand-held reader), sends out an electromagnetic wave to inquire if there are any RFID tags present in its active read field. When the reader receives any signal from a tag, it passes that information on to the decoding software and processes it for forwarding to the information system it is a part of. Recently, it has been forecast that very soon, RFID readers will not be just distinct, dedicated devices. Rather, RFID reading capabilities will soon be capable of being integrated into cell phones, PDAs (personal digital assistants), and other electronic devices, technology that is being tested even today (Thomas, 2005).

“What’s the Frequency, Kenneth?” Frequency designates the intensity of the radio waves used to transmit information. Frequency

is of primary importance when determining data transfer rates (bandwidth), in that the higher the frequency, the higher the data transfer rate. In principle, any RF system works much akin to your car radio (assuming you don’t have satellite radio!). For instance, all FM radio stations in the United States must operate between 88 and 108 MHz. Thus, if you are currently tuned to 97.1 FM, it means that your radio is tuned at the moment to receive waves repeating 97.1 million times per second. There are four common frequencies used in RFID systems. Each of the four frequencies has its own properties, and there are a variety of reasons why each is used in specific applications. An overview of the characteristics of each frequency range is provided in Table 4. While work is progressing to harmonizing world standards in each of the four frequency ranges, frequency

Table 4. Characteristics and applications of RFID frequency ranges Frequency Band

Read Range/Speed

Example Applications

Low (LF) 100-500 kHz (Typically 125 to 134 KHz worldwide)

• Short read range (to 18 inches) • Low reading speed

• • • • •

Access control Animal identification Beer keg tracking Inventory control Automobile key/ antitheft systems

High (HF) (Typically 13.56MHz)

• Short to medium read range (3 – 10 feet) • Medium reading speed

• • • • • • •

Access control Smart cards Electronic article surveillance Library book tracking Pallet/container tracking Airline baggage tracking Apparel/laundry item tracking

Ultra High (UHF) 400-1000 MHz (Typically 850950 MHz)

• Long read range (10-30 feet) • High reading speed

• Item management • Supply chain management

Microwave 2.4-6.0 GHz (Typically 2.45 or 5.8 GHz)

• Medium read range (10+ feet) • Similar characteristics to UHF tags, but with faster read rates

• Railroad car monitoring • Toll collection systems

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restrictions imposed by governments around the world have been a significant obstacle facing RFID development (Moore, 2003). For instance, while Europe uses 868 MHz for UHF systems, and the U.S. uses 915 MHz. Japan and China currently do not allow any use of the UHF spectrum for RFID. National governments also regulate the power of the readers to limit interference with other devices (Fox & Rychak, 2004). The radio frequencies involved in RFID are all in the safe range. 13.56 MHz is between the AM and FM frequencies that have been used for years in commercial radio transmissions, without any known problems. The maximum power level in the United States and most countries is limited to 4 watts. 915 MHz is around the analog cell phone spectrum and has not been found to cause any health concerns at levels below one watt. 2.45 GHz is around the frequency of the newer digital cell phones. At 1 watt or less, there have been no proven health concerns (Wyld, 2005a). The read range refers to the working distance between a tag and a reader. The range that can be achieved in an RFID system is determined by five variables. These are: 1. 2. 3. 4. 5.

The frequency being used. The power available at the reader. The power available within the tag. The size of the reader and tag antennas. Environmental conditions and structures.

As seen in Table 5, higher frequencies tags have far greater read ranges than tags operating at lower frequencies. This is because all things being equal, power is the key element in this process. In the previously described energy harvesting technique that is employed to power passive tags, it is important to note that the process only returns the signal with a fourth of the power transmitted to power it up. Thus, with the relative—and unavoidable—inefficiency of the process, in order to double the read range, the power used must be increased 16 times (Committee on Radio

Frequency Identification Technologies, National Research Council, 2004). Finally, as is the case with so many technologies, while the physics are relatively simple, the devil is in the details to get readers and tags to properly communicate. While the goal for the technology to be “automatic” and hands-off necessitates 100% read rates, such has not always been the case in pilots and early implementations (Sliwa, 2005). There are several variables that can dramatically affect read rates in practice. These include: tag selection and placement, antenna selection and placement, and reader (interrogator) settings (Sirico, 2005). According to Clarke (2005), it must be remembered that experiments and pilots of tags and readers in controlled circumstances “represent the best possible scenarios for readability” and, as the shift is made to an actual warehouse conditions and higher quantities/higher speeds, readability can be significantly challenged” (n.p.). There are many seemingly extraneous factors which can complicate the reading process. Firstly, when metal or water is present, either in the item itself or in the reading field, this can cause significant declines in read rates. This is because liquids absorb radio waves and metal reflects them. Much has been written about the technical problems of dealing with both problems. For instance, when dealing with the tagging of aircraft parts (Wyld, 2004b) and even luggage tracking (Wyld, 2005b), the metals present in the aircraft must be taken into consideration. Likewise, there are problems in dealing not just with the presence of water and humidity in the environment, but high water content in the packaging and in the items being tagged, labeled. These include, but by no means are limited to: • • • •

fruits (Maenza, 2005) vegetables (Downey, 2006) beer (Roberti, 2005) wine (Wyld, 2005c)

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In any setting in which RFID is used, there is the potential for radio signal interference to occur. When this happens, the read rates—and therefore the functionality of the system—can be hampered on anything from a minor to catastrophic level. For instance, Douglas Martin, an Executive Consultant with IBM Global Services, observed that in IBM’s work with Wal-Mart on a pilot project involving the back-rooms of seven stores’ grocery operations, IBM consultants experienced interference from a number of sources. These included: walkie-talkies, forklifts, cell phone towers, and bug zappers (Sullivan, 2004a). Likewise, Hewlett-Packard has reported that in some cases, when HP’s forklift drivers would use their cell phones, this would cause misreads of RFID tags (Albright, 2005a). Finally, there is the simple matter that sometimes, the people element comes into play, as workers need to be informed that it is important that they drive the forklift at a certain speed past a certain point or apply a smart label at a precise location on a carton, in order for the RFID tags to be read properly. In the end, making RFID systems work in practice—meaning produce 100% read accuracy—is thus a complex matter. In fact, L. Allen Bennett, the President and CEO of System Concepts, an RFID integrator providing services to the Social Security Administration and other organizations, provided an apt analogy when he stated, “It’s a little like Chinese cooking,” in that all the ingredients have to be prepared “right” and be combined in the proper manner (quoted in Olsen, 2005, n.p.). Every location where RFID is to be used and every item to be read by RFID thus presents its own unique set of circumstances. Thus, at present, there is no “one best way” to accomplish RFID, whether your setting is a distribution center, an airport, a hospital, a parking lot, or a retail location. Carey Hidaka, an RFID specialist at IBM Global Services, observed that “in many ways, these (RFID) deployments are more art than science, although the science is very important.” Hidaka stressed that when working with RFID, it is vital

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to remember that “these are not plug-and-play systems” (quoted in Albright, 2005b, n.p.).

the Move to rFId In the publIc sector The push for RFID has been propelled by the mandates that have been issued for the use of the technology in the supply chain. Various retailers, both in the U.S. and abroad, have issued RFID mandates, including: • • • • • •

Wal-Mart Target Best Buy Albertson’s Metro (Germany) Tesco (United Kingdom)

The early results found by Wal-Mart have shown marked improvements in supply chain management and item availability (Wyld, 2006a, 2006b). In contrast to the American approach that has leading organizations mandating the use of RFID in their supply chains, the European marketplace has indeed seen a more collaborative approach being taken between large retailers and their major suppliers in the case of Metro and Tesco (Goodman, 2005). However, it is the U.S. Department of Defense (DoD) that has issued the largest and most sweeping RFID mandate. While the RFID mandates from Wal-Mart, Target, Albertson’s, and other retailers will be important, the Defense Department’s RFID mandate is far more reaching than that of any retailer, due to the sheer size and scope of the military supply chain. The U.S. military’s supply chain is a worldwide operation, which moves almost $29 billion worth of items worldwide each year. The military supply chain is not just bullets, bombs, and uniforms, as it involves a wide panoply of goods, the majority of which are consumer goods as well. The DoD’s direc-

The Little Chip That Could

tive will ultimately affect approximately 60,000 suppliers, the vast majority of which are not the Lockheed’s and Boeing’s of the world, but small businesses, many of which employ only a few people (Wyld, 2005d). As such, the DoD’s RFID mandate has been rightly categorized as the likely “tipping point” for widespread use of RFID in supply chains (Roberti, 2003). As of the end of 2006, the DoD will have all 19 of its centralized distribution depots in the U.S. mainland RFIDenabled (O’Connor, 2006a).

an rFId agenda For governMent At this early stage in the widespread use of RFID technology, there are far more questions than answers, far more pilots than implementations, and far more interested observers than users of RFID. Indeed, we are early on in the lifespan of RFID technology. In fact, many leading industry experts expect full-fledged implementation of RFID to take 10-15 years, or more (Emigh, 2004). According to Amar Singh, Vice President of SAP’s Global RFID Initiative, observed that at present, no one knows the what the true and lasting impact of RFID on the overall business of companies yet simply because “no one has done it yet” (cited in RFID News & Solutions, 2004, p. R8). However, as Alan Estevez, the Assistant Deputy Under Secretary for Supply Chain Integration for the Department of Defense bluntly put it: “Here’s the real lesson learned: the technology works” (quoted in Albright, 2005a, n.p.). However, there are several areas where government can aid in the progress of the RFID revolution. These are in the areas of: • • • • •

Best Practices Standards Research Education Privacy

In many instances, these efforts should be, by design, joint undertakings by the public and private sectors, due to the fact that it may be hard to separate the visible hand of government from the invisible hand of the economy in regard to many aspects and applications of this technology. National and even provincial/state governments are taking an even more direct role in the promotion of RFID technology, including: • • • • • •

China (O’Connor, 2006b) United Kingdom (AIM Global, 2003) Singapore (Shameen, 2004) South Korea (Ilett, 2005) Scotland (O'Connor, 2005a) Victoria (Australia) (Anonymous, 2005a)

best practices If we look at what is necessary for RFID to be tried, tested, and evaluated in the public sector, the primary need is for executive leadership. Champions must emerge at all levels of government who are willing to set a path toward RFID. Perhaps the greatest function that the public sector can serve is that of a testing ground for RFID technologies, and champions need to emerge who are willing to evaluate if the technology can improve their operations. In May 2005, the Government Accountability Office (GAO) (2005) issued a comprehensive survey of RFID interest and use across the federal sector, looking for planned and pilot uses beyond the Department of Defense. The GAO found that there were 28 planned or active RFID projects across 15 cabinet-level agencies. The GAO’s findings are summarized in Table 5. One example of a successful RFID program beyond the Department of Defense in the federal government is that of the Social Security Administration (SSA). The Social Security Administration has been a progressive federal agency in the use of RFID. The SSA piloted RFID in 2003 in its internal office supply store, individually tagging

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Table 5. Federal agencies reported or planned use of RFID technology Agency

Application

Department of Agriculture

• Animal identification program

Department of Defense

• Logistics support • Tracking shipments

Department of Energy

• Detection of prohibited articles • Tracking the movement of materials

Department of Health and Human Services

• Physical access control

Department of Homeland Security

• • • •

Department of Labor

• Tracking and locating case files

Department of State

• Electronic passports

Department of Transportation

• Electronic screening

Department of the Treasury

• Physical and logical access control • Records management (tracking documents)

Department of Veterans Affairs

• Audible prescription reading • Tracking and routing carriers along conveyor lines

Environmental Protection Agency

• Tracking radioactive materials

Food and Drug Administration

• Tracking pharmaceutical drugs for product integrity/ anticounterfeiting

General Services Administration

• Distribution and asset management • Identification of contents of shipments • Tracking of evidence and artifacts

National Aeronautics and Space Administration

• Hazardous material management

Social Security Administration

• Warehouse management

Border control Immigration and customs Location systems Tracking and identification of baggage on flights

items and issuing RFID-enabled shopping cards to allow for automatic reconciliation of “shopping” activity. In the store operations, tagged items could be scanned at checkout, and the system provided greater inventory accuracy and enabled automatic reordering (Albright, 2004b). The SSA has now also implemented RFID in its warehouse management (for forms, flyers, supplies, etc.). In the SSA’s warehouse operations: •

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98% of the orders are now processed within eight hours.

• • • •

•

Order processing time has been reduced from 45 days to 3. Order backlogs have been eliminated. Picking has increased from 500 lines per day to 1,500. Fill rates of 94% on normal orders and 98% on emergency orders have been accomplished, both with minimal safety stock. The agency has been able to reduce its warehouse space by 60,000 square feet through inventory optimization (Anonymous, 2005c; Olsen, 2005).

The Little Chip That Could

From the perspective of SSA Project Manager Gary Orem, the agency has been able to dramatically improve the accuracy of its inventory data, while providing labor savings on the order of 70%. According to Orem, “Initially, the SSA warehouse and supply chain operations were done manually and very labor intensive, which resulted in system inaccuracies and delays in getting product to our customers. The agency reaped significant benefits—including more production with less staff …(and) an annual savings of $1 million” (quoted in Anonymous, 2007, n.p.). Apart from its warehouse operations, the SSA has employed RFID for fleet management of an 86-vehicle pool, which receives over a thousand use requests monthly. The system employed RFID for key-management systems, and it provided greater availability of pool vehicles, while making for cost-operational efficiencies (Burnell, 2004). RFID has been demonstrated by public sector users to be able to improve the tracking of both critical things and even critical people. In the former category, there are several exemplary public sector examples of RFID pilots and implementations that have already taken place. These include: • • •

Library materials Court documents and evidence Hazardous waste

Libraries have been at the vanguard of implementing RFID-based tracking, inventory, and check-out systems. For instance, in Virginia Beach, Virginia, the public library system in investing $1.5 million in an RFID-based inventory system and placing tags (at a cost of 50 cents each) in each of approximately 800,000 items at nine library locations (Sternstein, 2005b). Likewise, in suburban Frisco, Texas, the library system is outfitting its libraries with a similar RFID-based tracking system (Anonymous, 2005d).

The interest in RFID systems for libraries is high. This is because in library operations, RFID can: • •

• • •

Enable self-checkout. Reduce the time librarians spend handling materials (by as much as 75% in Frisco, TX). Enable library staff to more easily locate lost/misplaced items; Reduce repetitive stress injuries. Empower librarians to engage in more “value-add” services with library patrons (research assistance, storytelling, etc.) (Wyld, 2005a).

Dr. Ron Heezen, Director of the Frisco Public Library, commented that: “We wanted to redesign our library for the next generation, as it became very clear to me that all public libraries will have to make do with fewer employees and tighter budgets in the future” (quoted in Anonymous, 2005d, n.p.). Yet, there is a civil liberties aspect to implementing the technology in public libraries. In fact, privacy concerns led the San Francisco Board of Supervisors to deny funding in July 2005 for an RFID system that would have replaced bar code based tracking at 12 of the 28 branches of the San Francisco Public Library. Similar concerns were also brought to the fore across the bay when the Berkeley Public Library actually installed an RFID pilot system in 2004 (O’Connor, 2005b). One of the most promising applications of RFID technology is in the area of file tracking. While many government agencies are attempting to move to a paperless environment, the fact remains that for most, the ubiquitous manila folder is at the heart of their operations. However, building upon systems designed for law firms to better organize and track their files by employing RFID tagging, legal agencies are taking the lead in the public sector to better manage their paper files.

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The DeKalb County, Georgia Juvenile Court works with more than 9,000 children in their system annually. To do so, the court system faces the task of tracking over 12,000 manila file folders. DeKalb County estimates that on average, clerks spend about 10 hours each week simply searching for lost files. According to Juvenile Court Judge Robin Nash: “We have about 2,200 cases of neglect investigated every year, and between 1,100 and 1,200 kids in foster care at any given time. My assistant spends about two hours daily trying to track down files on the three floors of the courthouse, and we believe the RFID system will become a huge labor savings” (cited in Sullivan, 2005a). Thus, DeKalb County is spending $50,000 to tag file folders with RFID-labels and equip clerks with desk-mounted and handheld readers. DeKalb County is projecting that the payback on this system will come within 2 years, as it estimates that the reduction in lost files will save the Juvenile Court approximately $30,000 each year. When a new, far larger courthouse opens later in 2007, DeKalb County plans to outfit the building with an RFID file-tracking system throughout the facility (Sullivan, 2005a). Across the country, similar results have been achieved in two reported installations of RFIDbased file tracking systems in both Marin County, California, and Maricopa County (Phoenix), Arizona. Using 13.56 Mhz tags embedded in file labels, the tracking capabilities enable employees to track files. The main benefit of the file tracking system, in the view of Marin County District Attorney Ed Berberian Jr., is that it dramatically cuts down on the wasted time in locating misplaced and lost files, a cost his office estimates to be approximately 2,500 man hours per year. If an employee should not be able to locate a file in their office, the 3M system allows employees to use handheld devices to track the wayward file down. Likewise, in Marin County, staff members routinely screen each of the forty attorneys’ offices several times a week to catalog the files in their possession. The systems employed in both

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jurisdictions enable employees to be alerted if a file is physically misfiled or placed out of order in a storage drawer or file cabinet. They also use reading pads that can successfully scan a stack of files a foot high (Swedberg, 2005)! With a high critical value, RFID has proven to have significant potential in improving the handling of the most critical things, like hazardous waste. For example, the Department of Energy is currently overseeing the clean-up of the Hanford Nuclear Site, the former plutonium production facility in Washington State. With its private contractor, Bechtel Hanford, the DOE is transporting 4,000 tons of radioactive waste daily from around the 586-square-mile, 200-mile-long Columbia River Corridor area to a central landfill facility. Prior to the May 2005 introduction of the RFID-based system, as the trucks bearing the hazardous waste were weighed prior to entering the landfill, operators had to manually key in the identity codes for both the truck and each of the up to ten steel cans bearing tons of waste for over 200 truckloads daily. The system utilizes active tags operating at 315Mhz, with a range of 100 feet. Steve Teller, who directed the RFID deployment for Bechtel Automation Technology, reports that the system is presently achieving a 98% read rate, in spite of the challenge of dealing with the metal cans. Teller stated that: “We use cans with four different designs. If you look at them, you wouldn’t think the designs are very different, but those little differences become big differences when you’re using RFID, because the radio waves are bouncing off everything” (OConnor, 2005c, n.p.). At the Dryden Flight Research Center at Edwards Air Force Base in California, a team of NASA innovators has shown how RFID can be combined with sensor technology to enhance the safety of handling hazardous materials. Ralph Anton, NASA Dryden’s Chemical Program Manager, commented that: “When we heard about RFID, we saw its potential. But instead of just producing a PowerPoint slide show of what RFID

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could enable, we went ahead and developed a working solution to prove it” (quoted in Collins, 2004, n.p.). Their pilot in late 2004 demonstrated how RFID tags, augmented by temperature sensors, could be used to monitor the proper storage of hazardous chemicals in one of Dryden’s five storage facilities. The system developed by the NASA engineers tied-in to the existing hazardous materials management system, enabling alerts to be triggered if a tagged chemical bladder, container, or cardboard box holding a chemical was moved, stored incorrectly, or reached a threatening temperature. The Dryden test showed that safety could be enhanced while producing labor savings in the physical monitoring of the hazardous chemicals. Perhaps even more importantly, the pilot demonstrated that RFID-labeling could enable emergency responders could more quickly access information on the chemicals they were dealing with, should an incident occur off-site (Collins, 2004). The initial NASA test further demonstrated the value proposition for RFID in the handling of even nonhazardous chemicals, and the agency is planning to extend it to cover all chemical storage at the Dryden facility. According to NASA’s Anton: “Storing at the correct temperature can extend the useful life of chemicals. Given that for every $1 spent buying a chemical it costs about $10 to dispose of it, monitoring the temperature can save the government money in future” (cited in Collins, 2004, n.p.). RFID can be also be used to track the most critical possible thing, namely people. In Mexico, Attorney General Rafael Macedo de la Concha made headlines last year when it was announced that he and 160 federal prosecutors and drug investigators were implanted with subcutaneous RFID chips to provide the most secure access possible to Mexico’s new federal anticrime information center. The number of chipped officials in Mexico reportedly grew to include key members of the Mexican military, the federal police, and even staffers in the office of Mexico’s then President, Vicente Fox (Anonymous, 2004b).

While nothing like this would be a widespread practice for government in this country, due to the obvious civil liberty concerns, there are several exemplary public sector examples of the use of external RFID tags for what might be categorized as “critical people.” First, borrowing from the same RFID concepts that have been successfully used at theme parks (Dignan, 2004) and sports venues (Wyld, 2006c) with RFID-enabled smart bands, patients can be tracked using RFIDequipped smart brands or bracelets. For instance, the U.S. Navy used smart bands to identify the wounded aboard hospital ships in the Iraq War in 2003, replacing the “Civil War technology” of tracking patients through the use of paper-based charts (Ewalt, 2003). The Los Angeles County Sheriff’s Department has earmarked $1.5 million dollars to a program to monitor inmates, using active RFID bracelets. The sheriff’s office feels that the technology is a good investment, due to the fact that it will aid in enhancing security and in decreasing violent incidents (Sullivan, 2005b). Finally, after pilot testing an RFID-enabled access card program at the Marshall Space Flight Center in Alabama, National Aeronautics and Space Administration (NASA) plans to implement a system to deploy 100,000 “smart cards.” With RFID-enabled access cards, NASA hopes to achieve improved security and access control (Bacheldor, 2004). In sum, the government—at all levels—should be a test-bed for RFID technologies. Much of the message today is that it is important to begin to experiment with, to pilot, and to plan for implementation of RFID, even if the business case for doing so can be categorized as being “fuzzy.” In the view of the President of EPCglobalUS, Mike Meranda: “You learn by doing, even though the technology is not perfect” (quoted in Albright, 2005a, n.p.). Speaking in April 2005, Former U.S. Department of Homeland Security Secretary Tom Ridge commented that: “The return on investment most businesses want is a little bit different than the return on investment you

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might find in the public sector....This (RFID) will improve efficiency. This will improve accountability. This will improve the bottom line. And, oh, by the way, as a direct consequence, this will also enhance security” (quoted in Wasserman, 2005a, n.p.). Thus, government can and should undertake RFID projects that may produce ROI over a longer period than could be done by their private sector counterparts, and work to “push the envelope” and expand the knowledgebase on RFID technology in the process. We have also seen the federal government become an active partner in industry-wide initiatives to make use of RFID to improve supply chain security, including animal identification (Wyld, 2006d) and pharmaceuticals (Wyld & Jones, 2007). RFID should be viewed as part of a larger wave of wireless technologies that are fast-becoming a significant part of the governmental IT market. Writing in Washington Technology, Welsh (2005) observed that “RFID is quickly moving from being viewed as a standalone technology to one that can be blended with complementary technologies into more robust solutions” (n.p.). However, he also cautioned that it will be difficult for governments to pursue these solutions when they are struggling with their own budgets. Thus, it is very likely that there will be a role for the federal government to play in encouraging governmental applications of RFID technology at the state and local level. This could be done through grant programs and demonstration projects from various agencies. However, as we have seen the potential for government to aid in programs that foster both technological and economic growth (such as President Bush’s hydrogen car initiative), the stage would seem to be set for a wider RFID initiative at the federal level. Such a Presidential initiative would certainly place RFID on the national agenda, and spotlight its potential to find new ways to do things in better ways, and create companies, jobs, and technological progress in the process.

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Privacy is a huge issue as RFID moves forward. As Paul O’Shea (2003) reminds us, RFID is a technological tool, and “as with all technology, it can be used to manipulate our world or be abused for unwarranted control” (n.p.). The fears of a “Big Brother” use of the technology are widespread. It is only inflamed by references to the Biblical “Mark of the Beast” (Jones, 2005, April 3) and to Orwellian popular culture examples, such as in the movies A Beautiful Mind and Minority Report. From a procedural perspective, privacy must be a consideration in all federal RFID initiatives. The E-Government Act of 2002 requires that each agency must undertake a privacy impact assessment (PIA) when they decide to employ undertake an information technology project or redesign a business process to incorporate new technologies. These PIAs must be published and made available to the public. Thus, in the view of Kenneth Mortensen, an attorney with the U.S. Department of Homeland Security’s (DHS) Privacy Office who spoke on the subject at a privacy forum in July 2005, “We have privacy baked in” on any federal RFID project. He cited as an example the DHS US-VISIT (United States Visitor and Immigrant Status Indicator Technology) program, which will incorporate RFID and biometric technology at border crossings with Canada and Mexico. His office filed the PIA for the project in January 2004 and continues to work with project managers and technologists, asking questions like: “’What is the purpose?’...and ‘Why am I using or collecting or storing this information?” to help project teams incorporate privacy considerations into their proposed designs and solutions (quoted in Wasserman, 2005b, n.p.). Certainly, privacy will continue to be a huge issue in the development of RFID, especially as the technology begins to migrate to the consumer level. We have seen this again and again, from when the first shoppers began encountering RFID tags at Wal-Mart’s Sam’s Wholesale Clubs in Texas (Sullivan, 2004b) to the recent controversy over

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the U.S. State Department’s attempts to place RFID chips in U.S. passports (Zappone, 2006; Wyld, 2005e). It will behoove both those in the public and private sector to maintain a “finger on the pulse” of the public and workers to gauge their understanding of and misapprehensions about the capabilities of RFID technology. However, there is a significant risk to have policy go beyond protecting individual rights and hamper the full use and deployment of the technology, which could perhaps delay or make impossible breakthroughs that could aid the public in retail, health care and many other arenas of their lives. Further, with significant concern over identity theft and other forms of hacking, as cases of such are reported with RFID (Hesseldahl, 2004), calls for encryption and other forms of protection for RFID tags may be furthered.

CoNClUSIoN: “UNClE SAM’S GUIDING hAND” For at least a decade to come, we are likely to see the U.S. government’s investment in automatic identification technologies and its formal and informal mandates create profound changes in the way all of us conduct business and even live our lives. If so, it would be history repeating itself. When we look at federal programs, the direct ROI for the spending is often miniscule when compared to the spin-off effects of the technological developments. This has occurred several times over the past few decades, including: • •

•

The 1960s and 1970s with NASA and the space program; The 1980s and 1990s with the Defense Department and ARPANET, which laid the foundations for the Internet; and The present, post-September 11th environment, where the push for greater homeland security is leading to large investments in wireless technologies, scanning, imaging,

and data mining, which is already producing technology transfer to private enterprise and public benefit. A couple of years ago, noted futurist Paul Saffo (2002), the Director of the Institute for the Future, characterized RFID as being this decade’s entry into the pantheon on the new technologies that have come along to reshape the information technology landscape. As Dan Mullen, President of AIM Global, put it: “The government was a huge driver in the development of the bar code market, and there is an incredible amount of parallel in how the RFID market is developing.” As such, the government “can serve as a model for others who want to explore new opportunities to improve” (quoted in Burnell, 2004, p. 16). Likewise, the Department of Defense’s commitment to be an early adopter of RFID technologies throughout its complex, worldwide, multilayered supply chain is likely to advance not just the pace of automatic identification technology development, but the scope, standardization and utility of RFID technologies in general. In the end, we may well judge that the U.S. government’s push for RFID technology—through both the military’s mandate and the host of other mandates and initiatives across federal agencies—will perhaps be the key driver to make automatic identification a reality throughout consumer-facing industries in the very near future. Writing on the subject of “Uncle Sam’s Guiding Hand,” Greenemeier (2004) argues that the government’s role in technology adoption can be a complex one, which both fosters and retards innovation. He shows that throughout history, government—by issuing mandates, by acting as a major purchaser, and by helping to set standards— can be a positive force in advancing technology. However, if regulations and mandates are too constrictive, government can stifle innovation and actually slow the progress and acceptance of the technology. Dave Wennergren, The Navy’s Chief

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Information Officer, remarked that the proper role of government when technology is new and in flux is a leadership position, helping “to help make some order out of chaos.” Wennergren points out that “in a networked world, government can use its size in a united way” to advance the technology and set standards (opinion cited in Hasson, 2004, n.p.). In the end, the current push for RFID may be a small part of a larger mosaic. Indeed, Paul Saffo foresees that much of the focus on RFID today is on doing old things in new ways, but the truly exciting proposition is the new ideas and new ways of doing things that will come from RFID. RFID makes possible “an Internet of things” (Schoenberger, 2002) or a “wireless Internet of artifacts” (Gadh, 2004), Saffo sees RFID as making possible what he terms “the sensor revolution.” This is based on viewing RFID as a media technology, making it possible for what he categorizes as “’smartifacts’ or intelligent artifacts, that are observing the world on our behalf and increasingly manipulating it on our behalf.” Saffo thus stresses the importance of thinking outside the box on RFID and looking beyond today’s problems to find “unexpected applications,” which is where “the greatest potential for RFID lies” (quoted in O’Connor, 2005d, n.p.). Indeed, Saffo urges people to take a 20-year perspective on RFID, believing that we are in the early stages of “a weird new kind of media revolution,” in that “RFID will make possible new companies that do things we don’t even dream about” (quoted in Van, 2005, B1). If we indeed take the long-view of history, we can see that some of today’s biggest industries, most pedestrian technologies, and most indispensable parts of our lives come from sparks of imagination on how to use a technology in unimagined ways. Indeed, we have seen bar coding itself used in applications far beyond the supply chain functions it was created for (Brown, 1997). Who would have dreamed that GPS systems would today be routinely used by business

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executives, lost in their rental cars in big cities, and by fisherman and hunters on the bayou? Who would have dreamed that people around the globe, from Moscow, Russia to Moscow, Idaho and every place in between, would have their own cell phone? In the 1950s, when people gathered around a lumbersome black and white television to watch “I Love Lucy,” who could have dreamed of a 500-channel universe? Could anyone at DARPA have envisioned the multitude of opportunities for companies such as eBay, Amazon, and Google that would be spawned by the Internet? As with the RFID-enabled golf balls discussed earlier in this chapter, we are today seeing the first fruits of this “weird” new media revolution that RFID is sparking. There will undoubtedly be an “RFID Revolution.” How far and how fast it will move, and at what cost (both in dollars and privacy) and benefit (to the contactless of our commerce to our own health) remain to be seen. The trajectory and the timeline for this revolution may be uncertain, and the ultimate scale of RFID’s impact on business, society, and indeed our everyday lives spans a very wide margin of error (from minor conveniences to total transformation). Yet, we can benefit today from being cognizant of the long view of history, knowing how the government has played a role in advancing other communications media. Thus, the public sector should have a different ROI equation on its RFID investments than any private sector entity can—and perhaps even should—have at present. While companies such as Wal-Mart can look at their internal investments in RFID and hope that others in its supply chain will follow, they must ultimately make their decisions on RFID on what is going-on within their own four walls. Yet, we ultimately do not—and really cannot—live in a “slap and ship” world. In supply chains, the move has been toward greater integration and cooperation. Likewise, the power of the network has been shown over and over again, with the Internet, the Web, mobile telephones, electronic media, and

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so forth. As shown in Figure 4, the larger the net cast by RFID becomes (linking organizations internally and their external supply chain partners, both upstream and downstream), the higher the overall value proposition becomes. RFID thus presents a classic “chicken and egg” problem, in that wider RFID adoption will likely lower the costs associated with RFID and markedly increase the beneficial aspects. The author of a recent book on RFID, Steven Shepard (2005) categorized the current supply chain as operating under what he aptly described as “The Kevin Costner Effect.” Adapting the famous line from the movie Field of Dreams, he described the layers of players in the supply chain as operating under the philosophy that, “If you build it, they will come” (p. 7). In the long-view then, it is likely that an “RFID multiplier” will emerge, whereby one lead entity’s RFID spending will cause ripple effects in the form of RFID investments by others in its supply chain, and then on to a next-level of derivative supply chain partners down-line. Yet, while this concept applies to government RFID investments

as well, RFID ROI in the public sector will come not only from the RFID multiplier in the supply chain, but from a larger spark in those new ideas and new companies that will pursue them in the marketplace. This will lay the ground for job and wealth creation, and make government investments in what Saffo termed a “weird new kind of media revolution” an enticing economic development tool. The public sector’s efforts to pilot and implement RFID today and over the next decade will help to advance the RFID knowledge base, establish best practices, overcome some of RFID’s technological quirks and physics problems, and set the standards to provide the common set of tracks for the RFID industry. Thus, federal, state and local officials must rightly examine each prospective use of RFID, from the battlefield to the warehouse to the library to the hospital for the implementation and ongoing costs vs. the tangible service gains and cost savings that can be achieved to determine the short-term ROI of their project. Yet, they should also know that by making the decision to implement ROI in their venue for their purposes and for the benefit

Figure 4. The increasing value of automatic identification interconnectivity

Multiple Value Chains

Single Value Chains

Value of Application

Single Organization Stand Alone

Degree of Connectivity

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of their stakeholders, they are helping to advance the technology and ultimately, they will help to not only give the technology the lift needed to fly over the long-term. Thus, they will help lay the foundation not only for future RFID uses in their own organization and aid the development of the people and companies that will prosper in the midst of this RFID revolution. Will RFID be “the next big thing?” At this point in the technology’s life cycle, it is too early for anyone to tell, but the stars certainly seem to be in alignment for the next decade to be a tremendously exciting one. As Under Secretary Alan Estevez (2005) recently wrote: “The real value of RFID lies not in what it can do today but in what it will do in the future” (n.p.). As Albright (2005b) so aptly characterized the RFID challenge, “We’re in the very early stages of a marathon” (n.p.). Many share the sentiment of Kuchinskas (2005) that: “RFID will change business and society as much as cell phones and the Internet have. While the technology will transform business processes, it also will ease some of life’s daily annoyances” (n.p.). One blogger captured the latter sentiment regarding RFID exquisitely when he wrote: “I’m serious. I don’t really care much for Wal-Mart’s inventory problems. RFID could solve my inventory problems” (emphasis in the original, n.p.). He desired a smart home that could help one locate “unhappy objects,” such as a forgotten coffee cup or the TV remote, made intelligent with RFID. For instance, if he couldn’t find his bag for a trip, he’d like to have the smart home system prompt him with: “Dude. You left the suitcase in the bathroom, under the sink again” (Grosso, 2004, n.p.). If we reach that point, RFID will have arrived. RFID may prove to be one of the true technological transformations of our time. As the successor to bar code technology to identify “things,” each and every day, new applications are being developed for the technology, both in the supply chain and beyond. Just as it has with the space program, the Global Positioning System, the

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Internet, and indeed, with the use of bar codes, the federal government is playing a significant role in the advancement of RFID technology and in the development of best practices and models that can be used both in the public and private sectors. Thus, the “visible hand” of government is once again an important factor in technological advancement and commercialization.

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Collins, J. (2005, April 8). Consumers more RFID-aware, still wary: A recent survey finds that more U.S. consumers have heard about RFID, but worries about privacy remain. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/articleview/1491/1/1/ Committee on Radio Frequency Identification Technologies, National Research Council (2004). Radio frequency identification technologies: A workshop summary. Washington, D.C.: National Academy of Sciences, National Academies Press. Retrieved July 19, 2007, from http://www.nap.edu/ catalog/11189.html Dignan, L. (2004, May). Riding radio waves: Are radio frequency identification tags good just for cutting costs in the supply chain? Not hardly. Theme park operators think they can boost sales. Baseline, 1(30), 22-24. d’Hont, S. (2003). The cutting edge of RFID technology and applications for manufacturing and distribution: A white paper from Texas Instruments. Retrieved July 19, 2007, from http:// www.ti.com/tiris/docs/manuals/whtPapers/ manuf_dist.pdf Downey, L. (2006, November 13). Can RFID Save the Day for Spinach? RFID Journal, Retrieved December 6, 2006, from http://www.rfidjournal. com/article/articleview/2802/1/82/. Emigh, J. (2004, December 1). Full-scale RFID could take a decade. eWEEK. Retrieved July 19, 2007, from http://www.extremetech.com/ print_article2/0,2533,a=140277,00.asp Estevez, A.F. (2005, May-June). RFID vision in the DOD supply chain. Army Logistician. Retrieved July 19, 2007, from http://www.almc.army.mil/ alog/rfid.html Ewalt, D. (2003, June 9). Navy puts RFID into service: Doctors and nurses at Iraq military hospital use the technology to track and treat patients. InformationWeek. Retrieved July 19,

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2007, from http://www.ti.com/tiris/docs/news/ in_the_news/2003/6-9-03.shtml Ezzamel, M. (2004). Work organization in the Middle Kingdom, Ancient Egypt. Organization, 11(4), 497-537. Fox, R., & Rychak, L. (2004, May). The potential and challenges of RFID technology. Advisory. Retrieved July 19, 2007, from http://www.mintz. com/publications/detail/264/Communications_ Advisory_The_Potential_and_Challenges_of_ RFID_Technology/ Gadh, R. (2004, August 11). The state of RFID: Heading toward a wireless Internet of artifacts. Computerworld. Retrieved July 19, 2007, from http://www.computerworld.com/mobiletopics/ mobile/story/0,10801,95179,00.html Glinsky, A. (2000). Theremin—Ether music and espionage. Champaign, IL: University of Illinois Press. Goodman, B. (2005, March 17). Is RFID taking off, or just taking its time? Integrating the Enterprise. Retrieved July 19, 2007, from http://www.itbusinessedge.com/content/3Q/3qpub2-20050317. aspx Government Accountability Office (GAO). (2005, May). Report to Congressional requesters— INFORMATON SECURITY: Radio frequency identification technology in the federal government. Retrieved July 19, 2007, from http://www. gao.gov/new.items/d05551.pdf Greenemeier, L. (2004, December 13). Uncle Sam’s guiding hand: Government mandates increasingly translate directly into IT initiatives, setting the top priorities at many companies. InformationWeek. Retrieved July 19, 2007, from http://www.informationweek.com/story/showArticle.jhtml?articleID=55301001 Grosso, B. (2004, November 15). Intelligent objects versus unhappy objects. RFID Buzz. Retrieved July 19, 2007, from http://www.rfidbuzz.

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com/news/2004/intelligent_objects_versus_unhappy_objects.html Hasson, J. (2004, September 27). The next big thing for government. Federal Computer Week. Retrieved July 19, 2007, from http://www. fcw.com/fcw/articles/2004/0927/news-nextthing-09-27-04.asp Hesseldahl, A. (2004, July 29). A hacker’s guide to RFID. Forbes. Retrieved July 19, 2007, from http:// www.forbes.com/2004/07/29/cx_ah_0729rfid_ print.html Ilett, D. (2005, June 24). Korea dishes out $800m on RFID. Silicon.com. Retrieved July 19, 2007, from http://networks.silicon.com/mobile/0,39024665,39131408,00.htm Jones, J. (2005, April 3). Is the “RFID” chip the mark of the beast? Political Gateway. Retrieved July 19, 2007, from http://www.politicalgateway. com/main/columns/read.html?col=323 Kuchinskas, S. (2005, January 12). RFID tags a booming biz. Internetnews.com. Retrieved July 19, 2007, from http://www.internetnews.com/ wireless/article.php/3458331 Landt, J. (2001). Shrouds of time: The history of RFID. Retrieved July 19, 2007, from http:// www.aimglobal.org/technologies/rfid/resources/ shrouds_of_time.pdf Maenza, T. (2005, June). Supply chain visibility exposes weak links, hidden costs. Insights. Retrieved July 19, 2007, from http://www.unisys. com/commercial/insights/insights__compendium/supply__chain__visibility__exposes__ weak__links__hidden__costs.htm Malone, R. (2004a, August). Reconsidering the role of RFID. Inbound Logistics. Retrieved July 19, 2007, from http://www.inboundlogistics.com/ articles/supplychain/sct0804.shtml Malone, R. (2004b, December). Sensing the future. Inbound Logistics, 24(12), 18-19.

Moore, B. (2003, February). RFID: Not a perfect world. Material Handling Management, 58(2), 50. O’Connor, M.C. (2005a, January 26). Scotland provides RFID support: The government-supported Wireless Innovation Demo Lab at Scotland’s Hillington Park Innovation Centre fosters RFID applications. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/1358/1/1/ O’Connor, M.C. (2005b, July 11). SF Library denied funds for RFID: The San Francisco Board of Supervisors’ Budget Committee rejected a request by the city’s library system to reserve funds for an RFID project in the coming fiscal year. RFID Journal. Retrieved July 19, 2007, from http://www. rfidjournal.com/article/articleview/1708/1/1/ O’Connor, M.C. (2005c, May 13). RFID helps Hanford manage waste: Bechtel Hanford has deployed an active-tag RFID system to identify a fleet of trucks and containers transporting 4,000 tons of radioactive waste daily. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/ article/articleview/1592/1/1/ O’Connor, M.C. (2005d, April 13). RFID and the media revolution: Renowned futurist Paul Saffo predicts that RFID’s biggest impact will come from surprising applications. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal. com/article/articleview/1508/1/1/ O’Connor, M.C. (2006a, July 28). DOD getting Gen 2-ready: The Department of Defense is expanding its RFID requirements and infrastructure while it takes steps toward transitioning its requirements to support the EPC UHF Gen 2 standard. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/2530/1/1/ O’Connor, M.C. (2006b, August 16). Will China’s RFID standards support EPC protocols, systems?: China has yet to release its much-anticipated RFID

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standards; some observers say it has produced too little, too late. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/2593/

Saffo, P. (2002, April 15). Smart sensors focus on the future. CIO Insight. Retrieved July 19, 2007, from http://www.cioinsight.com/print_ article/0,3668,a=25588,00.asp

Olsen, F. (2005, January 5). Social Security Administration utilizes RFID. USA Today. Retrieved July 19, 2007, from http://www.usatoday.com/ tech/news/techpolicy/2005-01-05-rfid-to-trackssa_x.htm

Schoenberger, C.R. (2002, March 18). RFID: The Internet of things. Forbes. Retrieved July 19, 2007, from http://www.mindfully.org/Technology/ RFID-Things-Forbes18mar02.htm

O’Shea, P. (2003, June). RFID comes of age for tracking everything from pallets to people. ChipCenter. Retrieved July 19, 2007, from http://www. chipcenter.com/analog/ed008.htm Parkinson, J. (2003, September). Tag! You’re it! The value of RFID goes way beyond the supply chain. Imagine knowing exactly how old that carton of milk really is. Or how safe your tires really are. CIO Insight, 1(30), 37. RFID News & Solutions. (2004, August). RFID— Powering the supply chain: Questions every user needs to answer before implementing RFID. Reed Business Information, 5(8), R1-R16. Ricadela, A. (2005, January 24). Sensors everywhere: A “bucket brigade” of tiny, wirelessly networked sensors someday may be able to track anything, anytime, anywhere. InformationWeek. Retrieved July 19, 2007, from http:// www.informationweek.com/story/showArticle. jhtml?articleID=57702816 Roberti, M. (2003, October 6). The tipping point: The U.S. Military’s decision to require suppliers to use RFID tags will have an even bigger impact than Wal-Mart’s RFID mandate. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/articleprint/607/-1/2/ Roberti, M. (2005, February 8). The puzzle of putting RFID tags on beer. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/ article/articleview/1393/1/1/

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Shameen, A. (2004, July 12). Singapore seeks leading RFID role: With the goal of becoming Asia’s foremost center for RFID technology, the island-republic will invest millions on research and training. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/1024/1/1/ Shepard, S. (2005). RFID: Radio frequency identification. New York: McGraw-Hill. Singel, R. (2004, October 21). American passports to get chipped. Wired. Retrieved July 19, 2007, from http://www.wired.com/news/privacy/0,1848,65412,00.html Sirico, L. (2005, February 3). Numbers that please the palate. RFID Operations. Retrieved July 19, 2007, from http://www.rfidoperations.com/newsandviews/20050203.html Sliwa, C. (2005, January 24). Retailers drag feet on RFID initiatives. Computerworld. Retrieved July 19, 2007, from http://www.computerworld. com/printthis/2005/0,4814,99170,00.html Sternstein, A. (2005a, June 20). FAA gives goahead to RFID. Federal Computer Week. Retrieved July 19, 2007, from http://www.fcw.com/ article89316-06-20-05-Print Sternstein, A. (2005b, March 4). Virginia Beach sees RFID payoff. Federal Computer Week. Retrieved July 19, 2007, from http://www.fcw.com/ article88216-03-03-05-Web Stockman, H. (1948, October). Communication by means of reflected power. In Proceedings

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of the IRE (Institute of Radio Engineers) (pp. 1196-1204).

Van, J. (2005, April 16). RFID spells media revolution, futurist says. Chicago Tribune, B1.

Sullivan, L. (2004a, October 25). IBM shares RFID lessons: Disruption to radio signals comes from surprising sources as IBM Global Services rolls out the technology in Wal-Mart tests. InformationWeek. Retrieved July 19, 2007, from http://www.informationweek.com/shared/printableArticle.jhtml?articleID=51000091

Wasserman, E. (2005a, April 11). Ridge says RFID can protect the U.S.: At RFID Journal’s Conference in Chicago, the Former Secretary of Homeland Security says that through innovation, RFID can strengthen both the nation’s security and its economy. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/1499/1/1/

Sullivan, L. (2004b, October 28). Wal-Mart takes RFID to Sam’s Club: The retailer will soon begin putting RFID-tagged cases and pallets in its chain of club outlets. InformationWeek. Retrieved July 19, 2007, from http://informationweek.com/story/ showArticle.jhtml?articleID=51201255 Sullivan, L. (2005a, January 24). Georgia court system hopes to trial RFID: The DeKalb County Juvenile Court wants to use RFID to track thousands of file folders on more than 9,000 children processed through the system each year. InformationWeek. Retrieved July 19, 2007, from http:// www.informationweek.com/story/showArticle. jhtml?articleID=57703442 Sullivan, L. (2005b, June 20). Where’s RFID going next?: Supply-chain projects spurred development. Now chips are turning up in ever-more-innovative uses. InformationWeek. Retrieved July 19, 2007, from http://www.informationweek.com/story/ showArticle.jhtml?articleID=164900910&tid= 5978 Swedberg, C. (2005, April 5). Marin County DA saves with RFID: The District Attorney for Marin County, California says his Department Will Trim 2,500 Man-hours a Year Thanks to a 3M RFID System That Lets It Locate Legal Files.” RFID Journal. Retrieved July 19, 2007, from http://www. rfidjournal.com/article/articleview/1486/1/1/ Thomas, L. (2005, January 26). RFID cell phones? Maybe in 2007. Mobile Magazine. Retrieved July 19, 2007, from http://www.mobilemag.com/ content/100/102/C3673/

Wasserman, E. (2005b, July 18). Agencies affirm privacy policies for RFID: A panel of government officials explained how agencies are trying to build privacy safeguards into potential U.S.-issued RFID-enabled IDs. RFID Journal. Retrieved July 19, 2007, from http://www.rfidjournal.com/article/ articleview/1747/1/1/ Welsh, W. (2005, March 21). Growin’ on empty: RFID’s many uses outpace available funds. Washington Technology. Retrieved July 19, 2007, from http://www.washingtontechnology.com/ news/20_6/statelocal/25834-1.html Wyld, D.C. (2004a, December). What’s the buzz on RFID?: New research reveals Americans’ knowledge, attitudes and concerns about the growth of automatic identification technology. Global Identification, (18), 14-19. Wyld, D. (2004b). Soaring benefits from RFID. Global ID Magazine, (18), 46-52. Wyld, D.C. (2005a, October). RFID: The right frequency for government. The IBM Center for the Business of Government. Retrieved July 19, 2007, from http://www.businessofgovernment. org/main/publications/grant_reports/details/ index.asp?gid=232 Wyld, D.C. (2005b). Delta Airlines tags baggage with RFID. Re:ID Magazine, 1(1), 63-64. Wyld, D.C. (2005c, March). Beyond the bar code: How the use of RFID (radio frequency identification) will give wine marketers unprecedented

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Wyld, D.C. (2006c, October). Sports 2.0: A look at the future of sports in the context of RFID’s weird new media revolution. The Sport Journal. Retrieved July 19, 2007, from http://www.thesportjournal.org/2006Journal/Vol9-No4/Wyld.asp Wyld, D.C. (2006d, October). The National Animal Identification System: Ensuring the competitiveness of the American agriculture industry in the face of mounting animal disease threats. Competition Forum, 4(1), 110-115. Wyld, D.C., & Jones, M.A. (2007). RFID is no fake: The adoption of radio frequency identification technology in the pharmaceutical supply chain. International Journal of Integrated Supply Management, (in press).

Wyld, D.C. (2006a, November). Better than advertised: The early results from Wal-Mart’s RFID efforts are in, and the technology may be outperforming even some of the most optimistic forecasts for improving retail…And the best is yet to come. Global Identification, (30), 10-13.

Zappone, C. (2006, July 13). E-passports: Ready or not here they come—The State Department expresses confidence in “e-passports’ while technologists fret about their security risks. CNN/ Money. Retrieved July 19, 2007, from http://money. cnn.com/2006/07/13/pf/rfid_passports/

Wyld, D.C. (2006b, September). Delivering at the “moment of truth” in retail: How RFID can reduce out-of-stocks and improve supply chain performance to store shelves, benefiting both retailers and product manufacturers. Global Identification, (28), 50-53.

Zebra Technolgies. (2004). RFID tag characteristics. Retrieved July 19, 2007, from http://www.rfid. zebra.com/faq_RFID_tag_characteristics.htm

This work was previously published in Patriotic Information Systems, edited by T. Loendorf and G. Garson, pp. 186-224, copyright 2008 by IGI Publishing (an imprint of IGI Global).

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Chapter 3.8

Web & RFId Technology:

New Frontiers in Costing and Process Management for Rehabilitation Medicine1 Massimo Memmola Catholic University, Italy Giovanna Palumbo Ospedale Valduce, Italy Mauro Rossini Ospedale Valduce, Italy

abstract Radio frequency identification (RFId) has recently begun to receive increased interest from practitioners and academics. This type of technology has been widely used in healthcare organizations for different purposes, like to localize patients, devices, and medical instruments. This chapter presents the results of a study in which we used RFId technology and modern systems of cost management methodologies (e.g., activity-based costing, activity-based management, and process management) in a “proof of application” aimed at defining some specific data on care needs of a

person with a disability, costs of the main activities performed during the person’s rehabilitation process, and level of performance which could be reached in order to improve the “disability management” process, from a clinical as well as a managerial perspective.

IntroductIon In recent times there has been a sustained trend of growth of healthcare and rehabilitation services demand coming from people affected by different types of psycho-physical disabilities related even

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

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to genetic characteristics and/or different type of traumatic events (e.g., stroke, traumatic brain injury, spinal cord injuries, anoxia, etc.). In order to have an idea about the “dimension” of this phenomenon, it is useful to refer to the World Health Organization (WHO), who has estimated a total of around 600 million people with some kind of disability all over the world. Some reasons for this rapid growth may be found in the interaction among different variables: • •

•

Demographic, referred to population growth and/or progressive aging. Social, mainly related to the higher probability of traumatic events related to traffic crashes, domestic and/or sportive accidents, work accidents, war-related, and/or violencerelated event injuries. Medical, related to a higher surviving rate after critical events due to the use of new trauma management techniques mainly in the acute phase and in general, to the higher survival rate of subjects with some kind of disability coming from congenital and/or acquired malformation of the central nervous system.

As an answer to such a growing demand, rehabilitation medicine has became a specific discipline aimed at the integral recovery of physical, psychical, social, and functional capacities of people with disabilities. These change-drivers have an important impact on the organization of activities in rehabilitation which, inevitably, follow different criteria if compared with acute care services. In rehabilitative medicine it is necessary to deal with “long term complexity” coming from the different potential development of a disability itself as time passes by. This characteristic, which is not present in acute care, is usually concentrated on short-term care. Furthermore this long-term

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feature has promoted a progressive trend toward home care vs. the option of in-hospital stay. This trend has been supported by the development of technological devices able to guarantee an appropriate quality of life for the person with disability while staying at home. Beside these issues, which are mainly referred to clinical factors, there are others mainly related to managerial aspects such as the way the healthcare service is organized, how much it costs, and how the rehabilitation process may be evaluated. In fact, rehabilitative medicine, as a fundamental aspect of disability treatment and even more than acute care field, poses some difficult questions about the measurement of type and quality of service offered and/or performed. Up to now those questions have received only partial answers through experimentation. Furthermore, experimentation in rehabilitation management has been considered a nonclear and nonprecise experimental field. Obviously, it is not just a trivial problem of working-load identification. Rather, its objective is to know if the care service performed is suitable to the diverse needs a person with a disability presents. This includes in-hospital as well as home care, in a continuous-care perspective. In rehabilitative medicine it is necessary to define what to do, how many people should be involved, which kind of professional must be involved, how long they will be involved, during which part of the day-time service will be provided, and which type of knowledge should the caregiver have in order to be able to assist the person with a disability. Therefore, the critical points are: Which and how much care service should be provided, who should be responsible for each activity, how it must be performed, and, finally, how much it costs. The answer to such questions depends on the possibility to measure costs through a structure that permits taking into consideration the specific nature of chronic care clinical paths. Those clinical paths may be considered as very complex “produc-

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tion processes,” which are usually performed in medium/long term periods (maybe during years) and which results present serious difficulties for being evaluated, if they could be evaluated at all. It must be considered that a rehabilitation process, different from an acute care process, does not mean the complete overcoming of a disability itself. Moreover, they usually are very “labour intensive” processes which produce a high working load to all healthcare operators involved, such as medical doctors, nurses, or physiotherapists. It is therefore, necessary, to define a system able to register clinical performance, (i.e., improvement of the degree of functional independence of the person with disability) as well as managerial performance (i.e., costs of rehabilitative clinical paths, optimization of working load assignment, and maximization of the use of available resources) As in other managerial fields, in rehabilitative medicine information and communication technology (ICT) may play an important role. This chapter presents an exploratory case study (Yin, 2003) aimed at clarify the potentiality of using new technologies of active and passive geographical localization (i.e., radio frequency identification [RFId]) in order to solve some of the problems mentioned above. RFId technology is nowadays widely used in different sectors, from logistics to massive distribution and production systems. Using it in the healthcare field is not a new trend, since literature refers to plenty of experiences where this technology has been used to trace, for instance, the movement of patients, devices, and other clinical equipment in different areas. This research has been performed considering the use of RFId technology and modern systems of cost management methodologies (i.e., activitybased costing, activity-based management, and process management) in a “proof of application” aimed at defining some specific data on care needs of a person with disability, costs of the main activities performed during a person’s

rehabilitation process, and level of performance which could be reached in order to improve the “disability management” process, from a clinical as well as a managerial perspective.

the lIMIts oF a tradItIonal approach to actIvIty-based costIng and IMplIcatIons oF tIMe-drIven approach Activity-based costing (ABC) is a particular methodology used for the determination of the costs of a product or a service. It is widely known in literature (Brimson, 1990; Cooper, 1988a, 1988b; Cooper, 1989a, 1989b; Cooper & Kaplan, 1988; Johnson & Kaplan, 1987; Shank & Govindarajan, 1992) as well as in the current managerial practice. This methodology was developed during the 80s as an answer to the changes that have, since then, characterized the organizational management and the corporation governance. Changes like the globalization of the market place, the rapid growth of automation applied to productive processes, the development of new products, and an increasing complexity of corporate structures were the framework in which ABC intended to go over the typical limitations and inefficiencies of the traditional systems of cost-control. In fact, the absence of alignment between the new structures and the traditional accounting methodologies introduced some dangerous distortions into the information related to costs. Among them: • • •

A wrong evaluation of the costs related to corporation complexity Attention concentrated exclusively on economic/financial values Goals oriented mainly toward short-term results with special emphasis on budget vs. execution control

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• •

• • •

Low consideration to production-supporting activities A rigid corporate structure based on functional units with no attention paid to accountability centres Incapacity to see the trans-functional dimension of corporate processes The ability to highlight the symptoms of problems but not their true causes The absence of a strategic conception, able to conceive corporation as an open system in continuous interaction with costumers, providers, and competitors.

The essence of the ABC approach is that it is very difficult to observe the direct relationships between the quantitative measurements of production factors and the measurements related to costs and prices of final products. The possibility to determine the cost of a particular product or service from an ABC perspective involves the performance of three main tasks: • • •

Phase 1: identification of activities involved in each process Phase 2: identification of the amount of resources used in each activity Phase 3: identification of the contribution each activities gives to the final object of cost

However, after two decades have passed since the first contributions appeared in literature, which had provided the general framework and the definition of the model, there is a kind of “feeling” that activity-based costing has not maintained its promises (Kaplan & Anderson, 2004). Contrary to expectations, traditional approaches have not been abandoned, ABC has not shown the expected generalized diffusion, and, above all, some implementation projects have been unsuccessful. The reasons for such failures may be brought back to the complexity of the calculation model,

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which makes the task particularly difficult and complex. Inevitably, the costs of designing and maintaining the system itself have proven to be higher than those related to traditional systems. Moreover, it is important to mention the negative impact of such a complex and difficult system on the staff satisfaction level. However such a complexity is linked to the companies’ policy when organizing an activitybased costing system. After having outlined an activity map they usually proceed to quantify how much time in percentage every employee dedicates to each activity compared with the total time at disposal. Such a percentage enables one to obtain a parameter (resource driver) through which you can determine the cost of the resources used for every activity. These estimated values are based upon the employee’s perception and are obtained through interviews or surveys carried out within the company. So far there is really not much to add since the approach undertaken is quite normal, and, at least from a theoretical point of view, it does not raise any other consideration. From a practical point of view, instead, the matter changes. Experience has shown that this kind of approach, where detailed processes, activities, and resources must be identified, may be easily applied to small and medium companies, or even in big corporations, as far as it is used just in some specific unit and/or on experimental basis. Difficulties emerge when the system is developed on a large-scale basis, mainly when a highly complex structure is present. And this is the case of a hospital structure. In such a context you have to organize thousands of employees under different professional categories. Most of all you have to handle a particularly complicated activity map due to the numerous treatments to undertake according to the various pathologies which may affect a patient. It is quite easy to imagine how difficult it can be to quantify how much time clinicians and nursing

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staff may dedicate to the various activities linked to different treatments. Furthermore, even if you succeed in quantifying time after long and laborious work (very expensive too), it would be ever so difficult to be maintained. The course of a process in a hospital is often subject to modifications to comply with the innovations, which, as time goes on, are brought to the diagnostic-therapeutic protocols relevant to a specific pathology. In practical contexts the activity-based costing fails to achieve one of the targets for which it had been created, that is, adequately capturing and processing the organization complexity. Besides, it has another limit: It has no perception of the complexity implied in carrying out each activity. Rehabilitation medicine can produce many examples of this. The activities undertaken, especially when they require more interaction with the patient and more assistance, mostly depend on the type of disability of the patient. Although being caused by the same pathology, disabilities are a very personal matter which can vary according to sex, general conditions, psychological attitudes, and to previous rehabilitation treatments performed either in a hospital or/and at home. It could be useful to define different types of activities which would enable one to detect the level of disability of the patient at a certain time2. The complexity of the activity map reveals that this solution can only be theoretical. As a final consideration on the limits of a traditional approach for implementing an ABC system, we could enquire with the hospital personnel on how they cope with the various activities; this may lead to the definition of a resource driver erroneously based upon a vision of full saturation of the availability of production capacities. It is necessary, therefore, to make a distinction between theoretic and real production capacities. In the first case it corresponds to the number of hours personnel is present within the structure

(e.g., 8 hours a day for 6 days a week); the second case derives from the first but you must deduct a 15-20% of time which personnel dedicates to other nonspecific productive activities such as arrivals, departures, breaks, communication with other colleagues, formation courses, and so forth. A similar thing may involve machinery, for maintenance, repair and scheduling which reduces the availability of production capacities. If resource drivers have to show resources (such as personnel, medical material, and facilities) consumed by each activity, parameters obviously must consider real production capacities or must quantify the real time each person has dedicated to the various activities. This is necessary not only to improve efficiency levels, but mostly to enable a correct handling of the work load created by patients. In fact, according to the number of patients in hospital, assistance will be directly proportioned to the disabilities displayed at a certain time, since this value can change time to time. So the questions are: Must the activity-based costing be left aside, reiterating old cost accounting systems? Has the calculation of activity-based costs revealed it to be a failure? It is the personal opinion of the authors, supported by literature, that the ABC system has shown to be, during these past years, a wonderful instrument to improve company efficiency, to rationalize production systems through the elimination of activities with no value added, and to identify opportunities for continuous improvement even where economical-financial aspects are not directly involved. A possible solution to the problems discussed above is to implement the activity-based costing system proposed by Kaplan and Anderson (2004), known as “time driven activity-based costing.” The solution proposed is quite simple and may be achieved by totally changing the way the matter has been faced up until now. According to their proposal, managers should proceed to measure:

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a.

b.

c. d.

The effective capacity of production as a quota of the total capacity theoretically available. The total overhead divided by effective capacity of production measured in time units, in order to obtain the cost per time-unit. The effective time used by each person for the performance of a particular task. Quantification of overhead weight on each activity.

The two authors wish to point out that it is not crucial to quantify in detail the time required by an employee to carry out a specific activity; it is just sufficient to make an accurate and credible evaluation. The thing to emphasize is that it is no longer necessary to carry out far-ranging surveys to calculate in percentage the time personnel dedicate to the various activities, but it becomes essential to know how long it takes to complete a certain activity. This estimation must be carried out by the managers, who can in any case involve personnel through interviews, meetings, and so forth. It is in the authors’ opinion that even this approach will not completely eliminate limitations and problems as aforementioned. The weak point of Kaplan and Anderson’s ABC system is when it has to quantify time required by employees to carry out each activity. In the opinion of the two authors, managers should determine this parameter by cooperating with personnel and searching for a credible quantification which does not have to necessarily be detailed. Although, by acting this way the quantification of a resource driver will not be objective enough since it could be somehow affected by personal perceptions. Managers, in fact, do not always have a complete knowledge of each process and operation phase relevant to every activity. This is also due to their different professional formation. For example, the financial manager of a hospital may only have a broad idea of the various clinical activities. In this case, the cooperation of person-

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nel directly involved in these activities becomes fundamental. The danger is that they may give wrong evaluations not necessarily for personal interests but due to consolidated habits which somehow may ignore situations of inefficiency. Besides, quantification would express average values which would not enable one to detect the differences due to different professional formation or experience; as a matter of fact, quite likely time dedicated to some of the activities could be reduced if carried out by more competent people. Another further consideration is that Kaplan and Anderson state that the cost of an activity is subject to modification in the following two cases: a) when costs change for personnel needs (e.g., wage increase, operational cost increase when acquiring new instrumental goods); and b) when there is a change in the resource driver, in other words, in the quantity of resource employed for each activity due to either improvement or worsening of production efficiency. In both cases a manual updating of the parameters of reference is necessary to allow the costing system to record the changes. This is a common requirement for traditional accounts systems, but it becomes quite difficult to handle for an ABC system when it has to face a huge activity map. In the end, in order to handle the complexity of activities strictly linked to the level of disability of a patient, in the case of rehabilitation medicine, Kaplan and Anderson propose an if-clauses structure which leads, however, to a considerable increase of complexity and maintenance of the system3. Through the specific design used in the experimentation reported in this chapter, we have tried to get through the problems discussed above, using the potentialities of a combination of new technologies (i.e., RFId, the Web, and WiFi) in order to obtain relevant data able to sustain an ABC system on real basis. The technological infrastructure, indeed, facilitates the registration of the working load generated by each person with a disability being

Web & RFId Technology

assisted inside the structure in a given moment, on a real-time basis. This means that it is possible to have, on real time, the information about the consumption of resources (i.e., staff time, sanitary materials, and/or instruments) associated to the activities performed in order to fulfil a specific clinical path. In other words, it is possible to know in each moment, maintaining the privacy of patients and operators4, how much and which kind of assistance is performed, given a particular “mix” of pathologies, which is (if any) the saturation degree of resources and which is the cost of the service.

radIo Frequency IdentIFIcatIon (rFId) technology The RFId technology is, among all emergent technologies, the one that presents the highest potentiality of development and application (Nextinnovator, 2004; Teradata, 2004). It consists “of small integrated-circuit ‘tags’ that can store information and announce their existence passively (or actively, NOA) through wireless radio communication to a networks of RFId readers. RFId potentially can track physical items –medical equipment, patient charts, even patients- in real time” (Janz, Pitts, & Otondo, 2005). Literature, even if it is still rather scarce, refers to some applications of this technology in a healthcare context. Until this moment it has been fundamentally used to localize devices and/ or instrumental goods in order to keep track of them and make them available whenever they are needed (Becker, 2004; Glabman, 2004). There are also some applications used to localize patients inside the hospital structure (Becker, 2004) or to evaluate the conditions of under and/or subuse of some particular equipment (McGee, 2004). The benefits obtained from the use of RFId technology in a healthcare environment are not restricted, however, to the improvement of supply

chain efficiency, but they could also be applied to the improvement of outcomes in a given clinical path (Wicks, Visich, & Suhong, 2006). In this sense some experiences show the use of RFId tags aimed at verifying the correct administration of medicines, to the proper patient and in the proper dosage (McGee, 2004), or else to avoid that some clinical equipment and/or surgical instruments may be “forgotten” inside the patient’s body, or that some instruments may be not correctly sterilized. In the future, RFId technology may be used to control the access of patients, staff, and/ or visitors to some restricted areas of hospital structure. Reviewing the literature, it seems to be clear that there have not been reported experiments in which RFId technology is used to support costing systems and/or process management systems. This is the challenge of this chapter.

the advanced health process ManageMent (ahpM) project The AHPM project is aimed at developing an exploratory case-study (Yin, 2003), performed with the objective of evaluating the usability of RFId technology and combined with the potentialities of Web and WiFi systems, in order to define an ABC system along with a process management system able to get through the typical inefficiencies coming from traditional accountability approaches. This case study was performed in a specialized rehabilitation centre, that is, “Centro di Riabilitazione Villa Beretta - Costa Masnaga (Lecco)”, Italy. Villa Beretta, a secondary structure of Valduce Hospital, is an Italian excellence centre dedicated to patients with traumatic brain injury, stroke, and other cognitive and movement disorders. It is a 81-bed hospital, with 154 personnel staff, organized as follows: 10% physicians, 18% therapists, 24% nurses, 26% health support

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operators, 14% administrative staff, and 8% consultants. In this centre is a functioning WiFi network since 2006.

Research objective This study is aimed at determining the possibility of using RFId and Web technologies in order to define the cost of a person with a disability in a rehabilitation context using working load and activity-based costing as the main methodological approach.

Research Methodology The project has been performed in three main phases. The first one was technologically-oriented, and it involved the application of technical devices to the measurement of contact time among service users and healthcare operators. The second phase, analytical, was dedicated to the development of an activity-based costing system able to combine organizational strategic goals with operational aspects. This phase was developed in five steps: •

•

•

Preliminary analysis, which consists of the selection of specific clinical paths and the organizational units involved. Activity and process mapping, which is the identification of macro- and micro-activities and their relationship in selected clinical paths. Hospital costing-based system, which is the study of costs related to selected clinical paths.

The third phase refers to the evaluation of the organizational impact of RFId/Web technology in a rehabilitation hospital (RH).

630

Execution Steps of the Project Given the complexity of the project from a technological as well as an organizational point of view, the first part has been defined as an experimental phase which will enable researchers to identify: • • •

The reliability of current technology; Design, test, and tuning of software; and Identification of potential organizational barriers.

In this sense, in order to obtain such preliminary assessments, the experimental phase has been performed in the following steps: A. Identification of processes/activities to be studied B. Implementation of technology and devices (main criteria used was to choose a standard technology available in the market place) C. Software development (description of macro- and micro- activities) D. Identification of patients E. Data collecting (organizational aspects) F. Data analysis

A. Identification of Processes to be Studied In order to simplify the experiment, it has been decided that just some activities will be followed, which are the normal sequence of actions performed by operators during a standard in-hospital day. This includes: general clinical control, medical visit, physiotherapy, personal hygiene, medicines administration, meals administration, and preparation of patient for night time.

Web & RFId Technology

B. Implementation of Technology and devices

•

The technological structure was defined in three steps: • • •

Selection of standard technologies and providers Testing of technology Start-up

Some important criteria were considered,in order to select the most suitable technology: •

•

First of all, environmental and technological limits and conditions were evaluated in order to define a proper research strategy. Based on this information, a basic infrastructure was defined, taking into account that patients’ activities were usually performed in different places; therefore, technological infrastructure should allow the “following”

•

of patients wherever the activities were being performed. Each activity could involve more than one actor (i.e., patient, nurse, medical doctor, objects, and so on) which must be properly identified in order to avoid ambiguity during data collecting and analysis. Technology must be transparent to all participants and must be as minimally personaldependant as possible.

This set of criteria has evolved into a decision of using a technology able to establish a specific “zone” around the patient which will allow the identification of roles and activities on a real-time basis. In order to strictly follow those criteria, a semiactive RFId technology was chosen; meanwhile, architectural design is patient-centered and it is based on three technological levels plus a fourth one to facilitate communication among them. Each one of these levels is independent from each other from a technological as well as functional perspective (Figure 1).

Figure 1. Three-layered architecture

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level 1: RFId Structure

•

The first level includes the RFId structure. This level is dedicated to physical data collecting and is performed through specific technological components defined as RFId tags, antennas, and markers. The RFId structure included following components:

•

•

Four activity recording stations placed at strategic points determined by the previous environmental study. Each station includes a PC and a RFId antenna (Figure 2). Five markers: three of them were placed under patients’ wheelchair, and the other two were placed near patients’ beds (Figure 3). Each marker was battery supplied. Thirty-six RFId tags with univocal identification code numbers associated with different actors and/or objects.

•

•

The devices used were: •

Active UHF Tag i-B2 (Identec Solution) Intelligent Long Range® beacon tag. Static data are written on these tags and are continuously sent at a programmable interval (ping rate) without being requested by a reader.

Figure 2. Timekeeping station

632

•

•

Position Marker i-MARK 2 (Identec Solutions). This device is used to determine exact locations of the i-B2 tag. As the tag passes the induction loop connected to this position marker, it is “woken up” and identification data are downloaded onto the tag. This device was adapted in order to allow the constant following of the patient. UHF Reader i-CARD R2 (Identec Solutions). It is ILR® beacon tag reader in a PCMCIA card format. It allows real-time information form tags. UHF Read/Write Antenna i-A9185 (Identec Solutions). It is an elliptically polarised RFID antenna specially designed for the identification of numerous tags at the same time in a large read zone. Since the antenna’s polarization is elliptical, the direction of the tag relative to the antenna does not matter. CV60 Computer (Intermec Technology Corporation). These computers with full PC functionality provide the connections between UHF antenna through UHF reader cards and data storage servers. The connection was realized using Cisco® Compatible 802.11b/g radio standard wireless LAN.

Each patient was provided with a position marker and an active tag. Operators (i.e., medical doctors, nurses, and physiotherapists) were provided with active tags. Other active tags were attached to lunch tables, medication trolleys, and hygiene trolleys. When a tag passes near by position marker it is “woken up” and its identification code is registered. In this way the system is able to recognize the association between patient and activities. All these information are collected by the RFId antennas and acquired by an activity recording station. Using wireless Wi-Fi network this information is transmitted and stored into a server. Figure 3 shows an example of meal administration activity, where it is possible to observe the relationship among one marker reader and three

Web & RFId Technology

Figure 3. Example of meal administration scheme

RFId tags, that is patient, nurse, and cafeteria tray. Once the marker “wakes up” the tags it is possible to identify that a meal administration activity is being performed for the specific patient.

level 2: Data level This level is mainly referred to the point where all information coming from tags arrives. In this point information is filtered and consolidated in different activities. The activity monitor is the software component responsible for the registration of activities among patients, healthcare operators, and hospital assets. The activity monitor role is to receive signals from the RFId/WiFi infrastructure, which has already been described. After that, using a particular predefined system of rules, a specific activity is associated to a special combination of signals5. The activity monitor is a Web WAMP6-based application. This platform has been chosen for two main reasons: a) to reduce time related to technology and go-live development, and b) to reduce project costs.

Figure 4 shows the activity monitor functioning. The data collecting stations send information to the activity monitor, which is the back-end of the main application. This application includes a database and a routine which from time to time brings out the activities from the raw data (Figure 5). The Web interface application allows operators to continuously control current activities with observed patients. Given the characteristics of the application, it has been chosen a Web application as architectural paradigm. Using a simple PC with an Internet/Intranet connection (or even an Extranet) and a standard browser (e.g., Microsoft Internet Explorer, Mozilla Firefox, etc.) any operator may monitor the activities during its performance and the operator may also act in case of abnormal functioning7. Furthermore, the application allows one to obtain additional information on current activities through a simple selection of the activity’s name, which may be observed on the third column of the Figure 6. In this way it is possible to observe all information related to involved operators as

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Figure 4. The activity monitor

Figure 5. Activity monitor functioning scheme

Figure 6. Patients activities dashboard (Activity Monitor main screen)

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well as general information about patients’ hospitalization. The activity monitor is an example of how Web applications may change the business application in a substantial way; in fact, it is possible to go from the old concept of installation of applications on each user’s PC to a new logic client-server based on a Web interface. The benefits of this approach are not only the facility to access applications, but also include other critical issues like:

tags indicating the duration of each contact. For instance, whenever a position marker simultaneously reads a medicine trolley, a patient, and a nurse tag, software identifies this association as medicine administration activity. The information is available to researchers on real-time basis and may be observed directly on PCs screen and/or through a Web-based interface (Figure 6). All activities detected by each position marker were registered into a proper log file on the server.

•

D. Identification of Patients

•

•

Simplification and optimization of maintenance activities of the application. It is possible to develop a business intelligence application based on the data coming from the activity monitor in a completely transparent and automatic way. Application is available anytime, anywhere.

level 3: logic/Process level This is the analytic level where all detected activities are associated with processes and costs. Its development depends on software design.

C. Software Development

Six patients were asked to participate to the project and all of them accepted to be followed using RFId/Web-based technology. Their selection was done according to three main criteria: adults, whose estimated hospitalization time was at least two weeks and who had a relatively high programmed working-load. The hypothesis on their potential working-load was defined based on their evaluation on the new Barthel index (NBI)8, which determines the level of autonomy in daily life activities (DLA). In fact all patients except one presented a very low NBI, consequently there was a very high potential working-load for healthcare operators (see Table 1).

The software was developed in such a way as to allow the identification of specific activities through the relationship between the different

Table 1. Patients characteristics Patient ID.

Sex

Age

Diagnosis

New Barthel Index

701

M

55

TBI (Traumatic Brain Injury)

0

702

M

64

TBI (Traumatic Brain Injury)

4

703

M

61

Ischemic Stroke with non responsive period

4

704

F

44

Hemorrhagic Stroke with non responsive period

0

705

M

26

TBI (Traumatic Brain Injury)

0

706

M

46

TBI (Traumatic Brain Injury)

10

635

Web & RFId Technology

E. Data Collecting In order to proceed to data collecting, some organizational arrangements were done. First of all, it was necessary to guarantee the consensus and compliance of operators as well as patients and care-givers. For the reliability of data, we asked for some pre-experimental actions such as training, technical trials, and monitoring. Training. Special training was provided to healthcare operators, patients, and caregivers. They were told about how technology worked, how to used it, which kind of implication could results have, and how data obtained would be used. Additionally, IT providers were trained on rehabilitation clinical path, healthcare management and policies, and organizational potential impact of technology. Technical trials. Three experimental trials were performed in order to determine the ability to use devices and the effective technological capacity to collect data using a RFId/Web-based system. Monitoring. A continuous process of monitoring was performed in order to verify technology functioning as well as operators’ and patients’ compliance. Two main problems were solved using monitoring. The first one was the maintenance of data collecting continuity, since some difficulties arose from crashes of the software during beta test and faults of WiFi service due to maintenance operation on the infrastructure not communicated to project manager. The second problem was related to technical granularity for activity identification. Having a range of action predetermined by the “marker” in a 3 meter diameter sphere, whenever more than one patient’s tag was read along with one single operator’s tag, it could be difficult to identify the right patient/operator relationship. In order to avoid potential interferences, operators were asked to keep themselves away at, at least, 1.5 meters from any other patient they were not work-

636

ing with. This requirement put a strong stress on operators, given rehabilitation room dimension and layout. Data was collected during a period of 8 days.

Data Analysis Tables 2 and 3 show the data of the activities carried out first during the preliminary experimental phase then during the adjustment phase of the system. As mentioned previously, the research group has decided to carry out an experimental preliminary session on the technological services in order to: • • •

•

Go ahead with a fine-tuning of the various hardware and software components; Evaluate what impact would this technology have on hospital personnel; Understand up to which extent technology could be bearable or invasive to patients, care-givers, and generally to the rehabilitation treatment; and To verify the reliability of information given by the system to establish whether a cost accounting activity-based system can be implemented.

Let us focus on the last point, leaving comments to points two and three to next paragraph. The estimation of the technological efficiency as to the targets of the project (defining an activitybased costing system based upon real-time and objective measuring of time hospital personnel dedicate to various activities) has been the most important aspect of the research. This is the reason why during the experimental phase one person of the team accurately followed either operators or patients in order to check whether the system would correctly identify the different types of activities and properly measure time of each activity.

Web & RFId Technology

Table 2. Preliminary data collecting: activity information about Patients no. 701, 702 and 703 Activity

Time (minutes)

Total Counter Activity

Average of operator per activity

Patient 701 General Clinical Control

420

9

1

Medical Visit

0

0

0

Medicines administration

0

0

0

Physiotherapy

0

0

0

5201

9

1

0

0

0

934

7

1,8

0

0

0

Meals administration Personal hygiene Preparation of patient for night time Other residual interactions Total

6555

Patient 702 General Clinical Control

201

2

1

Medical Visit

119

3

1

Medicines administration

10

1

1

Physiotherapy

3413

5

1

Meals administration

2100

5

1

Personal hygiene Preparation of patient for night time Other residual interactions Total

50

6

1

25413

44

1,5

0

0

0

5

1

1

224

1

1

0

1

1

110

1

1

0

0

1

15

1

1

270

5

1,4

0

0

0

31306

Patient 703 General Clinical Control Medical Visit Medicines administration Physiotherapy Meals administration Personal hygiene Preparation of patient for night time Other residual interactions Total

Facts reveal that this effort has not been in vain. This experimental phase has pointed out some technological discontinuities already mentioned before, which must be contained, otherwise they could jeopardize the reliability of the system. We also had the chance to notice that the system

624

records very brief interactions between patients and clinicians, which are very difficult to identify as a specific activity. We decided to measure and quantify, during the definite survey, this interaction (see Table 3) which could depend on the following two factors:

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Web & RFId Technology

Table 3. Data collecting: activity information about Patients no. 704, 705 and 706 Activity

Time (minutes)

Total Counter Activity

Average of operator per activity

Patient 704 General Clinical Control

120

6

1

Medical Visit

84

8

1

Medicines administration

234

3

1

Physiotherapy

805

6

1

Meals administration Personal hygiene Preparation of patient for night time

863

27

1

1278

12

1,7

917

13

2

Other residual interactions

3875

Total

8176

-

-

Patient 705 General Clinical Control

128

11

1

95

11

1,2

244

6

1

Physiotherapy

1692

13

1

Meals administration

1116

8

1,3

Personal hygiene

1151

12

1,7

987

10

1,8

Medical Visit Medicines administration

Preparation of patient for night time Other residual interactions

3340

Total

8753

-

-

Patient 706 General Clinical Control

126

6

1

Medical Visit Medicines administration Physiotherapy

45

12

1

264

6

1

1892

13

1

Meals administration Personal hygiene

73

9

1

231

7

1

25

9

1,5

Preparation of patient for night time

•

638

Other residual interactions

3104

Total

5760

One could be technological due to the unevenness of the technology for identifying the activities as explained above. The marker creates a sphere around the patient of a diameter of 3 meters; within this sphere other operators may be present while carrying

•

-

-

out activities with other patients. This creates interference which may be only partly eliminated; Another could be organizational, regarding assistance and performance of rehabilitation treatments. During research we decided to

Web & RFId Technology

focus only on some activities of the process, especially concerning the patients’ stay in the hospital, which were quite time-intensive in regards to medical performance, excluding the rest. We believe that the interactions difficult to record refer to these latter activities or to brief contacts between patient and clinicians which do not include any professional service. Tables 2 and 3 have been structured in order to show in the first column the type of activity, in the second column the total time (expressed in minutes) for each activity, in the third column the number of times the specific activity was recorded in the established period, and the last column the average number of operators present during the performance of the activity. The following are a few remarks to better understand the information on Tables 2 and 3. The first experimental survey was carried out in July 2007 referring to patients identified with codes 702, 702, and 703. It lasted 3 weeks, but shows inevitable discontinuities due to the reasons aforementioned. After this test session, we carried on with a second survey in September 2007 for a week on patients identified with codes 704, 705, and 706. As shown in Table 1, the two groups are made of different patients with different disabilities or pathologies which caused the disability. The value carried in column 2 in Tables 2 and 3 is relevant to the quantity of each activity performed by the patient during the survey period. It refers to a partial measuring carried out during this period (one week) and not during the whole time of treatment. Column 3 shows the activity driver, that is, the number of times when a specific activity is carried out by a patient during the time of the survey. In this case, it is necessary to explain that value 12 for activity “medical examination” carried out for patient 706 does not mean that the patient personally went through this examination

12 times during one week. It means that the system recorded the activation of this activity 12 times during survey time. For instance, it could happen that during the medical examination, the doctor may get in and out from the sphere of influence of the marker various times. Regarding information carried on Table 3, we may consider the following: •

•

•

Patient 704 and 705, although they have a similar disability (according to the Barthel scale), they carry out a different number of activities probably due to the different pathologies which caused the disabilities. Patient 706 confirms the fact that fewer activities are necessary for disabilities of minor seriousness. If you get close to a specific treatment, by studying each relevant activity, you may notice that: a) the most time-intensive activities are the ones linked to physiotherapy, catering, personal hygiene, and preparing patients with serious disabilities for the night (Patients 704 and 705); b) patients with minor disabilities (patient 706) mainly require only physiotherapy, although personal hygiene and medicine dispensing remain always considerable activities; c) generally speaking physiotherapy is carried out in proportion to the level of disability; and d) the number of people involved in a treatment is also proportioned to the level of disability (activities concerning personal hygiene, night preparation, and catering to a certain extent are very often carried out by two operators at the same time, in the case of Patients 704 and 705).

dIscussIon Results reflect some critical points in three main areas, that is, technology design and use,

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relationship between technology and task, and, finally, relationship between data obtained and ABC model.

Technology Design and Use The decision to use standard existing technology revealed some difficulties due to the need stated by the experiment design to follow up the patient at any moment and in any place inside the hospital. In fact, the patient-centred architectural design chosen presented the need to convert a standard fix device (position marker) into a mobile device able to follow the patient. This adaptation revealed three main difficulties. The first one was that a market-proved stable technology became an unstable technology. The second one was that since the device was provided with a battery which needed to be charged daily in order to guarantee movement during all day long, it showed to be too dependent on operator collaboration. This put a limit on the autonomy of the whole system and, therefore, the device loses its intrinsic autonomy.

Relationship between Technology and Task The design of data collecting procedure imposed to each operator the use of an active tag during each operator’s working shift. This characteristic revealed some critical points. First of all, being a procedure to much dependent on operators’ collaboration and/or compliance, it happened some times that some of them forgot to take the tag and/or it was difficult for them to carry the tag during day-long activities, due to the tag dimension. In fact, it was perceived as a heavy device. Furthermore, it was perceived as a way to control their daily activities. Trying to understand this last finding, and in order to perform the experiment, it was necessary to do some interviews in which other organizational issues were detected

640

as relevant variables. For instance, it was clear to researchers that giving a consensus did not necessary mean being compliant and it was taking into account a variable already identified by literature as a clue variable, which is that operators did not perceive a positive relationship between their individual performance of a particular task and the result expected from the use of that specific technology. Moreover, in some cases, they expressed a general perception of being controlled while performing their normal task. This, of course, meant that a perception of a potential threat to their right of privacy was present. The appearance of these serendipity variables confirm the fit between individuals, task and technology (FITT) framework recently presented by Ammenwerth, Iller, and Mahler (2006), which invites researchers and managers to pay attention to organizational relationships as well as to technological issues.

Relationship between Data obtained and ABC Model The final aim, as the essence of the Advanced Heath Process Management (AHPM) project was to demonstrate the feasibility of using modern technology such as Web, WiFi, and RFId for creating a system of activity-based costing/ activity-based management which could enable an objective measuring in real time of the consumption of professional resources (e.g., clinicians, nursing staff, and physiotherapists) dedicated to various activities during a treatment. A system which would not only be able to overcome the limits of traditional approaches to the development of an activity-based system as mentioned in the previous chapter, but also make available a complete set of information for measuring the process performances both from a clinical and managerial point of view. As a matter of fact, details on how different professional resources are used in performing the activities of a treatment at an established time

Web & RFId Technology

or period may implement a system balanced by performance indicators which can be structured upon three main perspectives (Figure 7): financial perspective, patient perspective, and process perspective. For example, from a financial perspective, you can either detect the impact the cost of each activity has on the total cost of the treatment or establish the economical feasibility through a comparison with the reimbursement system according to national standards. From a process perspective you can either quantify the saturation point of the different professional resources or, during both budget and final balance, estimate the impact a treatment may have on the personnel available. In the end, from a patient perspective, it could be either possible to determine the duration of a treatment or record any improvements of the disability as a result of a mix of assistance/ rehabilitation activities carried out. Any consideration regarding the above would not be much different from what years ago led to the creation of measuring systems of the multidimen-

Figure 7. Evaluation perspectives of the managerial performances according to the AHPM project

sional performances not any longer based upon a traditional (and unique) economical-financial dimension (Kaplan & Norton, 2004), but which widen the analysis towards a qualitative dimension (regarding process, client satisfaction, growth, and learning) allowing to better link daily work to the achievement of the company strategy. In comparison with the systems of objectives, requirements, and working assumptions as previously defined, it could look as if the results obtained during research and outlined in these pages confirm the initial intuitions that modern technologies can bear both a cost accounting activity-based system and relevant multidimensional performance measuring system. Although, we ought to establish limits to this result since it is linked to research which is still at the experimental stage. We shall deal with these limits in the next paragraph. Tables 4 and 5 show information which has determined the cost for each activity relevant to Patient 706. Table 4 shows in particular details regarding the consuming of different professional resources for each activity expressed in hours. Table 5 shows the cost per activity as the sum of the cost for professional resources involved in each activity. Obviously, this is a partial cost measuring which takes into account—and we are aware of this—only the cost for the working resource. Although, we do believe this does not belittle the meaning of the results obtained, due to the following: •

AHPM

PATIENT PERSPECTIVE

•

In hospitals, especially where rehabilitation is performed, the cost for working resource is the highest; besides any estimation which can be made, no doubt that at the “Centro di Riabilitazione Villa Beretta” cost of work has a 70% impact on the total costs. It is quite simple to link the cost of each activity to another direct cost such as the medical and nonmedical material consumed. This information is available on the digital

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Web & RFId Technology

Table 4. Operators involved for each activity (analysis on Patient 706) Human Resources (time) Activity

Time (hour)

Medical Doctors

Nurses

Nurse Assistants

Physiotherapists

2,10

-

1,90

0,20

-

Patient 706 General Clinical Control Medical Visit

0,75

0,60

0,15

-

-

Medicines administration

4,40

-

4,40

-

-

Physiotherapy

31,53

-

-

-

31,53

Meals administration

1,22

-

-

1,22

-

Personal hygiene

3,85

-

0,95

2,90

-

Preparation of patient for night time

0,42

-

-

0,42

-

Other residual interactions

51,73

-

-

-

-

Total

6807,2

0,60

13,85

15,74

31,53

Table 5. Costs for each activity and professional involved for Patient 706 Human Resources (euros) Activity

Costs per activity (euro)

Medical Doctors

Nurses

Nurse Assistants

Physiotherapists

31,60

2,77

-

2,49

-

-

73,17

-

Patient 706 General Clinical Control

34,37

Medical Visit

31,59

29,10

Medicines administration

73,17

-

Physiotherapy

577,94

-

-

Meals administration

16,88

-

-

Personal hygien

34,37

-

Preparation of patient for night time

5,81

-

Other residual interactions Total

•

-

case history of the patient, therefore, a possible improvement could be achieved by connecting the two databases. Company overheads have a 20% impact on the total costs. A simple and effective solution could be to reload at same percentage the cost of personnel for each activity.

577,94

16,88

-

40,14

-

5,81

-

774,14

In the end, Drawing 8 shows the saturation point of the different professional resources during

642

15,80

-

-

29,10

123,06

65,60

577,94

the period of survey. It is a virtual measurement, not a real one, because this survey was carried out only upon three patients against many others who were there in hospital at the same time. The way the survey was carried out (towards three patients for one week) did not really give a sense to the value of the key performance indicators (KPI), which had been assumed during the multidimensional measuring of the performances.

Web & RFId Technology

Figure 8. Saturation points of the various professional resources

Medical Doctor

Nurses

Nurse Assistants

Therefore we think it would not be much useful to outline and discuss the results now, postponing the whole matter once improvements have been made.

Physiotherapists

•

•

lIMItatIons and Further research areas Instead of talking about the limits of this research, we believe it is much more useful to highlight in this paragraph what we have experienced and learned during this project development carried out at the “Centro di Riabilitazione Villa Beretta,” since we are quite convinced that any proof of concept needs further proof of concept until the ideas, models, considerations, and technological structures presented in these pages are validated. Obviously there are many elements to ponder upon. Starting from the technology aspect we must admit that the solution proposed is not really ready to start with, since:

•

It basically needs a WiFi net, which is not commonly used in hospitals yet, especially here in Italy. The RFId technology, or some of its elements, are still experimental, especially with regards to the configuration we used during our research which was based upon a net with an antenna, a marker, and a series of active tags; in particular, the feeding system of the marker was bulky and invasive both for personnel and patients. For sure the availability of a RFId technology with WiFi, with a less invasive marker and by becoming functional for medical personnel (for instance, an active RFId on a palmtop computer with Internet) could definitely be an improvement. The software used for capturing, storing, processing, and reporting data is another tessera of fundamental importance. This is another way to improve the quality and reliability of data by removing disturbances and wrong surveys.

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Although miracles do not happen by only installing technology, it is notorious that the most advanced technologies and the best projects may fail if the human factor is not taken into consideration. Therefore the following become necessary: •

•

•

A multidisciplinary approach in all phases in projects of this kind where medical, managerial, technological and project management competences alternate. A serious involvement of clinicians explaining to them the real objective and functioning modalities of the technology; we have many times remarked that a project of this kind can be misunderstood and seen as a sort of “big brother” controlling each movement of doctors and staff, but the aim is not this at all! But thanks to a more advance technology, and to detailed information we can feed a cost accounting activity-based system and better handle processes linked to an activity-based management (e.g., estimation of work-loads, determining how many disabled patients can be borne, measuring performance, and linking company strategies to operational attitudes). Formation activities regarding functioning modalities and the logic of the system.

and 706) enabled us to carry on with some necessary adjustments mainly required by the layout of the hospital building.

conclusIon The research has demonstrated that it is possible to use the potentiality of RFId, the Web, and WiFi technologies to design a tailor-made system for activity-based cost and performance management that may overcome the limitation and inefficiency observed in traditional approaches and referred by literature. This experimentation was performed in a highly complex context, that is, the one of rehabilitation medicine which emphasizes some of the previously discussed restrictions. Therefore, it becomes an optimal testing in the healthcare context. Some of the critical elements arising from this experiment are: •

According to certain logic of the process, the following is essential: •

•

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To correctly map the company processes, such as assistance and rehabilitation treatments which are different according to the specific disability of the patient. A multidisciplinary approach becomes essential as explained above. To carefully calibrate the technology and test its efficiency. The preliminary test we carried out (on Patients 701, 702, and 703) before a definite survey (on Patients 704, 705

•

The identification and development of technological infrastructure. In order to be able to use this experience, the technology and devices used are easily available on the market place even if they must be reengineered to fulfil the specific goals and to reduce investments. These operational criteria have had a strong impact on the perception of patients and staff about the experimentation process. The organizational and behavioural dimensions of involved staff. The highest risk that this technology has shown is that it may be perceived as a kind of “big brother” which controls every action and/or attitude performed by the clinical staff. As this experiment has demonstrated, this is not the actual goal of a project like this. On the contrary, it is oriented toward the identification of potential overuse of production capacity

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•

which could be a consequence of the presence, inside clinical structure, of patients with different disability characteristics. Nonetheless, the track-records of relevant information will permit to have a more effective clinical plan. Patients and personnel privacy protection. All information was anonymously recorded and treated in order to guarantee the maximum level of privacy protection.

This experimentation has demonstrated that current technology is able to produce valuable information to achieve the established goals. Probably, technology may be improved, reengineered, and industrialized. But, it is clear that just technology is not the complete answer. It is necessary to foresee the organizational aspects and impacts as well as the methodologies and analytical tools which will enable the healthcare manager to obtain not only information, but actual results.

ACKNoWlEDGMENT We thank for the collaboration to the project the following partners: IBM, Cisco, Intermec, and Softwork.

reFerences Ammenwerth, E., Iller, C., & Mahler, C. (2006, January 9). IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study. BMC Med Inform Decis Mak, 6-3. Becker, C. (2004). A new game of leapfrog? Modern Healthcare, 34, 28-38. Brimson, J. A. (1991). Activity-accounting: An activity-based costing approach. New York: John Wiley and Sons, Inc.

Cooper, R. (1988a, Summer). The rise of ABC (part I): What is an ABC system. Journal of Cost Management, 45-54. Cooper, R. (1988b, Fall). The rise of ABC (part II): When do I need an ABC system? Journal of Cost Management, 41-48. Cooper, R. (1989a, Winter). The rise of ABC (part III): How many cost drivers do you need, and how do you do select them? Journal of Cost Management, 34-46. Cooper, R. (1989b, Spring). The rise of ABC (Part IV): What do ABC systems look like? Journal of Cost Management, 38-49. Cooper, R. (1990, Spring). Implementing an activity-based cost system. Journal of Management Accounting, 33-42. Cooper, R., & Kaplan, R. S. (1988). Measure costs right: Make the right decision. Harvard Business Review, 66(5), 96-103. Davis, H. E., & Luehlfing, M. S. (2004, November). Radio frequency identification: The wave of the future. Journal of Accountancy, 43-49. Fanberg, H. (2004, Fall). The RFID revolution: Healthcare is ready to embrace this new technology. Marketing Health Services, 43-44. Glabman, M. (2004, May). Room for tracking: RFID technology finds the way. Materials Management in Health Care, 26-38. Janz, B. D., Pitts, M. G., & Otondo, R. F. (2005). Information systems and health care II: Back to the future with RFID: Lessons learned – some old, some new. Communications of the Association for the Information Systems, 15, 132-148. Johnson, T. H., & Kaplan, R. S. (1987). Relevance lost. The rise and fall of management accounting. Boston: Harvard Business School Press.

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Kaplan, R. S., & Anderson, S. R. (2004, November). Time driven activity-based costing. Harvard Business Review, 131-138.

endnotes 1

Kaplan, R. S., & Norton, D. P. (2004). Strategy maps. Boston: Harvard Business School Press. Mahoney, F. I., & Barthel, D. (1965). Functional evaluation: The Barthel index. Maryland State Medical Journal, 14, 56-61.

2

McGee, M. (2004). Health-care I.T. has a new face. Information Week, 988, 16-26. NextInnovator. (2004). Top ten technologies for 2005. Retrieved June 20, 2008, from http://technologyreports.net/nextinnovator/?articleID=3004 Shank, J. K., & Govindarajan, V. (1992, Winter). Strategic cost management and the value chain. Journal of Cost Management, 5-21. Taghaboni-Dutta, F., & Velthouse, B. (2006, November). RFId technology is revolutionary: Who should be involved in this game of tag. Academy of Management Perspective, 65-78. Teradata. (2004). Survival of the fastest. Retrieved June 20, 2008, from http://www.teradata.com/t/ page/126753/ Wicks, A. M., Visich, J. K., & Suhong, L. (2006, Summer). Radio frequency identification applications in hospital environments. Hospital Topics: Research and Perspective on Healthcare, 84(3), 3-8. Yin, R. K. (2003). Applications of case study research (2nd ed.). Thousand Oaks, CA: Sage Publication.

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3

This chapter is the result of joined research work done by all authors, nevertheless, Massimo Memmola is the author of section I, II and III; all the authors contributed in equal proportion in section IV, discussion and conclusion. There are different scales for evaluating disabilities based upon daily activities which can be carried out autonomously by the patient (e.g., the Barthel Scale). “The key insight is that although transactions can easily become complicated, managers can usually identify what makes them complicated. The variables that affect most such activities can often be precisely specified and are typically already recorded in a company’s information systems. To take an example, let’s assume a manager is looking at the process of packaging a chemical for shipment. In this situation, complexity arises from the potential need for special packaging and the additional demands of air as opposed to ground transportation. Let’s say that if the chemical is already packaged in a way that meets standard requirements, it should take 0.5 minutes to prepare it for shipment If the item requires a new package, however, the manager estimates, either from experience or from making several observations, that an additional 6.5 minutes will be required to supply the new packaging. And if the item is to be shipped by air, he or she knows (or can quickly determine) that it will take about 2 minutes to put the package in an air-worthy container. This information allows the manager to estimate the time required for the packaging process;

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Packaging Time = 0.5 + 6.5 [if special packaging required] + 2.0 [if shipping by air]” (Kaplan & Anderson, 2004). Moreover, this measurement may express average values which allow neither the identification of individual operator nor the differences due to the experience each operator shows mainly related to his/her expertise and time-experience.

5

6



7

8

See example on Figure 3 about meal administration activity. Windows, Apache, MySQL, and PHP For instance, whenever a specific activity is not performed during a predefine period, the operator may ask for performance of the responsible operator. Mahoney and Barthel (1965)

This work was previously published in Business Web Strategy: Design, Alignment, and Application, edited by L. Al-Hakim and M. Memmola, pp. 145-169, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 3.9

Smart Antennas for Automatic Radio Frequency Identification Readers Nemai Chandra Karmakar Monash University, Australia

abstract Various smart antennas developed for automatic radio frequency identification (RFID) readers are presented. The main smart antennas types of RFID readers are switched beam, phased array, adaptive beamfsorming and multiple input multiple output (MIMO) antennas. New development in the millimeter wave frequency band60 GHz and above exploits micro-electromechanical system (MEMS) devices and nano-components. Realizing the important of RFID applications in the 900 MHz frequency band, a 3×2-element planar phased array antenna has been designed in a compact package at Monash University. The antenna covers 860-960 GHz frequency band with more than 10 dB input return loss, 12 dBi broadside gain and up to 40° elevation beam scanning with a 4-bit reflection type phase shifter array. Once implemented in the mass market, RFID

smart antennas will contribute tremendously in the areas of RFID tag reading rates, collision mitigation, location finding of items and capacity improvement of the RFID system.

IntroductIon The Radio Frequency Identification (RFID) system is a new wireless data transmission and reception technique for automatic identification, asset tracking, security surveillance and many other emerging applications. An RFID system consists of three major components: a reader or integrator, which sends interrogation signals to an RFID transmitter responder (transponder) or tag, which is to be identified; an RFID tag, which contains the identification code; and middleware, which maintains the interface and the software protocol to encode and decode the identification

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Smart Antennas for Automatic Radio Frequency Identification Readers

data from the reader into a mainframe or a personal computer. Figure 1 below illustrates a generic block diagram of the RFID system. At the dawn of the new millennium, as barcodes and other means for identification and asset tracking are becoming inadequate for recent demands, RFID technology has been facilitating logistics, supply chain management, asset tracking, security access control, intelligent transportation and many other areas at an accelerated pace. A recent Google search of the terminology ‘RFID’ brought up thirty eight million hits. This large huge number of URLs represents the significant activities and applications of RFID in various sectors in either commercial domains or government agencies. RFID technology is an off-shoot miniaturized version of the ‘identification, friend or foe (IFF)’ radar system developed by British defence during World War II. This radar technology used backscattered signals to identify and/or discriminate friendly targets from enemy targets and enabled decisions to attack appropriate targets. While low frequency RFID tags use strong magnetic coupling by being in proximity to the RFID reader’s coil antennas, all ultra high frequency (UHF) and microwave RFID readers and tags are based on the radar principle of sending far-field electromagnetic (EM) interrogating signals from the readers and receiving the back-scattered modulated signals with the unique identification code of the tag. Thus identification of items, human beings and animals is possible in all weather conditions and off line-of-sight communication.

RFID was first proposed by H. Stockman (Stockman, 1948) who introduced the RFID system in his landmark paper “Communication by Means of Reflected Power”. Stockman advocated that considerable research and development work was required to solve the basic problems of wireless identification by means of reflected power. A complementary article on the history of RFID can be found in Landt (2001). Similar to radar technology, RFID is a multidisciplinary technology which encompasses a variety of disciplines: (i) RF and microwave engineering, (ii) RF and digital integrated circuits, (iii) antenna design, and (iv) signal processing software and computer engineering. The latter encodes and decodes analog signals into meaningful codes for identification. According to Lai et al (2005), “The fact that RFID reading operation requires the combined interdisciplinary knowledge of RF circuits, antennas, propagation, scattering, system, middleware, server software, and business process engineering is so overwhelming that it is hard to find one single system integrator knowledgeable about them all. …. In view of the aforesaid situation, this present invention (RFID system) seeks to create and introduce novel technologies, namely redundant networked multimedia technology, auto-ranging technology, auto-planning technology, smart active antenna technology, plus novel RFID tag technology, to consolidate the knowledge of all these different disciplines into a comprehensive product family.”

Figure 1. Generic RFID system Clock Data/Energy

Reader Global Network

Tag/ Transponder

Host Computer

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Due to the flexibility and numerous advantages of RFID systems compared to barcodes and other identification systems available so far, RFIDs are now becoming a major player in retail and government organisations. Patronization of the RFID technology by organisations such as WalMart, K-Mart, the USA Department of Defense, Coles Myer in Australia and similar consortia in Europe and Asia has accelerated the progress of RFID technology significantly in the new millennium. As a result, significant momentum in the research and development of RFID technology has developed within a short period of time. The RFID market has surpassed the billion dollar mark recently (Das & Harrop, 2006), and this growth is exponential, with diverse emerging applications in sectors including medicine and health care, agriculture, livestock, logistics, postal deliveries, security and surveillance and retail chains. Today, RFID is being researched and investigated by both industry and academic scientists and engineers around the world. Recently, a consortium of the Canadian RFID industry has put a proposal to the Universities Commission on the education of fresh graduates with knowledge about RFID (GTA, 2007). The Massachusetts Institute of Technology (MIT) has founded the AUTO-ID centre to standardize RFID, thus enabling faster introduction of RFID into the mainstream of retail chain identification and asset management (McFarlane & Sheffi, 2003; Karkkainen, & Ala-Risku, 2003). The synergies of implementing and promoting RFID technology in all sectors of business and day to day life have overcome the boundaries of country, organisation and discipline. As a wireless system, RFID has undergone close scrutiny for reliability and security (EPCglobal, Inc., 2006). With the advent of new anti-collision and security protocols, efficient antennas and RF and microwave systems, these problems are being delineated and solved. Smart antennas have been playing a significant role in capacity and signal quality enhancement for

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wireless mobile communications, mobile ad-hoc networks and mobile satellite communications systems. The advent of smart antennas has brought many benefits for the communications industries as many value added services can be accommodated in modern mobile communications. The spatial and polarization diversities exploited from smart antennas have added new and unique dimensions in wireless communications. The advantages of smart antennas have also been incorporated in RFID systems. Smart antennas are used in RFID readers where multiple antennas and associated signal processing units are easy to implement. Even multiple antennas are proposed in RFID tags to improve reading rate and accuracy (Ingram, 2003). This chapter concerns smart antennas for RFID readers to enhance the performance of automatic identification systems, asset tracking in real time and inventory control in warehouses. It is important to note that smart antennas for RFID readers are currently in the developmental stage. To the best of the author’s knowledge, such smart antenna systems for RFID have not yet emerged as the mainstream enabling technology. Therefore, smart antenna implementation systems for RFID readers are either still on the drawing boards of designers or under investigation, mainly by various commercial research groups as patents, be they either conceptual or physical developments. The chapter on smart antennas for RFID readers has been organised as shown in the flow chart in Figure 2 below. An introduction to RFID is presented first. The limitations of barcodes as the currently available mainstream identification system are presented next, followed by RFID as the replacement and enabling technology for barcodes in the new millennium. The significance of smart antennas for RFID readers for the improvement of the three major application areas of asset tracking and management, anti-collision, and supply chain management is presented. Literature reviews of these application- specific smart antennas for

Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 2. Document map of smart antennas for RFID readers RFID Smart Antennas Introduction to RFID

Barcodes and limitation

RFID as enabling technology

Application specific significance of smart antennas Asset tracking & Management

Anti-collision

Supply chain management

Background works An in-house developed phased array Conclusions

RFID readers are analysed next. An in-house developed phased array antenna for 900 MHz band RFID readers is then presented, followed by conclusions.

rFId as barcode replaceMent technology RFID systems potentially overcome the key disadvantages of bar codes: the necessity for line-of-sight scanning of individual items and the presence of human operator to direct the reading process. Because RFID tags are read remotely a human operator is not generally required to direct the reading process. This makes it practical to read RFID tags at many more points in a logistic or other chain, and to read them more often, thereby locating chain failures more rapidly. Several “anticollision” algorithms have also been developed to allow the simultaneous reading of multiple RFID tags (EPCglobal, 2005; Law et al, 2000; Ward & Compton, 1992). RFID technology therefore

additionally offers the possibility of automatically identifying multiple items at once, greatly speeding up the monitoring process. This multiple-reading capacity also has some disadvantages; it means that with RFID identification, every individual item must have its own, unique identification number. While all items of a particular type can be marked with the same barcode, this is not the case for RFID tags. RFID tags have much greater information content than barcodes and must be more easily customised, since every RFID tag must be unique. The key barrier to barcode replacement by RFID is therefore an inability to produce suitable tags cheaply. In particular, the chip on such tags must be eliminated and replaced with a fully-printable technology – that is, a printable, multi-bit, chipless tag must be developed. This must be achieved whilst retaining (or improving upon) all of the other necessary properties of such tags, such as a reasonable reading-distance, orientation invariance in the tag’s response to interrogation, and suitable collision avoidance

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measures. In effect, it is necessary to make the transponders as cheap as possible while building the required sophistication into the reader. This sophistication in the reader comes from smart but fast computational algorithms in signal processing chips and smart antennas for readers.

tag universal like the barcodes. To this end RFID readers play a significant role in reading tags with various formats and standards. Figure 3 shows a generic RFID reader system. An RFID reader comprises three main parts shown. These main three functional blocks are: (i) control section; (ii) high frequency (HF) interface; and (iii) antenna. At the user end, the reader is connected to the host application such as enterprise software. The control section of the RFID reader performs digital signal processing and procedures over the received data from the RFID transponder. The control section also enables the reader to communicate with the transponders, wirelessly performing modulation, anti-collision procedures, and finally, decoding the received data from the transponders. These data are sent to the HF interface to interrogate tags (read) or to reprogram the tag (write). This section usually consists of a microprocessor, a memory block, analog-to-digital converters and a communication block for the software application. The HF interface of the reader transmits the interrogating signal to the tag and receives the returned coded echo from the tag. The HF interface comprises two signal paths corresponding to two directional data flows to and from the transponder. The local oscillator generates the RF carrier signal, a modulator modulates the signal, the modulated signal is amplified by the power amplifier, and the amplified signal is transmitted through the antenna. A directional coupler separates the

rFId readers RFID readers should comprise smart antenna systems, dedicated digital signal processing units and embedded systems to efficiently read multiple tags with faster speed and highest accuracy. The system should also allow easy integration of RFID readers in data networks alongside middleware. Networking of these readers should comply with standardized data transfer protocols. However, the range and speed of data communications, and the operational frequency and coding techniques used by RFID readers vary greatly with application -specific requirements. The operating frequency for the application-specific RFID systems spreads over extremely low frequency (ELF) systems of a few hundred KHz up to microwave and millimetre wave frequencies such as 60 GHz and above (Tuovinen and Vaha-Heikkila, 2006). This wide range of applications and frequency bands creates a problem for ubiquitous RFID systems and acceptance of global standards. In Generation 2 tag systems a unified approach has been formulated to remove some of these barriers and make the

Figure 3. Complete block diagram of RFID reader HF Interface

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Data

Data input

Power supply

Data output

Host application/ middleware

Digital control unit

Communi -cation interface

P

Address

RAM ROM

Signal coding/ Data decoding

High frequency interface

Transmitted data Received data

Antenna

Power amplifier Local Oscillators

Directional coupler Low noise amplifier

Demodulator

Smart Antennas for Automatic Radio Frequency Identification Readers

system’s transmitted interrogating signal and the received weak coded echoes from the tag (Pozar, 2005). An alternate choice is to use a duplexer switch to separate the transmit and receive paths when a single antenna is used in a monostatic radar configuration. The low noise amplifier (LNA) increases the received echo’s amplitude before the signal is decoded in the demodulator. Another LNA can be used to amplify the signal after modulation of the signal. Different demodulation techniques are used to decode the received data from the transponder. Most RFID systems operate using binary phase shift keying (BPSK) (Kocer, Flynn, & Long-Range, 2005) and amplitude shift keying (ASK) (Karmakar et al 2006). RFID Reader Antennas. The efficiency of the RFID reader’s capability for interrogation and detection is highly dependant on the performance of the reader antenna. A comprehensive literature review (Preradovic & Karmakar, 2007) has suggested two types of RFID reader antennas: (i) fixed beam arrays and (ii) scanned arrays. A fixed beam antenna has a unique and fixed beam radiation pattern (Padhi et al, 2001). Most RFID readers are equipped with omni-directional or wide beamwidth antennas in order to cover as much area as possible as their interrogation zones. Several fixed beam antennas are also used, and can be commonly found in Alien Technology readers (Alien Technology Corporation, 2005). The antennas are easy to install and do not need any switching electronics and associated logic control to steer their beams. However, they pick up multipath signals when receiving transponders’ backscattered signals. This situation usually leads to reading errors during interrogations. R FID tags are subject to multipath interferencean inherent problem of electromagnetic signals. Multi-path interference makes an RFID tag unreadable even if it is within the reading range of the reader. To solve this problem, a new type of antenna technology is used that can electronically control the main beam emitted from the reader’s antenna. This smart antenna

technology incorporated in RFID readers reduces reflections from surroundings and thus minimizes degradation of the system performance due to multipath and other undesired effects. Smart antennas are scanned beam antenna systems, which point beams to selective transponders within their main lobe radiation zones, thus reduce reading errors and collisions among tags. This technique exploits spatial diversity, and in many cases polarisation diversity of tags. The directed beam also reduces the effects of multipath fading (Ingram, 2003). This new approach to RFID antenna technology is being adopted by RFID manufacturers such as Omron Corporation, Japan (Nakamura & Seddon, 2006), RFID Inc. (Profibus, 2007) and RFSAW, USA (RF Analyst, 2003). In March 2006 Omron Corporation developed a new electronically controlled antenna technology in their UHF band RFID reader systems (Nakamura & Seddon, 2006). Recently, RFID Inc. (Profibus, 2007) has introduced a 32-element compact package smart antenna with associated switches at 125 KHz. This new product advances RFID applications in factory automation, process controls and original equipment manufacturer (OEM) markets. However, the technical details of these smart antennas for RFID readers are not available to readers due to commercial in confidence considerations. In this chapter every effort has been made to present technical information on smart antennas for RFID readers and this is provided in subsequent sections.

applIcatIon specIFIc sMart antennas For rFId readers As stated above there are few smart antennas for RFID readers currently available on the market. This is due to the facts that (i) RFID technology is application specific and (ii) the operating frequency spans from extremely low frequency (ELF) of a few hundred KHz up to mm-wave frequencies of 60 GHz and above

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(Tuovinen and Vaha-Heikkila, 2006) depending on applications. A comprehensive literature survey by the author and a colleague (Karmakar and Bialkowski, 2000) suggests the following classifications of smart antennas for RFID readers: (i) switched beam array; (ii) phased array and (iii) adaptive antenna array. Unlike the low gain omni-directional antennas, directional steerable antennas are medium to high gain antennas and tend to have a gain value larger than 8 to 20 dBi. These antennas are made up of an array of small antenna elements, often for aesthetic reasons. The advantage of most directional antennas as opposed to omni-directional antennas is that the former tend to have better performance and suffer fewer signals fading (Ingram, 2003). The disadvantage of most directional antennas is that they require beam steering and switching electronics and associated beam-forming algorithms for adaptive antennas to point the focused beam of the antenna in the desired directions and nulls toward the interferers. Such antennas are therefore much more expensive that fixed beam antennas. In addition, higher gain directional antennas are usually much larger than

omni-directional antennas. Nevertheless, while size and cost are disadvantages, the improved performance of the directional antennas outweighs that of fixed beam antennas. Electronically steerable antennas are fixed with supporting platforms such as gantries. Electronic beam steering can be achieved by (i) switching on certain antenna elements and turning off the rest as in switched beam array antennas; (ii) changing the phase excitations of individual elements as in phased array antennas; and (iii) adaptively controlling the weight vectors of individual elements using array signal processing chips. Adaptive antennas can also be sub-classified as multiple input multiple output (MIMO) or multiple input single output (MISO). Finally, by combining switching and phase shifting the turned on subset of the antenna element, a switched beam phased array is formed. Figure 4 shows classifications of smart antennas for RFID readers. Following are detailed accounts of the available smart antennas for RFID applications.

Figure 4. Extended classification of smart antennas for RFID readers RFIDreader readerantenna antenna RFID Fixed-beam Fixed-beam array antenna array antenna

Smartantenna antenna Smart

mm-wave-IDMEMS MEMS Switched-beam mm-wave-ID Switched-beam phased array antenna array phased array antenna array Switched-beam Switched-beam phasedArray Array phased

Phasedarray array Phased

Adaptivearray array Adaptive

•MIMO •SIMO

ExtendedTx/Rx Tx/Rxantenna antenna Extended FDMA/TDMA/SDMA/MIMO/ withphase phasescanned scanned FDMA/TDMA/SDMA/MIMO/ with beampointing pointingarray arrayantenna antenna /frequencyhopping hopping beam /frequency /down converter /down converter

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Switched Beam Array Antenna A sequentially-fed switched beam antenna array (Hercht & Storch, 2007) is proposed to find the location of gaming chips with embedded RFID tags for gambling applications. The antenna array is capable of generating various subsets of beams in different directions. The RFID tagged gaming chips are located on a Blackjack, poker or other gaming table. A planar array of multiple elements is placed under the gaming table. A subset of antenna elements is sequentially turned on and off to form sectorized directional beams to illuminate the RFID tagged gaming chips, enabling the antenna to identify and locate a particular gaming chip. As an example of the excitation mechanism, the sub-set of antenna array 1 in a given zone is turned on to form a beam to illuminate the tags (called group 1) that subsequently respond to that first antenna 1. The data associated with antenna 1 are captured and recorded as antenna 1 activation. Next an adjacent antenna 2 is excited in the same reading zone and data are captured as antenna 2 activation. These two data sets are compared by an automatic programme and the results of the comparison reveal which tags are in group 1 but not in group 2. A proximity advantage of the tag to the reader antennas is exploited to learn which tag is within a particular interrogation zone and which tag is leaving a particular zone. Thus the location of the gaming chip can be determined in real time. The complexity of the algorithm and the antenna array configuration depend on the applications used in gaming systems. For example, a Blackjack game has limited and more controlled chip placement areas on the table surface than those of other gambling systems. Therefore, Blackjack would need a less complicated switched beam antenna array and sequence excitations that a gaming table with more positions. Therefore, linear arrays of 3 elements up to planar arrays of 13 by 13 antenna elements are proposed as possible solutions for different gaming applications (Hercht & Storch, 2007).

A switched beam proximity magnetic reader has been proposed by Kowalski, Serra & Charrat (2005) to identify gas bottles and other metal containers with switchable inductive coils in the reader. The coils are sequentially turned on and off until the item is identified. Lee (2005) proposes a switched beam coil antenna array configuration for RFID readers for smart shelves for retail shops. Figure 5 shows a schematic block diagram of the smart array reader. The reader has an N-number of coil antennas which can be placed under the platform of the racks. The items to be identified will be placed on the platform of the coil antenna. Antennas will be sequentially switched on one at a time and will go through frequent recalibrations for matching and tuning depending on the load changes on the coil antenna. This load changing happens when shoppers pick up items from the shelves and replace them after inspection. The sensing unit records the voltage levels received from the antenna and sends this information to the reader controller unit. The reader controller unit will send appropriate commands to the antenna matching unit to maximise the received power from the coil antenna. The directional coupler separates the transmit path from the receive path. The reader controller is connected to the I/O port and data communication bus to another server for the application software for identification and location. An anti-collision protocol with ‘tag talk first’ is implemented to discriminate multiple tags within the reader’s zone of a coil antenna. The reader can be connected to many antennas and can retrieve the tag information and location of particular items in real time. The sensing unit is a half wave peak voltage detector and the matching circuits are made from switched capacitor banks, varactor diodes controlled by the reader controller, and switched inductors. This real time tracking and identification of items has great benefits for inventory control and supply chain management. Chung and Lui (2004) propose some types of

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loop antenna arrays for detection of objects. The loop antennas are placed at different angles in different planes in a 3D rectangular volumetric space so that the diversity of the individual loops can be exploited to maximise the reading of RFID tags. These antennas are suitable for baggage tracking at checkpoints of ports of entry, inventory tracking in a warehouse scenario, factory or warehouse inventory control, security identification and access control. Chung and Lui (2004) also propose a curtain antenna with five loop antenna elements and associated matching and switching circuits similar to the configuration shown in Figure 5 above. In an extended version of the antenna configuration, an elongated antenna element with back-to-back loop arrangement connected to appropriate filters is also proposed to maximise the reading range. The antennas can operate over wide frequency bands of 125 KHz, 13.56 MHz, 915 MHz or 2.45 GHz depending on the tuning capability of the loop antenna and its configurations (physical dimensions). A reading distance of 1 meter with 2 antenna arrays with a power of 25 watts is claimed. In another embodiment

of the switched loop antenna for RFID readers for metal containers, the loop antenna is made of cables with three undulations and the loop portions all in series and coupled to the tuner circuit. This mechanism well defines the detection region approximating the volume defined by the base and walls of the container. The coupling means of the antenna include a tuning circuit, a filter and a switch for selectively connecting a particular loop antenna to the external processor. Lai et al (2005) propose a redundant networked multi-media RFID system which uses wireless local area network (LAN) and Ethernet connections simultaneously. The system is capable of providing output videos, graphics or image data so that after reading RFID tags, one or more pictures preloaded into the system databases can be downloaded instantly for visual verification by logistics handlers, security guards or customs officers in real time. The reader is equipped with an active smart antenna for transmission and reception. In the transmitting antenna a power amplifier is incorporated for cable loss compensation and to boost the transmitting power. In the receiver

Figure 5. A switched beam antenna reader for retail chain inventory and supply chain management

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Transceiver Transceiver

Reader Reader controller controller

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Tagged Tagged items Tagged items items

1

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N Antenna Antenna sensingunit unit sensing (pilotsignal) signal) (pilot

Antenna Antenna matching matching controller controller

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Smart Antennas for Automatic Radio Frequency Identification Readers

chain a low noise amplifier (LNA) is added to the antenna to improve the received signal’s quality and power. The smart antenna for the reader has wider varieties of intelligence such as frequency hopping, time slotting, antenna positioning and beam scanning, subset antenna switching and polarisation diversity to exploit the maximum signal readability from multiple tags. A master synchronization controller controls all functionalities mentioned above for the antenna arrays. The antenna system can operate in multiple transmit and single receive mode or single transmit and multiple receive mode. The various transmitting antennas are configured in such a way that either spatial diversity collimation or temporal diversity collimation is achieved via sequential switching of the subsets of the antenna array. These spatial and temporal diversities will increase the reading probability of moving tags. As an extension of the above method, the author proposes multiple transmit multiple receive, in which the active transmit antennas operate in different frequency channels or hopping frequency channels. In this arrangement, the multiple transmitting antennas are not time slotted in operation, but all are actually working at the same time. This will enable true simultaneous multi-detection bistatic reader operation. Mendolia et al (2005) in their US patent entitled “RF ID tag reader utilizing a scanning antenna system and method” claimed three inventions of the adaptive antenna systems for RFID readers: (i) a 9-element cylindrical switched beam phased array antenna (ii) a single band electronically steerable parasitic array radiator (ESPAR) and (iii) a dual-band ESPAR. In this section only the switched beam phased array antenna is discussed. The latter two antenna types will be discussed in Section 4.3. Figure 6 illustrates the claimed embodiment of the 9-element switched beam antenna in a simplified block diagram. The proposed antenna configuration has some advantages over the conventional phased array antenna, being cost efficient, power efficient, simple and compact

in configuration, and finally, protocol efficient. Figure 6 is the simple configuration derived from Mendolia et al (2005). The array antenna has 9 dual polarised stack patch antenna elements, which are arranged in a circular grid. The antenna and associated antenna RF electronics are housed in a radome supported by a round baseplate. The top parasitic rectangular patch antenna is a 1.4 inch thick brass plate supported by a 1.5 inch dielectric slab having dielectric constant of 2.8±7% and loss tangent of 0.002. The lower exciting rectangular patch is made of copper plate, which is supported by another dielectric slab with similar dielectric properties to the top one. Two orthogonal feed probes are inserted through the ground plane and soldered to the exciting patch. The 90° phase shifter is used to change the polarisation of the antenna from vertical to horizontal, to slanted polarisations to ±45°. The RF switches connected to the antennas permit any of the three consecutive antennas to be active and properly phased at any time. The switch is an MMIC switch and connected in consecutive 1, 4, 7 fashion so that at any time three consecutive elements are energised to generate a directional beam. The switching electronics receive the command from the micro-controller based on the feedback of the pilot signal from the RF transceiver. The digital electronics are connected to the host computer via a serial I/O interface such as RS232. The phase shifter is a tunable dielectric phase shifter made of two thin film SMD Parascan surface mounted varactor diodes. Mendolia et al (2005) propose a polarizer to generate four different polarizationshorizontal, vertical, and ±45° slanted polarizations to maximise reading rates using polarization diversity. In this polarizer the dual feed lines of the antenna are connected to two single pole double throw (SPDT) switches followed by a quadrature hybrid coupler and another set of SPDT switches. Karmakar and Bialkowski (1999) have built an 8-element switched beam phased array antenna for mobile satellite communications. For insights

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Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 6. A 9-element switched beam phased circular array 9 9-element cylindrical antenna array module

1

8 2

7

3

6 4

5 Radome

SP3T Switch

To host application

Phase shifter module

Switching electronics Serial I/O R232

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into the antenna design and the RF electronics of the switched beam phased array antenna, readers are referred to this article. Marsh et al (1996) propose an embodiment of a switchable transmit and receive reader antenna system composed of two transmitting panel antennas and one receiving panel antenna. The panel antennas are a 4×2-element microstrip patch antenna array. The transmit interrogating signal is a narrowband signal at different bandwidth and the receiving identification signal from the tag is a broad bandwidth signal so that the tag is responsive to one or more interrogating signals. The patch antenna array is design at 800 – 1000 MHz band and covers the features of polarization diversity in transmission and reception. The transmitting interrogating antenna has facilities with different frequency switching in a narrowband manner. The frequency switching is carried out in an alternate

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1-to-3-way power splitter

Power supply

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manner so that the tag receives two distinct signals from the interrogating antennas. The interrogating signal is also more directive than that of the received signals. The beamwidth of the receiving reader antenna is at least five times that of the transmitting antenna. Polarization diversity and beam switching are implemented in the reader antenna so that there are no overlapping nulls of the E-field of the transmitting antenna, and as a consequence no tag is missed out of the reading process. Thus this proposed system increases the probability of identification. The use of different frequencies and different polarizations is very useful in a situation when many articles are thrown in a shopping trolley randomly. In such a situation, different frequency transmissions and different polarized antennas will pick up signals from all transponders.

Smart Antennas for Automatic Radio Frequency Identification Readers

Thus far switched beam antenna arrays for RFID readers have been discussed. The next embodiment of smart antennas for RFID readers is phase scanned array antennas.

Digital Beamforming Phased Array antennas In relation to digital phase scanned array antennas, Salonen and Sydanheimo (2002) propose a non-uniform antenna array based on binomially distributed current excitations to achieve a -37 dB sidelobe level from a 5-element antenna array. The amplitude is controlled with variable amplifiers/attenuators connected to individual antenna elements in the array. Beam steering is done with an array of 4-bit digital phase shifters as shown in Figure 7. The phase shifter is an array of four bit reflection type phase shifters comprised of quadrature hybrid couplers and reactive loadings at its coupling and isolation ports. The transmission phase of the hybrid coupler changes with switching RF pin diodes ON and OFF. The phase shifter bits are 22.5, 45, 90 and 180°, giving a resolution of ±11.25° phase shift. The attenuation and phase

shift of each antenna element is controlled by a control bus which is eight bits wide. With this control bit, a theoretical fine beam steering angle of 3.75° is achieved. The detection of the received signal is based on the highest power levels from the tag for the reader antenna. The microprocessor controller of attenuations and phase shifts of each element makes the antenna adaptive. By controlling the current distributions and phase shifts to individual elements, any possible 3D steerable beam pattern can be adaptively generated. The adaptive beam can point the main beam toward the desired signal and nulls towards interferers. Detailed physical layer development of phase shifters, beamforming networks and antenna arrays are presented in Section 5.

Adaptive Antenna Arrays •

Electronically steerable parasitic array radiator (ESPAR)

As mentioned above, Mendolia et al (2005) have developed three adaptive antenna systems for RFID readers. The 9-element cylindrical switched beam phased array antenna has been

Figure 7. Adaptive digital beamforming network for RFID reader smart antenna 1

φφ

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Smart Antennas for Automatic Radio Frequency Identification Readers

discussed in the section on switched beam array antennas above. The other two antenna typesa single band electronically steerable parasitic array radiator (ESPAR) and a dual-band ESPARare discussed here. The next embodiments are single RF port electronically steerable switched parasitic monopole and dipole antennas. The salient feature of the ESPAR antenna is that, having only a single RF port, the adaptive antenna needs only one A/D converter. This is a significant cost saving measure in terms of hardware and software implementation. The detailed features of an ESPAR can be found in Sun, Hirata, Ohira & Karmakar (2004), where the authors provide details of the construction, algorithm development and prototype testing of an ESPAR antenna array for a mobile ad-hoc computing network. Figure 8a shows a single band ESPAR antenna with 7 monopoles. Figure 8b shows a dual-band ESPAR antenna with lower monopoles for the low frequency operation and small monopoles for the high frequency operation. The lower frequency band is at 1.6-1.7 GHz and the upper frequency band of operation is 2.4-2.5 GHz. Conventional adaptive antennas cannot operate in dual-band as proposed here. The central single port RF monopole antenna has the same length of the low frequency monopoles. An example of beam patterns generated by a 7-element

ESPAR antenna derived from Sun and Karmakar (2004) is shown in Figure 9. The beam pattern can be adaptively shaped in any form pointing the maximum power to the desired tag and nulls to the interferers enabling anti-collision to be effectively implemented with an ESPAR antenna. According to the current embodiment of the ESPAR antenna for RFID readers, the radiated power can also be concentrated in the near field thus maximising the reading of the tag in the vicinity of the reader.

Maximum Ratio Combining (MRC) Adaptive Antenna Array While the ESPAR antenna is the simplest embodiment of an adaptive antenna array, Wang et al (2004) invented a very complex embodiment of adaptive antenna array. Figure 10 shows a detailed schematic diagram of an adaptive array antenna with multiple antenna elements, modulators, weight vector generator, RF receiver and automatic gain control (AGC) loop. The adaptive antenna combines signals received from multiple tags from the vicinity of the reader. The smart antenna processing module is connected to the antennas with transmit and receive (T/R) switches so that the same antenna can be used for trans-

Figure 8. ESPAR antenna (a) single band and (b) dual-band Exciting monopole

Low frequency parasitic element

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( b)

Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 9. Beam Patterns (a) to (f) in the Azimuth Plane for Antenna Sampling Periods #1 to #6. Load Reactance: x1 = -90 ohm, x2 = 0 ohms, x3 = 0 ohm, x4 = 0 ohms, x5=0 ohms, x6 = 0 ohms, where xi is the reactive loading of the varactor connected to the parasitic element i, and i = 1…6.

mitting signals to the tags and receiving signals from the tags. The main objective of the smart antenna processing module shown in Figure 10 is to maximise the received signal-to-noise ratio (SNR) based weights determined by maximal ratio combination (MRC). The antenna uses a closed loop signal processing operation for antenna weight computation (IW and QW) and signal combination (Σ). Though the same antennae can be used to receive and transmit with a T/R switch (not shown here), Wang et al (2004) present the receive mode adaptive antenna array only. In the receive path of the closed loop feedback operation, the signals received from tags are amplified with low noise amplifiers (LNAs) and down-converted in the respective down converters. Each of the down-converters multiplies the output of the respective amplifiers by a local oscillator’s in-phase (LO-I) and a quadrature phase signal (LO-Q). The resultant I and Q signals pass through the lowpass filters (LPFs) followed by the AGC loop which normalises the signal level before the MRC algorithm. The AGC provides consistent performance for the smart

antenna processing unit at different input signal levels. At the output of the variable gain amplifiers, a power detector combines the power of I and Q signals and compares the signal power to a threshold value. The output of the comparison is fed to the AGC loop, which adjusts the gain of the variable amplifier accordingly for a consistent output. Thus the MRC algorithm-based beam forming processor is able to work at different input signal levels. MRC Beam Forming: In MRC beamforming the processor adjusts the antenna weight adaptively by correlating each of the received signals with a combined signal as the received signal arrives. The correlation time determines the received signal’s post-detection SNR of the weight computation. Increasing the correlation time allows optimal antenna weights to be achieved in very low SNR environments. The SNR improvement comes from both the antenna combining gain and the diversity gain as for example the polarisation diversity. Thus dual polarized antennas for transmission and reception is an added feature of the adaptive antenna. With MRC antenna weights, the

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Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 10. Schematic diagram of an adaptive array antenna for RFID reader

RFreceiver receiver RF

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received signals from different antennas can be coherently combined (i.e. in phase) while uncorrelated noise from different antennas is combined incoherently. As a result, the SNR increases after combining. Additionally, the signal received by some antennas could experience fading, by which the signal strength could be reduced significantly. Combining signals from all the antennas reduces the probability of the signal fading in the output signal and thereby achieves diversity gain. The authors claim significant SNR improvement in a fading channel. For example, with an array of 2-elements and in a Rayleigh fading environment, 8 to 9 dB of SNR gain is achieved for 802.11b WLAN signals. This SNR gain increases to 1214 with a 4-element adaptive antenna within the same environment.

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•

Multiple Input Multiple Output (MIMO) Antenna System

Ingram (2003) has proposed a multiple inputmultiple output (MIMO) antenna system for RFID tag identification to maximise system capacity in terms of bit/s/Hz. Ingram is the first researcher who has proposed multiple antennas for RFID tags for capacity improvement and anti-collision. The physical layer development of a multiple antenna based tag is reported by Collins et al (2005). A set of multiple antennas is used for the transmitter and multiple antennas are used in the receiver. Figure 11 shows the actual MIMO RFID system where N transmitting antennas are transmitting signals to L number of tags (Griffin & Durgin, 2007). The backscattered signals from the L number of tags are received by N number of receiving antennas in the reader.

Smart Antennas for Automatic Radio Frequency Identification Readers

A tag with at least a signal antenna or an array of antennas is placed in between the transmitter and receiver. The algorithms to compute the weight from both transmit and receive antennas are represented by the channel antenna gain as follows: H=

H11 H 21

H12 H 22

and

G=

G11 G12 G21 G22

(1)

where, Hij represents the channel gain of the transmit antenna and Gij represents the channel gains of the receive antenna. A computing device comprised of a microprocessor or a computer identifies the product channel matrix: CP = HG.

(2)

The identification of CP can be performed by sending a pilot signal from each transmitting antenna and measuring the responses at receiving antennas. Blind channel method can also be used to avoid the overhead with pilot signal. The computer device then performs a singular value decomposition or approximate identification of CP, given by: CP = UΣVH

(3)

where, the columns of U are the left singular vectors, Σ is a diagonal matrix of the singular values of CP, the columns of V are the right singular matrix and the superscript ‘H’ means conjugate transpose. The received output signal of the receiver is expressed as:

(

z (t ) = XCPW H m(t ) cos ωc + ωo t + θ + b cos(ωc t + θ ′) + n(t )

) (4)

where, X = [X1, X2] the weight vector of the transmitting antenna, W = [W1, W2] is the weight vector of the receiving antenna, m(t) is the information waveform, ωc is the RF carrier angular frequency, ωo/2π is the pulse repetition rate of a periodic square wave, θ and θ′ are sideband and carrier phases, respectively, b cos(ωct+θ′) is the carrier component and n(t) is the additive thermal noise. The other sidebands, which are also proportional to XCPWH, are not shown. Griffin & Durgin (2007) propose new analytic probability density function (PDF) expressions and several RFID tag design guidelines to exploit the reduction in small scale fading inherently available in the backscatter channel. According to Griffin and Durgin, in the RFID system uplink scenario, the backscatter channel is a pinhole channel and experiences small-scale fading, even under line-of-sight conditions. This is due to the fact that indoor operation is a cluttered reader environment and the tagged objects are inhomogeneous in nature. Ingram (2003) also states that even with a highly directional antenna array with beam steering capability this situation cannot be improved much. Griffin and Durgin (2007) propose the RFID channel as M×L×N dyadic backscatter channel consists of M transmitter (TX), L RF tags and N receiver (RX) antennas as illustrated in Figure 11. They assume the forward and backward links to be narrowband and present the baseband signal as: 1  y (t ) = H b (t ) S (t ) H f (t ) x (t ) + n (t ) 2

(5)

where y(t) is an N × 1 vector of received complex baseband signals, Hb(t) is the N × L complex baseband channel impulse response matrix of the

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Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 11. (a) The general M×L×N dyadic backscatter channel with M transmitter antennas, L RF tag antennas, and N receiver antennas. (b) The signal received at the n-th receiving antenna is the sum of ML Gaussian products, L of which are statistically independent (Griffin & Durgin, 2007, Copyright IEEE, 2007).

backscatter link, and Hf (t) is the L×M complex baseband channel impulse response matrix of the forward link [3]. S(t) is a narrowband L×L matrix that describes the time-varying modulation that an RF tag places on the radio signals absorbed and scattered by the L tag antennas. x(t) is an M × 1 vector of signals transmitted from the TX antennas, and n(t) is an N × 1 matrix of noise components. Figure 12 shows the probability density function (PDF) fα(α) where α is the channel envelop. Here we assume both forward and reverse channel links are Rayleigh distributed for ρ representing the correlation coefficient between the elements Hb(t) and Hf (t). Figure12 shows that the PDF of the dyadic (two folds) backscatter channel has deeper, more frequent fades than a one-way (1x1x1) Rayleigh channel. However, the PDF improves as the number of RF tag antennas is increased, reducing small-scale fading. For an equal number of RF tag antennas, the PDF for ρ = 1 (when a single reader antenna is used to transmit and receive) exhibits even deeper fades than for the ρ = 0 case. In former case, Hf is identical to Hb; however in most cases where separate reader TX and RX antennas are used, Hf and Hb are uncorrelated. In

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alternate terms, yn(t) is proportional to the sum of the number of tag antenna L independently identically distributed (i.i.d.) product terms that each represent a set of spatially separated and, therefore, independent propagation paths. As L increases, additional independent propagation paths reduce the probability that the envelope will fade. In other words, increasing the number of RF tag antennas increases the number of independent pinholes through which a signal may propagate. As L becomes very large, (3) approaches a Rayleigh distribution. Based on the analyses and results presented above, Griffin and Durgin (2007) propose the following RF tag antenna design guidelines: (i)

Multiple RF Tag Antennas: at least two RF tag antennas should be used. Closely spaced antennas can have low envelope correlation (Auckland, Kilmczak & Durgin, 2003), especially if they are cross polarized. Furthermore, each RF tag antenna must be used to modulate backscatter in order for the channel PDF to improve. Additional antennas used only for receiving power are of no communication benefit, resulting in passive

Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 12. Plots of the envelop PDF at the nth receiving antenna with ρ {0, 1} (Griffin & Durgin, 2007, Copyright IEEE, 2007. Used with permission.)

RFID tags that can power-up in some locations yet still not transfer information. (ii) Separate Reader TX and RX Antennas: In order to avoid fully correlated links (ρ = 1), which can only occur if the same antenna is used for the reader TX and RX, separate TX and RX antennas should be used. (iii) Higher Operating Frequency: Operating modulated backscatter systems in the 57255850 MHz industrial, scientific, and medical (ISM) band offers several advantages over the 902-928 MHz band. In this band, RF tags with multiple, uncorrelated antennas are easier to design while maintaining a small tag footprint. Maeda et al (2007) propose a wireless ID tag integrated with multiple antennas and an antenna direction sensor in order to improve the accuracy in location measurement of wireless ID tags. A gravity sensor such as a gyroscope or magnetic sensor can be used as the direction sensor in addition to the data received at the base station and the polarization of the tag antenna. Along with the estimation algorithm, a three orthogonal antenna arrangement in the ID tag is exploited in the location measurement.

In the measurement set up four base stations with highly directional log periodic antennas focusing at the centre of the room were used. The reader/base station antennas were vertically polarised. Two types of tag antennasa half wavelength dipole and a 17 mm diameter loop antenna to simulate the tag antenna were set at x-, y- and z-orientations for polarization diversity. The received power P(d), in each direction of X, Y (horizontal polarisation) and Z (vertical polarisation) was measured. The procedure was performed for each direction of the antenna. A cumulative probability distribution was derived from a distance error function. The 17 mm diameter loop antenna was predicted to be as good as the half wavelength dipole antenna, meaning that a tag with three dimensional orientation antennas can effectively be used for location measurement. Figure 13 shows the error distance of the three orientations and the average error of approximately 2.26 meters. Therefore, Ingram’s (2003) proposition to use multiple antennas in RFID tags and Griffin and Durgin’s (2007) proposition to push the operating frequency to 5.8 GHz ISM band to miniaturize and fit a plurality of antennas in RFID tags are well justified by the experimental findings by Maeda et al (2007).

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•

RFID Anti-collision Algorithm with Multipacket Reception

The objective of an efficient RFID reader is to read as many tags as possible within its reading range. However, the reader suffers from anticollision problems when many tags are residing in close proximity. RFID tags as miniaturized devices have limitations in computational power, memory and communication bandwidth. To overcome the anti-collision problem while reading multiple tags in close proximity in terms of location and frequency of operation, two anti-collision algorithms are usually usedthe slotted Aloha algorithm (EPCglobal, 2005) and the binary tree algorithm (Law, Lee & Siu, 2000). However, their tag reading rates are not high enough to simultaneously recognize a large volume of tags, especially when time is crucial in delay-sensitive applications such as a warehouse scenario where many items should be identified instantly.

Lee (2007) proposes multi-packet reception (MPR) capability in an RFID reader to enhance the RFID tag reading range. This is done with an antenna array which allows the reception of multiple responses transmitted by the tag simultaneously. Ward and Compton (1992) analyzed the throughput enhancement of S-Aloha packet radio networks with adaptive antenna array. In Lee’s model (Lee, 2007) the reader is equipped with an array of multiple antenna elements as shown in Figure 13. There are N tags in the vicinity the reader. The reader operates in the downlink sending interrogation message to the tags. M responding tags are synchronized in packet transmission because they transmit in response to the request message broadcast by the reader. In Lee’s MPR model, the MPR capability of a reader is characterized by a pair of positive integers {F, K} where F≤ K: F and K represent reception capability (throughput or successful reading) and collision-free capability (number of antenna elements or independent channels) respectively. Assuming i is the number of tags responding to

Figure 13. Cumulative errors distance of tag with 3-orthogoanl antennas with 4 base station antenna located in 4 corners of a room with shelves: (a) half-wavelength dipole antenna and (b) 17 mm loop antenna. Measurement is at 300 MHz (Maeda, Matsumoto, & Yoshida, Copyright IEEE, 2007. Used with permission).

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Smart Antennas for Automatic Radio Frequency Identification Readers

a given slot, there are four possible cases: (i) idle slot where i = 0, (ii) 1 K, collision occurs and no signal can be decoded. All i tags are correctly identified. In general, a receiver MPR system cannot separate signals whose number is more than the number of antenna elements. Thus K is assumed to be equal to the number of antenna elements and F is bounded by K. The detailed of both algorithms can be found in (Lee, 2007). Figure 14 shows throughputs (successful reading) of RFID tags with the two algorithmsbinary tree and S-Aloha protocols with various F and K combinations. As can be seen with the degree of freedom (F), the throughputs have increased significantly. Here K = 1 represents the baseline case without MPR capability. Therefore, channel estimation and signal separation techniques (which determine F) play as important a role in improving system performance as the receiving antenna (K).

In their previous work Lee et al (2004) have analyzed RFID throughputs with two anti-collision algorithmsS-Aloha and Binary tree splitting and two smart antenna systemsadaptive array antennas and MIMO antennas. The results are produced in Figure 15. As can be seen in the figure, the throughput (tags/slot) has increased with the binary tree split and MIMO antenna combination. This is due to the fact that in MIMO antennas, K number of elements possess K number of degrees of freedom giving K number of independent channels. On the other hand, a K-element adaptive antenna array nullifies K-1 interferers. •

Millimeter-wave Identification System

So far various smart antenna systems and MIMO antenna systems for improvements in the areas of RFID system capacity, location finding of tags, tag reading rate and anti-collision have been discussed. Research in these areas has been presented in detail. All aforementioned tag systems use frequency bands from a few hundred

Figure 14. Throughput of (a) binary tree (number of bits indicating the initial ID range of interests R = 12, tag density ρ = 0.5) and (b) S-Aloha (optimal response rate l and total number of tags N = 130). “A” and “S” stand for “Analysis” and “Simulation” respectively. (Courtesy Lee, J. Seoul University, Korea. Used with permission).

(a)

(b)

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Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 15. Maximum throughput vs degree of freedom (no. of antenna elements) (Lee, Das & Kim, copyright IEEE 2004. Used with Permission)

KHz up to 2.45 GHz. Griffin and Durgin (2007) propose a new class of 5.8 GHz frequency band RFID systems to accommodate multiple antennas in RFID tags for throughput improvement. While most RFID researchers and developers have been striving to develop low frequency and microwave RFID tag systems, an ambitious project has been undertaken by VTT – Millilab to develop a mm-wave identification system at 60 GHz frequency band and above. MMW-ID enjoys many advantages over conventional ELF and microwave frequency RFID systems. The MMW-ID advantages are as follows: • • • •

Extremely miniaturized tags Novel remote sensor principle based on mm-wave frequency carriers Extremely large bandwidth Beam steering in mm-wave will open up many new initiatives and demands in microelectro-mechanical system (MEMS) based antennas, actuators, oscillators and many other components.

This new technology will also open up new application areas of RFID such as wireless embed-

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ded health monitoring systems of patients, such as body temperature and heart beat recording in real time, in-room environment monitoring, ultra wide band short range data transmission, in-car and car-to-car traffic management, intelligent transportation and many more. Tuovinen and Vaha-Heikkila (2006) propose a MMW-ID system based on MEMS devices including membrane antennas, switches, phase shifters, oscillators and amplifiers. Their research is on the whole gamut of physical layer and protocol developments at MM-wave frequencies. On-going activities (as at 2006) are in the areas of beam steering antenna development for RFID readers, mm-wave ID tags and nano-components such as mm-wave nano-oscillators and nano-filters. The candidate antenna elements for the mm-wave smart array antenna are patch antennas, 45 GHz and 60 GHz GaAs membrane antennas, Yagi-Uda antennas and full wavelength rectangular slot antennas. The beam steering phased array antenna is a 4-element linear array antenna. Figure 16a shows the layout of the MEMS antenna array developed by Tuovinen and VahaHeikkila (2006). As can be seen in the figure, the antenna element is an inset-feed MEMS

Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 16. MMID phased array antenna: (a) layout and (b) beam patterns (blue lines: co-polar and red lines: cross-polar patterns) (Tuovinen and Vaha-Heikkila 2006, copyright IEEE 2006. Used with permission).

(b)

(a)

rectangular microstrip patch antenna element. The beamforming network is comprised of a coplanar waveguide (CPW) based loaded line phased shifter. The phase shifter is formed with a CPW loaded with MEMS switches on both sides of the CPW. By turning on and off the sequence of MEMS switches the reactive loading on the CPW is changed and required phase shifts are achieved. The connection from the microstrip line feed antenna element to the CPW based beamforming network is achieved via a transition between microstrip to CPW (Simons, 2001). The dc bias connections to the MEMS switches are via the DC bias control from both ends of the transition as shown in Figure 16a. The four phase shifters are connected to a 1-to-4-way power splitter developed by Uppsala University. The total area of the MEMS smart antenna array of 2.4 cm × 1.8 cm is extremely small compared to the conventional phased array antennas in mm-wave frequency bands, and this antenna has significant

advantages for portable and embedded electronic gadgets. The resultant beam from the antenna is shown in Figure 16(b). Up to 42° beam squint is obtained with an appropriate phase excitation for the 4-element antenna array. The expected reading distance is 1~2 meters with a path loss of about -80~-100 dB. Once fully developed, this MMW-ID will open up a new horizon for identification systems.

developMent oF phased arRAY ANTENNA FoR 900 MhZ RFID reader So far various smart antenna systems for RFID readers have been discussed, including the MMID system developed by VTT (www.vtt.fi). All published research has provided only superficial information in the commercial in confidence con-

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Smart Antennas for Automatic Radio Frequency Identification Readers

text, and the relevant information is available only in patent applications. Scholarly publications are seldom found in this field of research. In this section, the technical development of a planar phase scanned antenna developed by the Monash RFID Research Group led by the author (Karmakar, 2007) is presented. The 900 MHz frequency band has many applications in RFID systems. The application areas are: 868-870 MHz for short range devices in CEPT countries including Australia and most European countries that recognize the U.S. participation in the CEPT Radio Amateur License (http://www.arrl.org/FandES/field/regulations/ io/#cept, accessed 11 Sep. 2007); 902 – 928 MHz ISM band RFID for North America; 918-926 MHz RFID for Australia; and 950-965 MHz for Japan (Mendolia et al, 2005; Clampitt, 2006). Therefore, there is a demand for a smart reader antenna which can cover all these bands of operations. To fulfill this requirement the author has developed a 3×2-element planar phased array antenna operating at 900 MHz frequency band covering 100 MHz bandwidth. The objective is to make a low cost, low profile and light weight antenna for easy mounting and portable operation, aimed at mass markets worldwide. These research and development activities in RFID have also trained personnel for this potential RFID market. •

Array Antenna Design

To make the antenna compact and fully planar the most suitable candidate is the microstrip

patch antenna, but the conventional microstrip patch antenna is a half-wavelength resonator. At 900 MHz, the antenna element is sizable compared to the wavelength and is not portable and easily mounted. Therefore, a crescent antenna (Deshmukh & Kumar, 2006) has been used as the most suitable candidate for portability and ease of mounting. The salient features of the developed array antenna are provided produced in Table 1 below. Antenna Element Design. The antenna element is designed with full-wave electromagnetic solver CST Microwave Studio. Fig 17a shows the CST Microwave Studio generated half disk antenna with slot loading for broad bandwidth applications. The single antenna element is designed on FR4 substrate with a dielectric constant of 4.4 and loss tangent of 0.002. The thickness of the material is 0.787 mm. The radius of the half disk antenna is 8 cm. An L-shaped slot is added around the coaxial feed probe to increase the bandwidth to more than 100 MHz centered at 910 MHz. The total height of the antenna element is 25 mm. The single antenna element produces a RL bandwidth of more than 100 MHz centered at 910 MHz with a maximum RL of 35 dB. Beamforming Network Design. The next design is the beamforming network for the 3×2-element array antenna. Figure 17b below shows the layout of the beamforming network developed using Agilent’s Advanced Design System (ADS) 2005A. The beamforming network comprises a 1-to-6-way power divider, 4-bit reflection type

Table 1. Specification requirements of 900 MHz RFID reader phased scan array antenna

Frequency

670

910 MHz

1o dB RL Bandwidth

100 MHz

Beam scanning

3D azimuth and elevation beam scanning

Beamwidth

42°

Peak gain

12 dBi

Weight

Less tan 1 kg

Dimensions

3 cm x 50 cm x 50 cm

Smart Antennas for Automatic Radio Frequency Identification Readers

Figure 17. (a) Half disk single antenna element; (b) beamforming network; (c) complete array antenna layout and (d) photograph of the complete array antenna in a slick housing 4-bit phase shifter array Half disk patch

Bias control

Feed network

a

Feed probe

h

Ground plane

(a)

Output port antenna element

Input port

(b)

Antenna array

Baseplate Metallic barriers

(c)

phase shifters and biasing control of the 4-bit phase shifter array. All components are designed on Taconic TLX0 of thickness of 0.787, dielectric constant of 2.45 and loss tangent of 0.0006.The details of the reflection type phase shifter can be found in previous work by the author and a colleague (Karmakar & Bialkowski, 1999). The phase shifters are comprised of a quadrature hybrid

(d)

coupler in a very compact package with reactive loading. The reactive loading is comprised of high impedance transmission lines of various lengths followed by Philips BA682 pin diodes. A diode compensation technique is used to operate the diode in the 900 MHz band. The diode is a very low cost band switching diode with high parasitic capacitance at its OFF state. Detailed information

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Smart Antennas for Automatic Radio Frequency Identification Readers

about the diode compensation technique can be found in Karmakar (1999). The reflection type phase shifter is selected due to its large bandwidth and uniform configuration for all bits. The total beamforming network is delicately phase matched to each branch with zig-zag transmission lines. The feed network is a corporate type feed network with the first stage of a 2-way T-junction power divider followed by a three way power divider. Thus 1-to-6-way equal power division is achieved. In this primary design amplitude tapering was not attempted. However, the readers are referred to Karmakar (2003).

Integrated Array Antenna. Mutual coupling greatly affects the array antenna’s performance, especially when the antenna elements are brought too close together. The situation worsens when the heights of the antenna elements are considerable (in this case 2.5 cm). Metallic barriers have been used to reduce the mutual coupling effect between the antenna elements as shown in Figure 17c. Thus the antenna aperture area is maintained at 50 cm× 50 cm. This area is about 70% of a conventional patch array antenna at that frequency. Figure 17c shows the top view of the complete antenna array generated in CST Microwave Studio Layout

Figure 18. (a) s-parameter vs frequency of 1-to-6-way power divider; (b) input return loss of complete antenna array; (c) CST Microwave Studio simulated broadside gain pattern of the array and (d) measured squinted radiation patterns of the array (Pink: +20° and blue:-30° beams).

(a)

(b)

340

350

0

0

10

20

330

30

-10

320 310

40 50

-20

300

60

-30

290

70

-40

280

80

-50

270

90

260

100

250

110

240

120 230

130 220

140 210

150 200

(c)

672

190

(d)

780 Mhz

180

170

160

HP VP

Smart Antennas for Automatic Radio Frequency Identification Readers

platform. The antenna is housed in a microwave transparent plastic casing as shown in Figure 17d. The total weight of the antenna is about 0.7 kg, making it suitable for easy transportation and mounting. •

Results of Array Antenna

Figure 18a shows the s-parameters vs frequency for the 1-to-6-way power divider. The input return loss of the power divider is about 30 dB and the transmission loss for each output port is about -8 dB. The output ports are well phase matched at the center frequency of 910 MHz and fall linearly with the frequency. The maximum phase offset at the band edge is less than ±15°. The phase shifters have an average insertion loss of 3.5 dB in both on and off states and the maximum phase deviation of about ±18° for the four bit phase shifter array. This quantization error in phased array antennas is quite common. Fig 18b shows the input return loss of the assembled array antenna. The antenna is very well matched with more than 10 dB return loss in the prescribed bandwidth of 100 MHz centered at 900 MHz. Figure 18c shows the radiation pattern of the array antenna in the broadside direction. As the figure shows, the antenna generates a beam of 42° beamwidth with a gain of 12 dBi. Figure 18d shows the beam patterns when they are scanned at +20° and -30° from the broadside direction. The results indicate that the antenna is capable of scanning main beams in 3D orientation. However, due to the compact design with only 6 antenna elements the degree of freedom is confined to about ±40° elevation scan angles from the broadside direction. With the number of antenna elements this scan range can be improved at the expense of more complex beamforming network, associated switching electronics and a larger antenna aperture.

conclusIon RFID is an enabling technology which is transforming the identification, asset tracking, security surveillance, medicine and many other industries at an accelerated pace. This chapter has presented a comprehensive review of smart antennas for modern RFID readers. This comprehensive review has covered new ground as follows: (i) Analysis, synthesis and classification of various smart antennas for RFID readers developed recently, mainly by various commercial organizations. This classification is based on the unique features of the application specific RFID readers. They are: location finding for asset tracking and management in both retail chains and warehouse scenarios; anti-collision improvement using slotted-Aloha and binary tree algorithms and MIMO antenna systems; ESPAR and MRC adaptive antennas for maximising S/N ratios of receive signals from the tags and improving reading rate utilizing polarization and spatial diversity in smart antennas. To the best of the author’s knowledge such a comprehensive classification based on the available literature from various sources has not been presented previously. Smart antennas for RFID readers have created a new field of research, similar to the smart antennas for mobile communications a decade ago. (ii) Ingram (2003) has proposed multiple antennas in a tag to improve throughput. She has also recommended that MIMO antennas are more efficient in improving throughputs than a directional adaptive antenna. This finding has been reinforced by Griffin and Durgin (2007) in their throughput calculation, assuming a dyadic channel model to be the RFID propagation environment. They have suggested pushing the frequency of operation in the 5.8 GHz ISM band to exploit

673

Smart Antennas for Automatic Radio Frequency Identification Readers

the miniaturized tag antennas at the higher frequency. (iii) The scholarly literature available on smart antennas for RFID readers is lacking and it is very difficult to find technical information about recent developments. To overcome this shortcoming in smart antenna development in the educational arena, the author has investigated a 3×2-element compact planar phase scanned antenna array for RFID readers. A detailed design guideline for the component level physical layer development of the smart antenna has been presented. The antenna is low cost, compact and lightweight for portable applications and ease of mounting. The antenna is designed at the 900 MHz frequency band with 100 MHz bandwidth to cover a plethora of applications in this frequency band. The development certainly has a potential international market. Finally, tremendous strides have been evolving around RFID technology. These developments will make the RFID technology more ubiquitous, accurate, high speed and user friendly. Smart antennas for RFID readers have shed light on the solution of existing problems for various applications such as collision avoidance, location determination of tags, and reading capacity improvement of RFID readers. Almost every type of smart antenna has been developed to fulfil the demands of RFID applications. Now it is time for antenna designers to reap the benefit of this new enabling technology with innovative and active participation in this field of research.

ACKNoWlEDGMENT The work is supported by Australian Research Council’s Discovery Project Grant # DP665523: Chipless RFID for Barcode Replacement. The software supports from Agilent and CST are also acknowledged. Contract works by Muhammad

674

Saqib Ikram and Sushim Mukul Roy are also acknowledged.

reFerences Alien Technology Corporation (2005), BAP ALR-2850 reader data sheet, 2005. Retrieved from http://www.alientechnology.com/products/ rfid_readers.php in February 2006. Auckland, D.T., Kilmczak, W. & Durgin, G.D. (2003, October). “Maximizing Throughput with Ultra-Compact Diversity Antennas”, Proc. of IEEE VTC 2003-Fall, vol. 1. Orlando, FL, USA: IEEE, Oct. 2003, pp. 178–182. BALOGH RFID (2003), HYPER X LMB-6012 RFID reader data sheet, Jan 2003. Retrieved from http://www.balogh.cc/HyperX/Support/PDF/ LMB6012-6013.pdf in September 2007 Calmpitt, H.G. (2006). RFID Certification Textbook, 2nd Ed., Arlington Heights, IL, American RFID Solution LLC. Chung, K.K. T. & Liu, S. (2004), Antenna arrangement for RFID smart tags, US Patent no. US 6,703,935 B1, March 9, 2004 Collins, T.J., Gurney, D.P., Kuffner, S.L. & Rachwalski, R.S. (2005). Method and apparatus for multiple frequency RFID tag architecture, US Patent no. US 2005/0052283 A1, Mar. 10, 2005 Das, R. & Harrop, P. (2006) RFID Forecasts, Players & Opportunities 2006 – 2016, IDTechEx, London, UK,. Retrieved from http://www.idtechex. com/products/en/ view.asp?productcategoryid=93 on 11 September 2007. Deshmukh, A. A. & Kumar, G. (2006). “Various slot loaded broadband and compact circular microstrip antennas” Microwave and Optical Technology Letters, 48(3), 435-439. EPCglobal, (2005) EPCTM Radio-Frequency

Smart Antennas for Automatic Radio Frequency Identification Readers

Identity Protocols Class-1 Generation-2 UHF RFID Protocol for Communications at 860 MHz - 960 MHz, Version 1.0.9, January 2005. EPCglobal, Inc. (2006), The EPCglobal Network: Overview of Design, Benefits and Security, EPCglobal white paper. Retrieved on 11 April 2006 from http://www.epcglobalinc.org/ Griffin, J.D. & Durgin, G.D. (2007). “Reduced fading for RFID tags with multiple antennas”, IEEE Antenna Propagation Society International Symposium Digest 2007, July Honolulu, USA. GTA (2007). RFID Industry Group RFID Applications Training and RFID Deployment Lab Request Background Paper January, 2007 Hercht, K. & Storch, L. (2007). Sequenced antenna array for determining where gaming chips with embedded RFID tags are located on a Blackjack, poker or other gaming tables and for myriad other RFID application, USA Patent no. US 2007/0035399 A1, Feb. 15, 2007 Ingram, M.A. (2003). Smart reflection antenna system and method, US patent no. US 6,509,836, B1, Jan. 21, 2003 Karkkainen, M. & Ala-Risku, T. (2003) “Automatic Identification – Applications and Technologies”, Logistics Research Network 8th Annual Conference, London UK, September 2003. Karmakar, N.C., & Roy, S.M. (2007). “Development of a planar phased array antenna for RFID readers at 900 MHz”, Proc. 2nd International Conference on Sensing Technology (ICST 2007) (pp. 202-204). Palmerston North, New Zealand. Karmakar, N C & Bialkowski, M E (2000), “Electronically Steerable Array Antennas for Mobile Satellite Communications - A Review”, Proceedings of the IEEE International Conference on Phased Array Systems and Technology, Dana Point, CA., USA, May 21-25, 2000, pp. 81-84.

Karmakar, N C and Bialkowski, M E (1999), “A Compact Switched-Beam Array Antenna for Mobile Satellite Communications”, Microwave and Optical Technology Letters, 21(3), 186-191. Karmakar, N C and Bialkowski, M. E. (1999). “A Compact Switched-Beam Array Antenna for Mobile Satellite Communications”, Microwave and Optical Technology Letters, 21(3), 186-191. Karmakar, N C and Bialkowski, M. E. (1999). “An L-Band 90° Hybrid-Coupled Phase Shifter Using UHF Band PIN Diodes”, Microwave and Optical Technology Letters, 21(1), 51-54. Karmakar, N. C., Roy, S. M., Preradovic, S., Vo, T. D., Jenvey, S. (2006) “Development of LowCost Active RFID Tag at 2.4Ghz”, 36th European Microwave Conference, pp: 1602-1605, 10-15 September, 2006. Karmakar, N.C. (1999). Antennas for Mobile Satellite Communications, Unpublished doctoral dissertation, The University of Queensland, Australia. Karmakar, N.C. (2003). “A Low Sidelobe SubArray of Portable VSATS” Proc. 2003 IEEE International Antennas and Propagation Symposium and URSI North American Radio Science Meeting, Columbus, Ohio, USA, June 22-27, 2003. Kocer, F, Flynn, M.P. & Long-Range, A. (2005), “RFID IC with On-Chip ADC in 0.25 /spl mu/m CMOS”, Digest of Papers 2005 IEEE Radio Frequency Integrated Circuits (RFIC) Symposium, pp: 361-364, 12-14 June, Long Beach, California, USA, 2005. Kowalski, J. Serra D. & Charrat B. (2005), RFID-UHF Integrated Circuit, US Patent no. US2005/0186904 A1, Aug. 25, 2005 Lai, K.Y.A, Wang, O. Y.T., Wan, T.K.P., Wong, H.F.E., Tsang, N.M., Ma, P.M.J., Ko, P.M.J. & Cheung, C.C. D. (2005), Radio frequency identification (rfid) system, European Patent Application EP 1 724 707 A2, 22 July 2005.

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Landt. L. (2001). Shrouds of Time. The History of RFID, An AIM Inc. publications, October 2001. Retrieved September 11, 2007 from http:// www.aimglobal.org/technologies/rfid/resources/ shrouds_of_time.pdf. Law, C. Lee, K. & Siu, K.Y. (2000). “Efficient Memoryless Protocol for Tag Identification”. Proc. 4th ACM International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, August 2000. Lee, D.V. (2005) RFID reader with multiple antenna selection and automated antenna matching, US Patent No. 6,903,656 B1, June 7 2005 Lee, J. (2007). Wireless Networks Characterizations: Interference, Collision, and Localization. Unpublished doctoral dissertation, Soul University, Korea. Lee, J., Das, S.K. & Kim, K.A. (2004). “Analysis of RFID anti-collision algorithms using smart antennas” Sensys ‘04, Nov. 3-5, 2004, Baltimore, Maryland, USA, 265-266. Maeda, T., Matsumoto, T. & Yoshida, H. (2007, July). “A study on the accuracy of location measurement of wireless ID tags integrated with multiple antennas and an antenna direction sensor”, IEEE Aps Digest 2007, Honolulu, USA. Marsh, M.J.C., Lenarcik, A., Van Zyl, C.A. Van Schalkwyk, A.C. & Oosthuizen, M.J.R. (1996). Detection of multiple articles, US Patent no. 5,519,381, may 21, 1996. McFarlane, D. & Sheffi, Y. (2003). “The Impact of Automatic Identification on Supply Chain Operations”, International Journal of Logistics Management, 14(1), 407 – 424. Mendolia, G. Gupta, O. & Toit, C.F. (2005). RF ID tag reader utilizing a scanning antenna system and method, US Patent No. US 2005/0113138 A1, May 26, 2005

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Nakamura, T. & Seddon, J. (2006) Omron Develops World’s First Antenna Technology That Boosts UHF RFID Tag Read Performance, Omron Corporation press releases. Retrieved from http://www.omron.com/news/n_270306.htmlC in June 2007. Padhi, S. K., Karmakar, N. C., Law, C. L., Aditya, S. (2001). “A Dual Polarized Aperture Coupled Microstrip Patch Antenna with High Isolation for RFID Applications”, 2001. IEEE Antennas and Propagation Society International Symposium, vol. 2, pp. 2-5, Boston, USA, July 2001 Pozar, D. M. (2005). Microwave Engineering – 3rd Edition, John Wiley & Sons, Inc., NY, USA, 2005. Preradovic, S. & Karmakar, N.C. (2007). Modern RFID Readers – A Path to Universal Standard. Microwave Journal, 13. Retrieved from http://www. mwjournal.com/Journal/Issues.asp?Id=68) Profibus (2007) Device Net-Ethernet IP-ModbusRemote I/O, Retrieved from http://www.rfidinc. com in June 2007. RFID Analyst (2003). Issue 26, March 2003. Retrieved from http://rfid.idtechex.com/ documents/ en/sla.asp? documentid=67 in Sep 2007. Salonen, P. & Sydanheimo, L., (2002). “A 2.45 GHz digital beam-forming antenna for RFID reader”, Proc. 55th Vehicular Technology Conference, 2002, 4, 1766 - 1770 Simons, R (2001). Coplanar Waveguide Circuits, Components, and Systems, New York, Wiley. Stockman, H. (1948). “Communication by Means of Reflected Power”, Proceedings of the IRE, 1196-1204. October 1948. Sun, C. & Karmakar, N. C. (2004). “Direction of Arrival Estimation with a Novel Single Port Smart Antenna”, EURASIP Journal on Applied Signal Processing, 2004(9), 1364-1375.

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Sun, C., Hirata, A, Ohira, T & Karmakar, N. C. (2004). “Fast Beamforming of Electronically Steerable Parasitic Array Radiator Antennas: Theory and Experiment”, IEEE Trans. Antena. Propagt., 52(7), 1819-1832.

Wang, J.J.M., Yang, C.C. & Lin, W.Y. (2004). Method and apparatus for GPS signal receiving that employs a frequency-division-multiplexed phased array communication mechanism, US Patent no. US 6,784,831 B1, Aug. 31, 2004.

Tuovinen, J. and Vaha-Heikkila, T (2006), “MMID-Millimetre-Wave Identification using RF-MEMS and Nanocomponents”, 2006 European Microwave Conference Workshop Notes WS14 RF MEMS and RF Microsystem Application and Development in Europe, Manchester, UK, 2006.

Ward, J & Compton, R.T. Jr (1992), “Improving the Performance of a Slotted ALOHA Packet Radio Network with an Adaptive Array”. IEEE Trans. Commun., 40(2), February 1992.

This work was previously published in Handbook on Advancements in Smart Antenna Technologies for Wireless Networks, edited by C. Sun; J. Cheng and T. Ohira, pp. 885-890, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 3.10

Getting to Know Social Television: One Team’s Discoveries from Library to Living Room

Gunnar Harboe Motorola, USA Elaine Huang Motorola, USA Noel Massey Motorola, USA Crysta Metcalf Motorola, USA

abstract This chapter presents results from an ongoing social television project, in the context of other research in the field. The authors give a detailed description of the STV prototype used in their research, and summarize their studies, which provide the findings explained in the rest of the chapter. Three major research focuses are identified, namely evaluation and validation of Social TV systems, communication modality comparison, and detailed observation of user behaviors. Based on the findings in these areas, the authors list three major open questions and challenges for the field: multi-user support, new equipment requirements, and the creation of distinct and

Ashley Novak Motorola, USA Guy Romano Motorola, USA Joe Tullio Motorola, USA

unique social television experiences. Finally, the chapter suggests that the emphasis within social television may be moving from research to design, implementation and deployment.

IntroductIon Over the course of this decade, social television has gone from a marginal idea to a major focus area within the field of interactive TV (ITV). As members of an ongoing Social TV project in Motorola’s applied research division, we have studied the issues around the subject extensively. In this chapter, we aim to discuss some of the things we have learned about social television,

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Getting to Know Social Television

point out a few major unanswered questions and unsolved problems with social television, and offer a perspective on where social television is headed. Throughout, our own research and experiences are used as a jumping-off point, complemented by findings from the growing literature on the topic. The main part of this chapter is devoted to three big questions that have attracted much attention. We look at attempts to evaluate the effectiveness and appeal of social television experiences, and find that their potential has been largely validated, although some important misgivings remain around privacy, disruptions, and systems that do not offer full freeform communication. We next examine comparisons of different communication modalities, primarily text, voice and video. Our findings indicate that contrary to previous, inconclusive studies, text is a better communication option than voice in the context of in-home social television, while video does not appear to be a suitable option. Finally, we describe the observed usage patterns of Social TV systems. We find that in naturalistic use, conversations are not as closely tied to the TV content as has previously been thought. On the other hand, television presence provides an important link between viewing behaviors and social interactions. Because each topic draws widely on results from all our different studies as well as from the literature, the chapter is not broken up by individual study. Instead, to provide the necessary background for the reader to assess the findings we present, and explain where we are coming from, we first describe the prototype system used in our

research, followed by a summary of the research we have conducted, before we start talking about what we have learned.

STV: A SoCIAl TElEVISIoN systeM At Motorola, we have explored several different kinds of social experiences around the TV. One of the experiences that has received most attention is to allow a small group of friends or relatives to share a feeling of contact or togetherness while watching TV; a “virtual couch.” As part of this research, we have developed a series of prototypes, collectively known as STV, for use in lab and field trials (Table 1). The first iteration, STV1, consisted of a simple, single-session audio link between households, which allowed users to communicate via open room-microphones and hear their conversation partners through their television speakers, mixed with the TV audio (Harboe, Massey, Metcalf, Wheatley, & Romano, 2008a). There was no visual user interface, and the only control given to users was the ability to adjust the relative volumes of the TV audio and the voice audio using a remote control. Later prototypes have been more elaborate, providing fully integrated Social TV systems that could be deployed in the field for extended periods of time. Since their features largely overlap (the biggest difference being an evolution in communication capabilities), we will describe them together in more detail. The main features of these prototypes are television presence (pro-

Table 1. Versions of the STV social television prototype. Version

Presence

Suggestions

Communication

STV1

No

No

Voice

STV2a

Yes

Live, Future

Emoticons

STV2b

Yes

Live, Future

Emoticons, pre-defined text-messages

STV3

Yes

Live

Emoticons, text chat, voice

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Getting to Know Social Television

vided in the form of a buddy list and an ambient display), program suggestions, communication (including at various times graphical emoticons, pre-defined text messages, text chat and voice communication) and historical information such as viewing habits.

watching. The user can tune directly to those stations by highlighting the buddy and pressing Select on the remote, joining whatever channel the buddy is watching.

presence

The ambient display provides a way for users to be aware of their buddies’ presence status even when the TV is turned off. An Ambient Orb3 (Figure 2) displays aggregate presence information by glowing in different colors depending on how many buddies are currently active watching TV on the system, with separate states for no buddies, one buddy, and multiple buddies active. The ambient display is described in more detail in Harboe et al. (2008b).

Presence information provides users with awareness of other users’ current state, particularly their availability or whether they are currently active on the system. It can also include other information about their context. Television presence information, for example, lets users know about their buddies’ current TV viewing. In STV, the two most important presence features are the buddy list and the ambient display.

Buddy List The buddy list displays the user’s contacts (“buddies”) on the STV network, and shows their current status (Figure 1). Buddies who are not currently watching TV, or who have logged off in order to watch in private, show up in the list as unavailable. The STV buddy list is similar to buddy lists in instant messaging (IM) applications such as Windows Live Messenger1 or AIM2,but provides additional television-specific context, showing the station and program each buddy is Figure 1. Screenshot of the STV buddy list.

680

Ambient Display

Program Suggestions In addition to the ability to join what a buddy is watching through the buddy list, it is also possible to actively ask buddies to watch something. Program suggestions help users let each other know about specific shows, and arrange to watch them together. Upon receiving such a program suggestion, a user can accept and automatically tune to the program, or close the suggestion without accepting (Figure 3).

Figure 2. Ambient “orb” display sitting next to the television.

Getting to Know Social Television

Figure 3. Screenshot of incoming program suggestion.

tions: emoticons and pre-defined text messages. When these features proved unsatisfactory, STV3 restored voice support, and also added text chat, providing a choice of freeform communication options.

Closed-Form Communication

STV2a and STV2b also let users send suggestions for upcoming shows. If the recipient accepted the suggestion, the show was scheduled so that a notification would remind them when it was about to start. STV has no DVR capability, so there was no option to record the show. Perhaps because of this limitation, suggestions for upcoming shows were rarely used, and the feature was removed in STV3 to simplify the user interface.

Communication Different iterations of STV have included different forms of communication. Although STV1 supported voice, this function was not included in the STV2 prototypes, which instead tested more limited, closed-form communication op-

Two forms of simple, closed-form communication have been tested in STV, both of them restricted to users watching the same program. Graphical emoticons, present in all versions since STV2a, are meant as a quick way to comment on the program or exchange greetings without necessarily getting engaged in further conversation. Currently, this takes the form of “thumbs up” and “thumbs down” smileys (Figure 4b), which can be sent using dedicated remote control buttons. A third smiley, called “shoutout” and representing a generic greeting or exclamation, was included in STV2a, but proved redundant and was removed from later versions. In STV2b, the shoutout was replaced with a set of around 20 fixed, pre-written text messages and responses, with phrases such as “How is this show?”, “This sucks!”, and “Call me” (Figure 4a). The content of the messages was picked based on input from study participants so as to express the most important things users might want to say to each other. After poor participant response in the field trial of this version, the pre-written

Figure 4. Closed-form communication in STV. (a) Pre-defined text messages. (b) Emoticons.

(a)

(b)

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Getting to Know Social Television

messages were scrapped, and replaced in STV3 by full freeform communication in the form of voice and text chat.

Text Chat The text chat feature of STV3 is similar to IM, in that users select a buddy from their list (the buddy must be active, but can be watching a different channel), type them a message, and that message then immediately appears on the buddy’s screen, initiating a conversation. The chat window can easily be hidden so as not to obscure the TV program while waiting for a response. Users can be in multiple text chats at the same time, switching between them (Figure 5), but in our current system each conversation is one-to-one; we do not support group chat for text. Text entry is through a wireless Bluetooth keyboard, for ease of typing.

Voice Chat In STV3, as in STV1, voice communication is made possible by establishing an audio link with one or more buddies. This is done in STV3 by selecting them from the buddy list and calling them. If any of the buddies accepts the incoming call, the audio link is set up and the call starts directly. Group calls are possible; a call can be set

Figure 5. Tabbed text chat window with two ongoing conversations.

682

up with multiple people from the start, or more can be invited to an ongoing call (Figure 6). There is no particular ownership of a call; any participant can invite more people to join, and the call lasts for as long as any two participants remain. It is only possible to join a call by invitation, and each user can only be in one call at a time. While in a voice conversation, or when invited to a voice call, users can see a list of all the current participants in the call. For voice communication, users have an echocancelling room microphone placed somewhere central in the room, for example on the coffee table. The voices of the remote participants are transmitted through the television speakers, mixed in with the audio from the TV program. The microphone sensitivity and the volumes of the voices and the television program audio can be muted and adjusted independently. Users can start and maintain voice conversations with their buddies whether or not they are watching the same TV station or program. It is also possible to take part in both a voice and text conversations simultaneously, either with the same participants (e.g. using text as a backchannel in a voice conversation), or in different conversations with different people.

Figure 6. Screenshot of the calling process. Conversation presence in the top right lists participants in the call.

Getting to Know Social Television

historical Information In order to make the TV content more socially meaningful, STV provides users with some awareness of their buddies’ viewing habits. There is a viewing history which lists the programs each buddy has watched. Users can also see a list of favorite (most frequently watched) shows for each user, and for their buddy list as a whole (Figure 7a). These shows are also marked in the Program Guide grid with a little buddy icon (Figure 7b). Finally, another option allows users to view a list of shows they have in common with another buddy, meaning shows they have both watched. A program only counts as having been watched if the user watched at least ten minutes in total of a single broadcast of that program. Programs that a user watches while logged off are not counted, and users can remove programs from their own viewing history.

relationships through a virtual experience of being ‘physically’ close,” and that sharing the experience of commercially created content is an important way to maintain a personal connection (Bentley, Metcalf, & Harboe, 2006). In one study in particular, we noticed people calling each other during a TV show they were both watching, so that they could watch it “together.” This led us to explore the concept of social television. Our research to date has included paper prototyping tests, lab tests, usability studies, participatory design sessions and technical studies. However, the results presented here are drawn from four main studies (Table 2). Initial concepts were evaluated in a focus group study. The different STV prototypes were then tested in a series of in-home field trials. We will describe the methods used in each study in detail, followed by a summary of our data analysis methods, before going into our actual findings.

Focus Group Concept Study

our research The social television research in Motorola originated in several studies that we conducted on family communication and use of media. These found that “people who want to build/maintain relationships but live far apart can use mediated communication technologies to develop ‘close’

We ran a concept study to test the appeal of communication and interaction through the TV, as well as four other, more advanced, concepts (which will not be addressed in this chapter). The primary purpose was to guide the future course of the project. The study consisted of seven focus groups (and a pilot group) followed by a data

Figure 7. Historical information in STV. (a) Most popular shows. (b) Program guide with popular shows marked with a smiley face.

(a)

(b)

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Table 2. Overview of Motorola Social TV studies. Publications referenced: (1) Harboe., Massey, Metcalf, Wheatley, and Romano, 2007; (2) Harboe et al., 2008a; (3) Harboe et al., 2008b; (4) Metcalf et al., 2009; (5) Tullio, Harboe, and Massey, 2008; (6) Huang et al., 2009. Study

System

Method

Participants

Publication

Concept Study

[N/A]

2-hour focus groups

7 groups, 53 participants

(1), (2)

STV1 trial

STV1

1-hour field trials

4 groups, 8 (+1) households

(1), (2)

STV2 trial

STV2a

2-week field trials

2 groups, 10 households

(3), (4)

1,3-week field trials

2 groups, 9 households

(5), (6)

STV2b STV3 trial

STV3

analysis workshop. In the focus groups, which lasted for two hours, participants were shown a number of storyboards depicting possible Social TV scenarios and were asked to discuss them with respect to their own needs and lifestyles. They also individually filled in worksheets with quantitative ratings (using Likert scales) and other information. In all, 53 people participated in the focus groups. We had 6 groups of 8 participants, representing teens (17 to 19); young adults (25 to 35); and baby boomers (42 to 61), split into separate male and female groups. Finally, a group of 5 male Xbox Live gamers (18 to 25) was included. The groups were selected to include people with and without children, and for other demographic characteristics considered relevant. The concept study was run by an independent consultancy, with researchers from Motorola involved at each step of the design and execution.

STV1 Prototype Field Trials For these field trials, we used the STV1 voice-only prototype (described above). The prototype was set up in the participants’ own homes for a live TV show of their own choosing. We ran four trials with STV1. Each trial lasted for one hour, and the participants watched live programming that had been agreed upon in advance. Participants were recruited from our social networks using the friend-of-a-friend method. In all, there were 11 males and 8 females participating in 9 different

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households, and all were non-engineers (Table 3). The programs consisted of a basketball game, an American football game, an animated comedy followed by a sitcom, and a home decorating show. The participating households were connected to friends and their friends’ families, except in the second trial which brought together a family by connecting a woman with her son, daughter-in-law and grandchildren. In the first trial, one of the 3 participating households was that of a Motorola employee familiar with the project, and data from this household was not used in the analysis. The participants were video-taped during the trial (the researchers were not present), and interviewed immediately afterwards. We also recorded the programs they watched.

STV2 Prototype Field Trials We ran two separate in-home trials of STV2, using slightly different versions of the prototype (STV2a and STV2b). The focus in this iteration was on the presence awareness, with limited capabilities for communication through suggestions, emoticons, and (in the second half of the study) pre-defined text messages. Five households were recruited for each trial, and each trial was conducted over a two-week span. Participants were recruited using an independent recruiting agency. Its instructions were to find social groups in which the various household members were mutual friends, and all had strong ties with one another. A number of different kinds of relationships were represented

Getting to Know Social Television

Table 3. STV1 field trial sessions Trial

Households

Participants

Programming

1

2 (+1)

5-10 (varying)

Basketball

2

2

5

American football

3

2

2

Family Guy, Full House

4

2

2

Trading Spaces

Figure 8. Social network graphs of relationships between households in our field trials. The solid lines represent friendships and close kinships (siblings), and the dashed lines represent more casual acquaintances.

(a)

(b)

in the two social groups, but neither group was completely tight-knit (Figure 8a, b). All the recruited participants were women. However, other family members such as husbands, fiancés, and children also used the system, and were secondary participants of the study. We used multiple methods for data collection, including semi-structured interviews, automated usage logs, and voice mail diaries. Each household was interviewed three times. The initial interview lasted about half an hour and was used to collect background information about viewing habits, communication routines, and social relations between the participants. In a phone interview after the first week, lasting between 15 and 30 minutes, we gathered data about the participants’ use of and reactions to the prototype in the first week. The final interview, lasting from an hour to an hour and half, was structured to collect more detailed information on their use of and reactions to the prototype. We logged all interactions with the system, including when the TV turned on and off, and

(c)

(d)

all button presses during system use. Voice mail diaries were used to collect information on behaviors surrounding the ambient presence features and other communication with other people in the study, since these did not appear in our logs.

STV3 Prototype Field Trials This study, which used the STV3 prototype, was run in two stages: first a preliminary one-week trial with four households participating, then a more elaborate three-week trial with five households. For the preliminary trial, we contacted three participants from the first group of the STV2 study (A1, A2, and A5) who formed a tight-knit group. A fourth participant, A6 (the sister of A2 and A5) was also added (Figure 8c). The participants were between the ages of 25 and 35. The system was deployed in their households for a period of one week. As in the STV2 study, an initial interview was used to gather background data, a phone interview was conducted mid-week to investigate participants’ experiences with the system during

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the first half of the week, and a final interview was conducted at the end of the week to examine their experiences in-depth. We also logged all interactions and all text chats, and recorded all voice conversations held through the system. For the longer trial, a recruiting agency recruited five participants, all of whom were males between 30 and 36. All were sports fans, and most were old friends (Figure 8d). The study was run during “March Madness,” the American college basketball playoff series. For one week, the participants used the system without being able to connect to use the social features, in order for us to gather baseline data and to populate their viewing histories. After that, the social features were turned on, and they could use the system to communicate for two weeks, until the end of the study. The data gathering was as in the preliminary trial.

Analysis In each study, we used a form of grounded theory analysis to interpret the data, applied as an affinity diagram technique (Bernard, 1998; Beyer & Holtzblatt, 1998). We reviewed the interview data, and (when available) the observed or recorded data from the usage sessions. We pulled out direct quotes and observations from the sessions and interviews that addressed the research questions. These items were grouped using the affinity method. Insights were drawn from the patterns that emerged, and the resulting organization of the data surveyed to answer the research questions. For the focus groups and the STV2 and STV3 trials, we also performed statistical analyses of the data (ratings and usage logs, respectively). This quantitative data is mainly descriptive, especially for the field trials, since the small scale, non-experimental setup and numerous confound-

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ing variables don’t permit us to draw statistically sound conclusions about any effects. The results produced by our analysis, based on data from all the separate studies, form the material for the following section.

WhAT Do WE KNoW ABoUT SoCIAl TElEVISIoN? In addition to the primary research just described, we have been able to draw on a growing social television literature. Research around social television has mainly clustered around a few big questions. We can sum these up as: validating the effectiveness and appeal of social features around video; exploring and comparing specific features and communication modalities to produce more optimal designs; and studying the behaviors around use of social television systems. Through the studies detailed above, we have made important contributions to answering each of these questions, which we will deal with in order.

Effectiveness and Appeal As a concept that has never been widely deployed, social television requires some validation to test whether it is actually something people want or would enjoy, and whether it is useful or has other positive effects. The flipside of this question is the need to identify the concerns it causes and any potential negative effects it could have. This section will describe attempts to quantify the social and psychological effects of social television use, as well as people’s response to it. We will then look at qualitative studies that describe people’s first reactions to the idea without having used it, and compare these to studies of people’s actual experiences with different Social TV systems. Finally, we examine the issue of privacy, which is crucial to the acceptability of social television.

Getting to Know Social Television

Quantitative Measures: Ambivalence A fundamental question to ask about social television is whether it really does provide a social experience. A number of researchers have studied the problem using quantitative methods. Baillie, Fröhlich, and Schatz (2007) compared the “joint TV experience” and “social presence” of watching TV in the same room to watching together through the AmigoTV system. They found that using audio chat did not differ significantly on these measures from actually being in the same room, but communicating only through graphical symbols was significantly lower on both. Similarly, de Ruyter, Huijnen, Markopoulos, and IJsselstein (2003) found that group attraction was significantly higher for participants watching TV when they had a live video view of their friends watching the same thing than when they only had a sketch visualization or no view at all. Along the same lines, Weisz et al. (2007) found that the ability to text chat while watching video on the computer significantly increased feelings of closeness with and liking of other participants. Finally, participants in a 9-week trial of ConnecTV felt significantly more connected when using the ConnecTV system than when watching regular TV in a control condition (Boertjes, Klok, & Schultz, 2007). Plainly, there are social television features that increase television sociability, at least when compared to solitary viewing. As reassuring as it is that social television clears this fairly low bar, the last study, the ConnecTV trial, deserves a second look. In addition to measuring connectedness, the researchers collected four other ratings from participants, on the scales pleasant–unpleasant, activated–calm, satisfied–irritated, and inspired–bored. On all scales, ConnecTV scored worse than just watching television. Participants were less pleased, less calm, more irritated and more bored. After the trial, only slightly over half of respondents (17, N = 31) regarded the system as an interesting addition to watching TV (Boertjes et al., 2007).

Frustration over technical difficulties may have depressed ratings in this particular study, but the question remains: is Social TV something people even want? In our own concept study, focus group participants rated their response to the ideas presented in storyboard scenarios on a 5-point Likert scale. The ratings showed a response that can best be described as lukewarm, falling mostly near the middle of the scale (M = 3.0, SD = 1.04, N = 53) (Figure 9). Weisz et al. (2007) report a similar result for watching films while chatting on the computer; enjoyment was moderate (M = 4.3, SD = 1.6, N = 15, on a 7-point scale). In contrast, Regan and Todd (2004a; 2004b) report that 93% of participants in their study (N = 32) answered that they would like to IM with others while watching TV, and Baillie et al. (2007) also found that 93% (N = 30) of their participants would like to use a Social TV application. Harrison and Amento (2007) got a somewhat weaker, but still positive, response to their CollaboraTV system, with 69% of participants saying they would use the system, and most agreeing that it made watching TV more enjoyable (M = 3.56, N = 32, on a 5-point scale). Results from a later CollaboraTV study by Nathan et al. (2008) were similar (M = 3.57, SD = 0.65, N = 16). Figure 9. Focus group participant ratings of their interest in a generic social television concept. Likert scale from 1 (least interested) to 5 (most interested).

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A lot of this variation can be attributed to differences between systems and study conditions, especially the recruiting. Both Regan and Todd and Baillie et al. specifically recruited participants believed to be receptive to buying this kind of product, while Harrison and Amento recruited employees of, and Nathan et al. former interns at, a corporate research lab. The participants in the other studies, on the other hand, were recruited from the general public, or from particular communities without a predictable bias. This may indicate that social television appeals primarily to certain demographics, and does not currently inspire widespread interest.

First Impressions: Wariness In addition to responses measured quantitatively on Likert scales, there is a wealth of qualitative data on people’s reactions. This information helps to paint a richer picture of the aspects of social television that excite people, and the things that concern them about the idea. Three similar studies all point towards the same concerns. In our focus groups, the mixed reactions to the concept were expressed in words such as “intriguing,” “interesting” and even “wonderful,” on the one hand, but, “weird,” “unnecessary” and “pointless” on the other. Matching descriptions were elicited by Ali-Hasan (2008) in a participatory design exercise and by Hess (2008) in a user forum. In each case, the concerns of skeptical participants soon turned to privacy. The focus group participants worried about others being able to see what they were watching at any given time. Most argued that this social transparency overstepped the boundaries of acceptability, and several invoked “Big Brother,” perhaps recognizing in STV the telescreen from Nineteen Eighty-Four (Orwell, 1949/1990). As in the studies by Regan and Todd (2004b) and Ali-Hasan (2008), participants articulated their discomfort in variations of, “I don’t watch porn, but if I was…”; except for a few male participants who

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were more worried about getting caught watching programs targeted at women. The more general point expressed in these examples is that people worry about being embarrassed about their TV viewing. After all, many popular TV shows, as well as television viewing in general, are held in low regard critically and culturally. These “guilty pleasures” may therefore not reflect well on the viewer: “If someone was watching stupid shows all the time, I would think, ‘God, what a moron are they!’ to be watching these dumb shows.” Some of our focus group participants could see no value in linking personal communication to broadcast content. They felt that a television program would be an irrelevant distraction from their interaction with other people, for which existing communication technologies (telephone, email, IM, etc.) or face-to-face meetings were sufficient. The lack of perceived utility was captured in the words “toy” or “novelty,” terms also heard in other studies (Metcalf et al., 2009; Ali-Hasan, 2008).

Experiences: Interest and Appetite While user panels and focus groups are cautious in their response to social television, field studies where people get a chance to use it reveal a different and far more positive reception. The enthusiasm is apparent in the testimonials about Zync, the video sharing IM plugin, collected by Shamma et al. (2008) “Let me start by saying, I absolutely love Zync, currently myself and my wife are about 2000 miles apart but we love to watch movies together and it allows us to talk and watch together and its the closest thing we have to actually being together.” The participants in the STV1 trials were similarly very positive, on the whole, towards the concept and their experience with the prototype. We consistently found evidence that social television added value over and above watching alone, in a number of different ways. It helped to relieve boredom, allowed the participants to

Getting to Know Social Television

share their interests, gave them someone to ask questions of and show off their knowledge to, relieved loneliness, and heightened the intensity of exciting moments (cf. Weisz et al., 2007). In some cases it came close to the experience of actually being together: “I kind of forgot I was talking through the television at some point. I just was talking and could hear Mom.” “It felt like she was in the room.” Participants in the trial of the STV2 presenceoriented prototype expressed frustration about the lack of communication options (a reaction shared by participants in the Boertjes et al. (2007) pilot study of the similarly restricted ConnecTV system), and their overall reaction was more negative. When some of them had a chance to try STV3, which incorporated voice and text communication, they all agreed that it was a significant improvement, describing it as “a lot more fun.” Other participants in the STV3 study said the same thing: “I certainly liked it. It was a nice means to communicate and interact with my friends who share some of the same interests.” “It opens and creates new avenues and communication experiences that I didn’t have with a normal TV.” Other questions find less clear-cut answers. Some focus group members were concerned being in communication with others would interrupt and disrupt their viewing, causing them to miss parts of the show they were watching. They argued that television was their “down time,” the only chance they had to relax and unwind. In the tests of STV1, however, voice conversations generally did not prove to be greatly disruptive. This agrees with the findings by PARC researchers (Oehlberg et al., 2006; Ducheneaut et al., 2008) in a similar experimental setup. In our later prototypes, some participants expressed impatience with the amount of on-screen notifications and messages, calling it “a chore” to deal with, while others enjoyed the fact that they could use their downtime to stay in touch with friends. Weisz et al. (2007) found that text chat during video viewing is distracting, and their participants reported this as their main

dislike, and Weisz and Kiesler (2008) again demonstrated a distraction effect. On the other hand, when Regan and Todd (2004b) asked participants to rate how much incoming IMs interfered with the enjoyment of the TV content, average ratings were uniformly low. Just like the response of participants in our field trials was generally more positive than that of members of the focus groups, several of those people were more enthusiastic after having had hands-on experience with it than they were beforehand: “Before going into this I thought ‘What would I ever use this for?’ But it was a totally different experience actually doing it. Because I totally changed my mind: […] I would totally use it!” Supporting this observation, Abreu and Almeida (2008) found that the proportion of subjects expressing high interest in a Social TV application rose from 27% before use to 67% after having tried it. (Baillie et al. (2007), on the other hand, found that participant reactions after having used the system were consistent with advance expectations. However, in that study, initial attitudes were already overwhelmingly positive.) To us, this underlines the value of practical testing in the study of new experiences. It is hard for people to predict their reactions to a hypothetical scenario. That is not to say that prototype tests are without their own biases; in all our field trials, as well as in the ConnecTV pilot study, technical problems (including degradation of TV image quality) were among the top complaints, and appear to have affected participants’ overall reactions negatively. People seem to respond more positively to social television when they try it than when it is only explained to them, and those who like it are a lot more enthusiastic. Some of the concerns people have when the concept is presented are not as serious as they imagine.

Effects on Privacy As in the various user panels, aspects of privacy were also an important issue in our field 689

Getting to Know Social Television

studies. Although some participants expressed worry about government surveillance (“Can you imagine, like, back in the fifties, if they were Big Brother watching what we were watching? I would hope the government now doesn’t want to come and… I mean, we don’t have anything on our TV that’s so…”), spammers, hackers, the possibility of strangers stalking them through the system, and the safety implications for their children, the gravest privacy concerns were in relation to their friends and family, both those on the system and those with whom they shared the home. In practice the concerns were somewhat different from those anticipated by the focus group participants. The live television presence, which participants without direct experience worried about, did not prove to be a problem, perhaps because participants were able to log off if they wished to watch something in private. However, many felt that the viewing history feature, which logs all the TV programs watched publicly by a buddy, was too intrusive: “It’s not really personal information, but it’s not information required for anyone else to know. […] I don’t think people would feel comfortable knowing what other people have watched.” When the information was presented in summarized and aggregated form in STV3, participants had fewer objections. STV3 also exposed another problem: the confidentiality of—and the confidence participants could have in—communications via the system. In the trials, there were numerous instances of participants being impersonated by other members of their households when using text chat. In some cases they were quickly found out (“it was the way [her husband] said ‘Hey dude,’ and my sister doesn’t talk to me that way”), while in other instances the other person never realized the deception. Once people recognized this potential, reactions were strong: “Oh wow, that’s scary... Heavens no! I’d be, like, angry if I knew that that happened. […] That’s just, like, a violation of just, like, normal goodness!” “If she just pries a little bit, […] [my girlfriend] might find out something she doesn’t necessarily need to know.” 690

Everyone did not share this reaction. One participant was generally unconcerned about divulging information to the wrong person, because he felt that the conversations generally centered on topics that were not particularly sensitive: “What I was saying, even if it wasn’t the person I [thought I] was talking to, I didn’t feel like it was going to hurt anything. It wasn’t anything that was sensitive information… so I wasn’t too concerned about that.” “I wasn’t giving out my social security number.” Nevertheless, the overall trend is clear. When it comes to Social TV the primary threat to privacy is not Big Brother. It is people’s actual big and little brothers, as well as their sisters, significant others, and other close kin.

Effectiveness and Appeal: Discussion Evaluations of social television concepts and systems turn again and again to questions of sociability. The ability of the technology to provide a social experience is well established. When users talk about the benefits of having social television, nearly everything they say is about how it makes them feel closer to the other users: “It kept us more in touch with each other.” Observations of their usage show that they take advantage of the social nature of the experience to engage in a rich set of behaviors. Despite this alignment of potential capabilities with desired outcomes, and enthusiastic reception in certain studies, reactions to Social TV have not been uniformly positive. While some participant reservations could be caused by unfamiliarity with the concept (in focus groups, participatory design sessions and user panels) or technical problems (in prototype tests), two misgivings are not so easily dismissed: People worry about their privacy, although precisely what worries them is refined after experience with the system; and they are unhappy about disruptions that distract them from the TV viewing. These are problems that need to be addressed in the design of Social TV systems.

Getting to Know Social Television

A final cause of dissatisfaction with some social television systems is that they are not social enough. Prototypes such as STV2 and ConnecTV, which do not allow freeform communication between users, cause much frustration. This frustration is satisfied in systems like STV3, which offers full text and voice chat support. It is therefore necessary to look at which features do satisfy people’s social needs around television, and how they compare to each other.

Communication Modality Alternatives The social television systems in the literature differ in the capabilities they offer for sharing a viewing experience. As pointed out above, merely offering presence awareness, or limited communication in the form of emoticons or pre-defined text messages to choose from, is not a satisfactory solution for users (Metcalf et al., 2009; Tullio et al., 2008; Boertjes et al., 2008; Baillie et al., 2007; However, see Schatz, Baillie, Fröhlich, & Egger, 2008). There is a need for more expressive communication. In existing systems, solutions to this have mainly taken the form of text chat (e.g. CollaboraTV) (Harrison & Amento, 2007), audio links (e.g. AmigoTV) (Coppens, Trappeniers, & Godon, 2004; Coppens, Vanparijs, & Handekyen, 2005), and occasionally video links (e.g. Reflexion) (Agamanolis, 2008), generally according to the designers’ beliefs as to what will provide the most satisfying experience for users. A number of studies have sought to compare these different modalities.

Text vs. Voice Geerts (2006) looked at voice chat and text chat in two separate systems, AmigoTV and Windows Media Center with Window Messenger. After using both systems in the lab, nine out of seventeen participants preferred the voice chat of AmigoTV, five preferred the text chat of Windows Messenger,

and three had no preference. While voice was the more popular choice and was considered more natural, younger users and those with experience with IM text chatting tended to prefer text. In a 144-person between-subjects lab experiment, Weisz and Kiesler (2008) compared communicating using text chat, voice chat, and both while watching streaming videos. Their results show that all options were enjoyable, and do not point to any of them as clearly preferable. Participants tended to prefer the option they personally experienced to other alternatives presented. Overall, text chat did slightly better both on enjoyment ratings and preference rankings, but on the other hand, users who had both voice and text available used voice almost three times as much. On the whole, the authors interpret the findings as support for adding voice. In our focus group, participants were uniform in their opinion that communication should be by voice, and that talking should be made as natural as possible. They rejected out of hand the idea of texting for communication. Participants in the field tests of the voice-only STV1 system agreed, while those in trials of STV2, which had no freeform communication, were split on which they would prefer. Some talked about the benefits of voice: “A microphone or something to turn on would be cool. I wouldn’t take the time to be typing in stuff back and forth; I’d pick up the phone and call them.” Others strongly preferred text: “There’s no way I want somebody talking to me during a TV show. Let the damn letters pop up. If I want to ignore them I’ll close the window. I would never want someone talking to me during a TV show.”

Text and Voice in Practice In an attempt to resolve these conflicting views and inconclusive studies, we included both voice and text communication in the STV3 field trials. The results were unambiguous, based both on people’s subjective opinions and on their actual use of the

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system. In the first, one-week trial, we counted 6 separate conversations, made up of combinations of 4 voice calls and 12 text chats. Of the 6 conversations, 3 involved voice, and all involved text. In the second trial, where participants had two weeks to use the communication tools, we counted 43 conversations, 6 of which involved voice, and all of them involving text. Text was clearly the more frequent communication choice, and all the participants except one stated that they preferred it to voice. Their reasons for this choice varied. Some simply gravitated to what they were most familiar and comfortable with: “I guess that’s a technology I’m used to, because I text-message a lot, and that’s all I do at work.” Other explanations were exactly the opposite: “[At work] I’m constantly on the phone, and you get burned out and it’s just easier to text.” A number of participants felt it was easier to use and interfered less with watching TV: “Texting is what I think I’m more comfortable with, and you can pay attention more with the program, doing that. And when the voice worked, it was still fine, ’cause I could hear the program and hear the person. But it seemed like it took a little bit more of your attention or focus to make sure that you’re hearing them and responding.” There was also a widespread feeling that contacting people over text was less intrusive and disruptive than trying to contact them over voice. “I don’t want to inconvenience somebody by calling them or vice versa.” Text was also seen as less intrusive to the rest of the household. “There’s no sound or anything, so it’s not disruptive,” especially at night after children’s bed time. The reluctance to use voice was closely tied to a sense of a social obligation being imposed. “I think the calling feature can be kind of tough to determine whether or not they’re up for chatting. […] Let’s say you want to [text] first instead of just popping into a call, and so they don’t have to turn you down and kind of feel bad about it or whatever.” Considering this problem of initiating

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the conversation, it is interesting to note that all but one of the voice conversations started out with a text chat first. As one participant put it: “Texting sets up the conversation, ‘Hey, I’ll give you a call’ or whatever… then I’ll know to call.” Text may have been seen as less intrusive than voice because of the semi-asynchronous flow of a text conversation. While the back-and-forth in voice sessions was essentially instantaneous, text chats often included long pauses—many minutes, sometimes even hours—between a statement and the response. Participants felt that this offered more control over the rhythm of the conversation. “You don’t feel like you have to be waiting on the other person’s response.” “I think you’re more in a control in a conversation where you can text, where[as] when you talk, you’re all in, you’re in that conversation, and you can’t just at the end of a sentence… you can’t just walk away and then get back to them ten minutes later, like in a text.” “So the same reason why you would send a friend a text, because they can answer it when they get to it, is the same reason you would text somebody on the TV. When you’re on a voice call with them, you can’t do anything else, you can’t talk to anyone else, you don’t feel like you can just get up and leave.”

Video Communication While there is now a substantial amount of data on text vs. voice, social television with video link communication has been less thoroughly studied. Agamanolis (2008) found that watching TV programs or movies together was “one of the most satisfying ways” to use the “magic mirror”-style video conferencing system Reflexion. And as already mentioned, de Ruyter et al. (2003) found that group attraction was significantly higher while watching TV with a video visualization of a remote group of users than when watching with a sketch visualization or without any visualization. In our focus groups, many participants said that they would like for social television to include

Getting to Know Social Television

a video link, so they could see each other while talking. However, the field trials indicated that adding video to the voice connection would be problematic. When our participants were relaxing in their living rooms, they were very unselfconscious, which didn’t always mean visually presentable. In the STV1 tests, all of our participants were barefoot, and most put their legs on a table or on the couch. In a more extreme example, a child walked naked into the living room during one of the sessions. “Good thing it’s not live video! [My kid] has got no bottoms on.” They joke about it—“That’s just sick! (Laughter)”—but it is clear that it would be a genuine concern to them. One participant told us that “the best part is [that] it’s not picture in picture, where you’re talking to someone and they see you.” “[People] don’t want anybody seeing you.” Mostly, having video just seemed unnecessary. “Visually you don’t see them but it did feel like there was this big sense of camaraderie. But outside of the optical sensation of [not] seeing them it did seem like they were here. It was strange.”

Communication Modality Alternatives: Discussion In none of our studies did participants actually have a chance to try a video-mediated Social TV experience, so our findings on this issue cannot be conclusive. The difference between the opinions solicited in focus groups, the results of lab experiments, and the observations made in field trials demonstrate that we should be cautious in drawing firm conclusions from limited data. In addition, our findings on voice and text chat are themselves based only on two small groups of users, and may not be widely generalizable. However, if the results hold up, it would seem that text is the preferred mode of freeform communication for social television, followed by voice, followed by video (which surely faces all the same problems as voice, and others in addition). Even this conclusion is not the full answer, however. Each communication modality can be

provided in a number of different ways. For example, text chat can be enabled with a QWERTY keyboard or using other text entry methods and input devices, and voice communication can be open-microphone/open-speaker, or using personal headsets. The effects of such design variations remain mostly unexplored. Schatz et al. (2008) have found that they have substantial impact on the user experience of Social TV, observing that the method of text entry often determined participants’ feelings about the text messaging features. The same authors also compared four ways to control voice conversations, and found significant differences in the experience of watching TV together and in the overall preference (Baillie et al., 2007). Additionally, they report that 50% of their participants preferred an open table microphone, 30% the headsets, and 20% had no preference.

The Uses of Social Television Some of the most fascinating insights about social television have come from the detailed observation and description of the behaviors of people using it. Two main approaches have been used: observations in a laboratory, and observations in system field trials. Lab studies can be run on a large scale and allow close monitoring of participants, but sessions are typically short in duration, and the lab setting and experimental conditions can affect people’s behaviors. Field trials can be much longer in duration and observe participants in their natural environment, performing natural tasks, but the ecological validity comes at a price: because they are time-consuming and difficult to set up they are typically smaller-scale, and rely more on interview data and records of actions than on high-fidelity monitoring. There are also studies with aspects of both approaches. We will first present an overview of the findings of a number of lab studies, then discuss two main themes identified in field trials: the content and style of conversations and the pattern of behaviors

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leading up to conversations. Finally, we touch on some of the ways social television can be harmful to social relationships.

Like Being There (In the Lab) Lab studies are the most common method used to observe groups using social television. The key exemplar of this line of research is PARC’s Social TV study (Oehlberg et al., 2006; Ducheneaut et al., 2008). In that study, the researchers compared the speech acts of a group of people watching TV in the same room to those of groups watching in two different rooms, connected via an audio link. Analysis showed that the nature and structure of the conversations were surprisingly similar in the two conditions. Participant interactions were tightly interwoven with the structure of the show, with conversations taking on a rhythm to minimize disruption of the programming. In our field trials of STV1, we observed many of the same things. The flow of the conversations was remarkably natural, much more like a face-toface conversation than a phone call. People would leave and come back, chime in from another room, fall silent when they were paying attention to the show, and chat with each other when nothing interesting was happening. In the bigger groups we often saw several conversations going on in parallel, and collocated groups frequently had side-conversations among themselves while still taking part in the larger, connected group. Adding support to these results, Baillie et al. (2007) found that conversations over AmigoTV audio chat had characteristics similar to face-toface conversations, and Geerts (2006) concluded that Social TV voice chat is more natural than text chat. Weisz et al. (2007) coded the content of text chats during video watching in the lab, using categories similar to ones identified by Oehlberg et al. (2006). In their data set, 55.3% of all statements were related to the content in some way, with 22.8% consisting of personal conversation. In a later lab study, Weisz and Kiesler (2008)

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found that 48.1% of the conversation concerned the video and 9.9% was personal. In a field study by Nathan et al. (2008), the numbers were 42.3% and 18.9%, respectively. When seen alongside the observational studies, these results point to social television experiences that are natural, unforced, but adapted to the rhythm of the concurrent content, which also provides the primary topic of conversation.

Just Talk Although the above studies point in the same direction, it is important to recognize that none of them observed behaviors under natural conditions. In each, researchers arranged the viewing sessions, set up the communication link ahead of time, and, in several cases, selected the content. (With one slight exception: In the Nathan et al. study the researchers selected the content and gave participants viewing assignments, but did not otherwise interfere.) Because the initiative to watch and interact did not come from the participants, the way they do these things may be different from how they would act if the decision to engage was spontaneous. In fact, when we examined these behaviors under less artificial conditions in the STV3 field trials, the results were very different. We found that personal conversation was far more common than conversation about television content. In the second group, for example, only eleven of the 43 text chats and three of the six voice chats contained any references to the current TV content. As one participant put it, “A lot of the text chats I had were more just general, ‘Hey. How’s your day? I haven’t talked to you in a while. What are you up to?’ It wasn’t necessarily focused actually on the program. Which I thought was interesting, myself, ’cause I was thinking at the start that that’s what we’re gonna be focused on, we’ll be talking about the programs.” The following excerpt from the beginning of a voice conversation serves as an example:

Getting to Know Social Television

C3: C1: C3: C1: C3:

[C3]. Hello. What’s up? [C1], what’s up buddy? How are you? You sound funny. Ha-ha, I’m just exhausted dude, Monday just eats me up. C1: It does, huh? It’s the first day of the week. You should be at your strongest. C3: Na, I think it’s just from really enjoying the weekend and come Sunday at night you’re finally feeling decompressed. You know? And then it’s like you go back to the grind. In cases where conversations did touch on the concurrent programming, the comments were in passing. The conversation structure did not seem to be tightly interwoven with the structure of the programs. “We touched on watching it, but [our conversations] were separate from what we were watching.” (Figure 10) In fact, conversations were on a wide variety of topics. Participants reported that the topics and tone of their conversations bore resemblance to their face-to-face conversations. Participants often engaged in small talk and banter, as in this text chat:

Figure 10. C1 in a text chat with C3, commenting in passing on the team’s green St. Patrick’s Day uniforms.

C5: does [C1] always text about food? C3: he must..i heard he sleeps with a bag of doritos under his pillow C3: if [C1] were president, free zingers 4 everyone C5: for sure C5: time to take the dogto dumpout C3: yeah, you have a good night bro C3: sweet dreams C3: sweet dreams on chocolate creams However, participants also used the system to engage in more serious conversation. In one text chat, one participant inquires about how the other is doing after a recent move: C2: C5: C5: C2:

How are you enjoying the burbs? we love it nice and quiet It is a change from the city. Like the quiet too. C2: Only thing is a good night out is going to Bennigans.haha In another example from a text chat, participants used STV3 to provide live context updates about themselves and their families, and to set up face-to-face meetings: A2: A2: A1: A1: A2: A2: A1: A2: A2: A1:

i’m back whats [your son] doing? being a pain in my butt. I am going o to put him to sleep soon but hes so cute sounds good yea he is i miss him we should get together really soon he gets his looks from his dad...

Just like the content of the STV3 conversations was largely independent of the TV programming, participants did not fit the rhythm of their speech in the audio conversations to the TV content.

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Unlike the long silences between statements that we observed in the STV1 trials whenever participants were paying attention to the program, here conversation was constant, with the longest pauses between utterances less than 5 seconds in length (except at times when users had technical difficulties with the system), and most no longer than a second or two (Figure 11). As a result, each audio session generally corresponded to a single, unbroken conversation. The data shows no indication of participants ever pausing to listen to the TV, and they generally kept the program volume low (but not off) during their conversations. Overall, despite the coupling of communication with television and the fact that participants could only communicate with others who were also watching television, television content was generally not the main focus of conversation. Participants in the STV3 trials did enjoy the opportunity to communicate through the television. However, the voice and text chat features were not primarily used to create a social television experience of “watching together,” but rather as another communication medium that had little to do with the television content.

Usage Patterns As touched on above, one explanation for the different patterns of use found with STV3 compared to previous studies is that the social dynamics of communication are to a large extent determined by when and how conversations are initiated, as

well as how they are terminated. If social television designers wish to encourage different types of social experiences, they need to understand the steps that lead up to the actual conversations. We investigated this issue in our studies of STV2 and STV3. STV2 and STV3 both incorporated an ambient display, intended to help participants establish contact with each other. We saw evidence that the ambient information did help to engage them in the social television experience. Many of our participants told us of occasions when they turned on the TV because the orb indicated that others were watching. For example, one participant told us “as soon as I come into the house or I wake up or come into the room, that’s the first thing. It draws my attention, and the first thing I do is turn on the TV.” Another said that the orb “has made me a little bit more aware, makes me want to, when it does change colors, to see which of my buddies are on.” Once their TV was on and participants could see not only who was watching but what was being watched, their curiosity was stimulated further: they would often take a peek at the shows their buddies were watching. Said one participant, “If they’re watching it, then maybe it’s a good episode or something.” Some participants used what their friends were watching as a sort of menu of program options to choose from. No one needed to suggest a show; they would simply be curious and join them. One participant described that she would turn on the TV “just to see who’s on,

Figure 11. The waveform of the first five minutes of a typical audio session. Participants are talking continuously, and pauses are short ( 3.5 meters

Addressing a crowd

of use, that is, whether users sit or stand. This fact also influences the height of a table. Hall (1966) found four different interpersonal distances in the social interactions which are listed in Table 1. It is a myth that tables have to be as large as possible. A lot of current interactive tables suffer from the small size (where the distance varies between intimate and personal distance). One reason is the limitation of the display. Another limitation comes from the tracking constraints (e.g., limitation of the ultrasonic tracking device, etc.). It is also important that people still should have the possibility to use the interactive table as a normal table—thus, there should be enough space for additional objects which can be put on the table (e.g., coffee mug) and there should be also the possibility to use it as a traditional table.

projected in a collaborative tabletop setup. See section (d) of Figure 3:

Transition Between Personal and Group Work

The interesting question is how to intuitively and efficiently transfer data between these different spaces. The hyperdragging metaphor, proposed by Rekimoto and Saitho (1999), seems to be a good way for moving data from one device to the other (Figure 4). Users can click on a note on their laptop computer desktop and drag it to the augmented tabletop surface. Once the mouse reaches the edge of the physical display, the note appears on the table connected by a virtual line to the centre of the desktop. Dragging with the mouse continues to move the note across the tabletop. The hyperdragging metaphor can have a problem with users losing their mouse cursor among several cursors on the table. To address this problem, we propose

The table is an ideal interface for sharing data as everybody can easily access to objects placed on the table. Around the table, it is essential that participants have access to their personal workspaces; in addition they still need easy access to the group data and shared workspace. Figure 3 depicts this arrangement on a traditional table. Territories and the users’ physical reach are also important features that have to be designed with care. Scott et al. (2004) investigate the phenomena of different territories in a tabletop setup. Using their approach, we can three identify different working areas (spaces/territories) that have to be

•

•

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Private Space: The users’ private territory, which could also be a private hardware device (e.g., laptop screen, personal printout) and/or the area on the table around each participant, where other users cannot see the private information of the others. Design Space: The shared table surface, visible only to those sitting around the table, which is mainly used during the brainstorming process and represents the chaotic workspace. Presentation Space: Either the table or an interactive wall can be used for presenting final generated results during a meeting.

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Figure 3. A face-to-face communication setup (a) with different data views, (b) different storage attitudes, (c) and “interacting”-territories. We identified three different spaces, the private workspace, the design space, and the presentation space (d) Group View Personal View

(a)

(b) presentation space

Digital Whiteboard

design space Interactive Table

Group Storage

Personal Storage

private space

(c)

Presentation Wall

(d)

Figure 4. The hyper-dragging metaphor; the user can drag the data from the personal device to the augmented surface

a visual extension cursor, a radar-mouse-cursor that shows inside the private space the position of the actual cursor on the table. This appears as a line on the private screen space that connects with the projected virtual mouse line on the display space. A closer description can be found in Haller, Billinghurst, Leithinger, Leitner, and Seifried (2005).

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Appropriate Arrangements of Users Usually, people are sitting or standing around the table, having different “distance zones” which are dependent on the relationship between the participants. Scott et al. (2003) present different constraints and investigate the phenomenon of territories. These spaces have to be enlarged if people put a lot of physical objects on the table.

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Figure 5 depicts a scenario with five participants, where one of the users leads the session and presents the results using a huge paper sheet. Not surprisingly, only the leader gets the best view to the data. All the other participants get a different perspective to the content which can become confusing—especially if the content is seen up-side-down (Morris, Paepcke, Winograd, & Stamberger, 2006). The four images of Figure 5 are depicting the views seen by the four collaborators—notice that especially for people who are sitting too far away from the content (as participants three and four), the distorted view can become too much and important messages can get lost. However, the table has to be large enough so that participants can arrange comfortably. They should not have to sit too close together, have an adequate distance and enough space for additional large sketches and printouts that should still be placed on the surface. An appropriate arrangement of users also requires a flexible re-arrangement of the projected

content. This means that the rendering engine should have the possibility to rotate the content in any angle, so that each of the participants can have an optimal view to it. The DiamondSpin is a rendering engine, developed by the Shen, Vernier, Forlines, & Ringel (2004). The Java based framework offers a variety of features to rotate traditional Windows-based applications. Moreover, it allows a lot of special features that support a collaborative and a multimouse interaction. Other rendering tabletop engines (e.g., the University of Calgary Tabletop Framework3) are mainly developed on top of OpenGL and/or DirectX, where the Windows content is mainly grabbed and visualized as a texture. The collaboration and multimouse functionality, which is not supported by the operating system, usually have to be emulated (Tse, 2004).

Support for Interpersonal Interaction At no point should the technology interrupt the natural flow of a conversation (Inkpen, 1997).

Figure 5. Different views; not every participant can get an optimal view to the content

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Instead, the interface should support people during the discussion. Not surprisingly, using laptops and other input devices can reduce the conversational awareness and interfere with the exchange of communication cues. Although many researchers try to integrate mobile devices in a tabletop environment (Rekimoto & Saitoh, 1999), we noticed that the usage of the digital pen, with the possibility to make annotations directly onto real paper, improved the discussion a lot.

Fluid Transitions Between Activities People should not have to actively switch between different tasks. We also noticed this phenomenon between different devices, which were used simultaneously in one of our testing scenarios. In most cases, people felt confused in using more than one device at a time. Once multiple control elements are placed on the table, it becomes especially difficult to understand which device is to be used for which task. Therefore, the benefits of each tool have to be considered carefully and participants should always know how to use the different tools.

Transition Between Collaboration and External Work Although the main research focuses is on how to find adequate interaction metaphors on the tabletop setup, it is important that data, created in the interface, can be accessible in other environments without additional special software.

Support of Physical objects People want to place objects and items on the table surface during the collaboration. Due to the tracking restrictions a lot of interactive tabletop systems do not allow physical objects to be placed on the table surface. In our setup, people interact

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with real objects, such as printouts. In addition, participants should also be able to include taskspecific devices (e.g., personal laptops, tablet PCs) and physical objects (e.g., paper, notebooks). However, people often tend to put physical objects on the table. Using pressure-sensitive surfaces, physical objects result in unwanted touches.

Shared Access to both Physical and Digital objects There is no doubt that a table is an ideal interface for sharing data. Therefore, the data access has to be easy-to-use and fast, and there should be a seamless integration of both the real and virtual content.

Support of Simultaneous User Interaction At no point should the system create an atmosphere where only part of the group can get control of the discussion. In our first experiments, we noticed that each participant has to have the same control elements. If the system supports special control devices (e.g., a wireless control device), it should be clear which of the participants can use it and how they can use it. Therefore, all participants should be able to interact simultaneously.

technIcal IMpleMentatIon There are two technical aspects that have to be considered during the design and implementation phase of an interactive table: • •

The display, which should be large, bright, and high-resolution The tracking system, which should enable users to interact with the table in a natural and intuitive way

Interactive Tables

Display There are several ways to display digital content on the table. The most popular system is a projector setup, mounted on the ceiling. Today’s standard projectors’ resolution is 1024x768 pixels, which obviously limits the size of the table. The image size can be increased by mounting the projector a bit higher, but this does not automatically increase the display resolution. Multiple-projector systems are a logical consequence to increase the resolution. Some graphics cards (e.g., Quadvision’ Xentera GT8 PCI) can drive up to eight displays. Two or more graphics cards can be connected to a single PC. A cost-effective solution is to upgrade the system by connecting the video output with a Matrox DualHead2Go box. The output signal simply gets split and can result in a dual monitor upgrade with few performance penalties. It is still a challenging task to calibrate the individual projectors. A detailed background and a good overview of using multiple projectors simultaneously are given in Bimber and Raskar (2005). Although plasma displays would be an attractive alternative, they are usually very cost-prohibitive and limited in size (Streitz et al., 2003). Another problem is the sensible surface which is at high risk of getting scratched.

Tracking The DigitalDesk was one of the first tabletop displays that combined augmented interaction with physical documents using computer vision technology (Wellner, 1992). In contrast, the DiamondTouch system from the Mitsubishi Electric Research Lab (MERL) is based on measuring the capacitance of users. Up to four users can sit on special chairs around the DiamondTouch table interface (Dietz & Leigh, 2003). Signals, which are transmitted through antennas, embedded in the table, are then capacitively coupled through the users and chairs to the receivers, mounted on the chair. A similar setup is presented by Rekimoto

(2002) with the SmartSkin project, where he uses a mesh-shaped sensor grid to determine the hand position. Both systems are highly robust and accurate. Unfortunately, both systems are expensive to manufacture. The InteracTable, a single-user system, allows an interaction using a stylus. In contrast to the related research, this system is based on a plasma display (Streitz et al., 2003). The DViT4 (Digital Vision Touch) technology uses smart cameras mounted in each of the four corners of the table to track the user input (Morrison, 2005). Thus, the lens of each camera has a 90-degree field of view. The current version allows two simultaneous touches. Unfortunately, people cannot place any physical objects (e.g., a coffee mug) on the surface without achieving un-wanted touches. While DViT is mainly used for the SmartBoards, NextWindow,5 a company from New Zealand has developed a similar system. This setup is mainly designed for touch-enabling existing LCD and/ or plasma displays. The MIMIO6 and eBeam7 ultrasonic tracking devices, where participants use special styli, are a good and cheap alternative tracking surface. However, they are limited in the range and a lot of objects on the table can interfere the tracking. The LumiSight table captures the objects on the table using cameras (Kakehi, Iida, Naemura, Shirai, Matsushita, & Ohguro, 2005). Using a transparent surface, both the cameras and the projectors are mounted inside the table. The advantage is that no additional hardware has to be placed on the ceiling. One of the first larger tabletop setups was been presented by Ishii et al. (2002). In their installation, they implemented a setup for engineers discussing urban planning. The system supports multilayering of 2d sketches, drawings, and maps in combination with 3d physical (tangible) objects and is primarily designed for group sizes up to 10 people. The setup consists of two projectors hanging from the ceiling. Two cameras (also mounted above the setup) capture all the users’ activities.

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shared desIgn space Shared Design Space (Haller et al., 2006) is a collaborative tabletop environment that is mainly designed for brainstorming based on the design requirements mentioned in the previous sections. The main goal of the Shared Design Space project was to develop a large interaction surface which should be inexpensive to manufacture, scalable, and robust (Figure 6).

hardware Setup The current hardware setup consists of four projectors (with a high-resolution of 2048 x 1536 pixels) mounted above the interactive table (Figure 7a). The tracking is realized by using a large Anoto pattern8 and digital pens; see section (b) of Figure 7. Anoto-based pens are ballpoint-pens with an embedded infrared (IR) camera that tracks the pen movements simultaneously. The pen has to be used on a specially printed paper with a pattern of tiny dots. Each paper sheet is unique. The pattern is protected by a transparent Plexiglas cover; see section (c) of Figure 7. Once the user touches the table with the pen, the camera

tracks the underlying Anoto paper. Instead of using a normal pen tip, we used a stylus tip that does not leave a mark on the Plexiglas. Both the surrounding light and the light of the projectors do not interfere with the pen tracking, because the camera tracks the pattern with its IR camera. We also noticed that the Plexiglas cover does not diffract the Anoto pattern. From the pen, we receive the ID of the pen, the ID of each paper sheet, and the position of the pen tip on the page. In our setup, we used two A0 (118,0cm x 84,1cm) sized paper sheets. The data can be collected in real-time and sent to the PC via Bluetooth. Currently, Anoto pens with Bluetooth are available from Nokia (SU-1B), Logitech (io-2), and Maxell (PenIT). All of these pens are pressure sensitive which allows for additional functionalities (i.e., better control in a sketching/drawing application). Our setup can identify simultaneous touches by using multiple styli. Whenever the pen touches the table’s surface, the system identifies the ID of the pen and the action of the users they want to perform. Theoretically, there is no limit to how many people interact simultaneously. We have tested our setup with twelve participants interacting simultaneously. Surprisingly, people never

Figure 6. The shared design space application: Users are interacting with digital pens on a large tabletop surface

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Figure 7. The shared design space setup consists of an interactive table and an interactive wall (a). 36 tiny dots are arranged on a square area of 1.5 x 1.5 mm2 (b). The Anoto pattern, placed under the Plexiglas, allows accurate tracking of digital data and the projected content does not interfere with the tracking of the digital pen (c)

(a)

(b)

mentioned problems with occlusion and shadows after testing the setup. We recognized that the users’ focus is always on the tip of the pen while working with the interactive table and the shadows never occur on the relevant area. Nevertheless, we have also started exploring a rear-projection setup using a special semi-transparent projection foil in combination with the Anoto pattern. Although the Anoto tracking performs well on transparent foil, we have not been successful yet in printing the high-resolution pattern on the special rearprojection foil.

(c)

In our setup, we use the Anoto technology in different ways (Figure 8). In the first scenario, we combine real printouts with digital augmented content (similar to Mackay, Velay, Carter, Ma, & Pagani, 1993). In a second setup, we only work with digital paper and digital ink. Participants can interact with tangible (graspable) interfaces (e.g., with a real color box). In each of the color tiles of the box, we used again the Anoto tracking technology. Thus, the pen does not track directly the color, but the underlying pattern. For all these different patterns, we implemented a calibration

Figure 8. The current version of shared design space allows the interaction with real paper and real ink in combination with augmented content (a) or the interaction with digital paper and digital ink (b). Participants can annotate the real paper. The digital pen recognizes the corresponding color by the Anoto pattern, printed under the color label (c).

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(b)

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Figure 9. The state transition diagram of the pick and drop paradigm

DROP

PICK Drag

Pen Down

Move

Move

Pen Up

Move to another workspace

Drag

Pen Down

Pen Up

tool, which allows a fast and robust calibration and registration of the pattern. After choosing a color, participants can draw with the digital ink or combine it with virtual content (e.g., images, videos, or 3d geometries). We used the Pick-andDrop metaphor, proposed by Rekimoto (1997), instead of using the Drag-and-Drop paradigm. Thus, users can pick-up digital data from the table and drop-it on the private workspace by using the digital pen. Figure 9 depicts a modified state transition diagram based on the idea of Rekimoto (1997). Once the user taps a digital object on the interactive table, our system automatically binds it (virtually) to the pen. Users can either drag it or lift the pen and move the pen to their own workspace. Once the users tap again to the private workspace, they can either drag the content again or put it to the desired position.

Implementation Figure 10 depicts the implementation concept of the Shared Design Space. As mentioned, a virtually infinite number of digital pens can be linked together without any system-inherent limitations. The digital pen server is implemented in C# using the digital pen SDKs from Maxell and the Tablet-PC API for gesture recognition. Whenever

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the users touch the table surface, the digital pen sends its data via Bluetooth to the digital pen sever. Thereafter, the resulted data are sent to the tabletop application, which maps the data to the local table coordinate system and which interprets the different pen inputs. Alternatively, we also connected an ultrasonic MIMIO tracking device to the gesture recognition component for testing the flexibility of our system. Again, the strokes are then directly interpreted and sent to the tabletop application. The digital pen server always sends the raw data (in the case of the Anoto paper, it sends the coordinates relative to the upper left corner of the paper). The tabletop application itself stores the digital content in a “scenegraph” and transforms the raw positions of the digital pen to the image space. Whenever, the user touches a digital content, it will be transformed accordingly depending where the user is interacting. Actually, we support simple transformations (translations by pointing to the middle of a digital content and by moving the digital pen, rotations and scaling by clicking to the anchors (handles), which are placed on the lower left of each widget). Thus, for each graphical object, we simply store its matrix which will be modified, whenever the user manipulates it with the digital pen. There are several ways that participants can interact with the table. They can use the private space projected in front of their place or use a per-

Interactive Tables

Figure 10. Shared design space implementation

sonal device (e.g., tablet PC), which is wirelessly connected to the table application. The personal workspace is rendered on the same machine as the rest of the design space. We simply implemented a huge rendering window, which is then split into the four different video outputs. Participants can create new content on their own device and move it by hyper-dragging to the design table. The client application is also implemented in C++ in combination with OpenGL. The communication is realized by TCP/IP and the “dragged” objects are moved to the server’s shared folder. The rendering engine of the tabletop application is realized using C++ and OpenGL. Similar approaches are implemented on top of the Mac OS X using Quartz and on Linux using the XRender Extension render engine. But it is still an open question if the system will support a multi-user input.

We used the ClanLib9 library for rendering the applications’ graphical user interface. The advantage of the ClanLib library is that the system is based on top of OpenGL and can therefore be combined easily with the basic rendering system of the 3d graphics library. In addition, we used the component model of the ClanLib library for the inter-component communication and the network extension for the communication between the clients and the server (e.g., communication with the gesture recognition engine). The real paper, as depicted in section (a) of Figure 8, is tracked by using ARTag markers, placed on top of each piece of paper (Fiala, 2005). We use small markers (2cm x 2cm) in combination with a standard WATEC WAT-502B camera with manual focus, mounted on top of the table. There is some noise in the ARTag tracker output which means the virtual, augmented content is not 100%

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projected on the exact position and orientation of the paper. This was one of the main reasons, why we changed our primary focus to the digital paper in combination with digital ink. The interactive wall, which consists of a rear-projection screen, provides a simple gesture tracking environment and allows for a multitouch interface for manipulating digital content, which can be moved from the interactive table to the wall and vice versa. The tracking on the interactive wall is mainly based on the JazzUp10 library and on the OpenCV computer vision library.

Task We designed a task that required a lot of collaboration, participants were provided with 60 images which included hidden shapes that the participants had to find (section (a) of Figure 11). There were three copies of each image on the table. Solving the task could be done by selecting a separate image for each participant or by using references. In this case, all users had the same view of the image and all annotations were seen by all participants simultaneously. At the beginning of each session, the digital images were randomly placed in the middle of the table (Figure 11b). Some of them were overlapping and they needed to be examined in order to reveal the hidden shapes. The goal was to work together as a group to identify as many of the shapes as possible. There was a time limit set to 5 minutes and the participants were encouraged to make the most of the time and to discuss with their team members while finding the solutions. In order to test, whether the Shared Design Space setup has an impact on the overall workflow and the user performance, we repeated the test by using paper printouts of the hidden images and using real ink for annotations; section c of Figure 11. In contrast to the Shared Space Design setup, there were no fixed workspaces, and it was not possible to scale the images or create references. Task assignment, table, and content were the same under both conditions.

user study We conducted a user study to explore how well the Shared Design Space can solve some of the design challenges described before.

Apparatus The participants performed the experiment while standing around the large table (183cm x 122cm), with a high-resolution (2048 x 1536 pixels) tabletop display. Three private workspaces were fixed in placed on the longer edges of the table. Two people were standing closer together—the third person was on the opposite edge of the table. A digital video camera, mounted on top of the table, was used to record the participant’s interactions and movements.

Figure 11. Participants had to find all hidden shapes (a) of the images. In the first scenario, participants were using the interactive table (b). In the second scenario, we repeated the test using real images (c)

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(b)

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participants Results of the primary user study were gathered from 48 participants who used the Shared Design Space setup. The participants were recruited during a local festival. Of these, there were 28 males and 18 females between the ages of 11 and 54 years. The mean age of the participants was 28.8 (SD = 9.29) years. Two participants did not report age and gender. We formed 16 groups consisting of three members. The comparison group that worked under the paper condition consisted of 12 participants recruited from the local university. Of these were nine males and three females between ages of 23 and 39 years ( x = 27.6, SD = 4.48). We formed 4 groups of three members each. In both cases most of the participants reported that they had good computer skills, using a computer more than 15 hours a week.

Procedure Before starting with the usability study, the participants were able to get familiar with the system. We demonstrated to them how to interact with the setup for about 5 minutes and allowed them to practice with the system themselves. Not all participants did the warm-up phase. After each session, a survey was presented to the participants with a number of statements and they were asked how much they agreed or disagreed with the statement. The participants were also asked for general comments and feedback about the experience. In addition we tracked the movements of the participants in both setups, by using a camera mounted above the table.

results Although the task had a fixed time limit of 5 minutes, participants had the possibility to “play” with the system before completing the task. Around 40% of the participants spent between 6 and 10

minutes around the table (including the warm-up phase and the test). Eighteen point three percent took between 11 and 20 minutes, only 26.7% played with the table for just 5 minutes. Fifteen percent of the users spent more than 20 minutes around the interactive table. The questionnaires were made up of 20 items, using a 5-point Likertscale (1 = totally disagree, 5 = totally agree). The data were analyzed by using SPSS and the main effects were tested with a pair-wise t-test to determine if there was a statistical significance between the two conditions. The questionnaires were grouped into the categories of learnability, interaction, interaction devices, collaboration and awareness, and space.

learnability Two items of the questionnaires addressed the learnability and ease-of-use of the system: • •

Q1: It was always clear to me how to use the system. Q2: It was very easy to learn how to use the system.

Several participants were concerned that it was not immediately clear how to use the interactive table setup ( x = 3.75, SD = 1.02). However, after a short introduction, people also mentioned that the application itself was easy-to-learn ( x = 4.67, SD = 0.63) and they were more comfortable with the different interaction metaphors (e.g., the pick-and-drop). Thus, the way to use the pen was obvious and intuitive for the participants.

Interaction At the beginning of each session, a lot of people found it difficult to move data from the table using the pick-and-drop metaphor without support. However, they immediately understood it once we demonstrated it in the warm-up phase. Three questions addressed the interaction with data: 755

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• • •

Q3: It was easy to select/grab an image from the table. Q4: It was easy to move an image on the table. Q5: It was easy to rotate an image on the table.

Overall, participants did not find it very difficult to select data sets, as indicated by the Likert-scale responses to the statement “I found it easy to select/grab an image from the table” ( x = 4.44, SD = 0.65). People also did not have a lot of problems moving data ( x = 4.56, SD = 0.68), nor did they find it very difficult to rotate data ( x = 4.45, SD = 0.90). Surprisingly, the pick-and-drop metaphor was not as intuitive as expected. The first approach of most participants was to move data into their private workspace using dragand-drop. As images can be moved by dragging with the pen, they tried the same technique to place them in their private workspace. Explaining the difference between moving data inside a workspace and changing the workspace location from public to private or vice versa clarified the two ways of handling digital content. While tipping once with the pen to the digital images, a lot of persons were confused where the content has gone and images got selected by fault. Dropping them and selecting a new image confused users. They tried to select a new image while they had the one still selected. This resulted in dropping the old image at the next click instead of picking up the new one. But not all of participants had this problem. Especially those who spent more time in the warm-up phase reported encountering fewer problems.

Interaction devices In the category interaction devices, we mainly focused on the tangible interfaces and the pen interaction asking the following four questions:

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• • • •

Q6: The palettes were very intuitive. Q7: The pen tracking was really accurate and robust. Q8: The pen vibrated really often. Q9: I would rather prefer interacting with the fingers instead of using the pen.

Not all users found the palettes very intuitive (x = 3.94, SD = 1.00). Many participants remarked that the icons on the palettes were not always clear. The color boxes however, as tangible interfaces were evaluated positively. Another problem was that real objects were hard to be detected on the table once they were illuminated by the projector’s image (Figure 13). So, for instance, it is not only hard to detect the right color, but also to find the color box itself, once many images are projected onto the table. Most of the participants had the feeling that the pen tracking was really accurate and robust ( x = 4.13, SD = 0.89). This was also confirmed while asking if the participants noticed the vibration of the pen which occurred whenever the pen camera could not track the tiny dots of the paper ( x = 2.58, SD = 1.25). The pen mostly itself vibrated when people held the pen too flat, which caused bad tracking results. When asked “Would you rather prefer interacting with your fingers instead of using the pen,” the average of all responses was 2.2 (SD = 1.37). The relatively high standard deviation indicates that in this question not all participants had the same opinion: interviewing participants, a lot of users thought that the pen should be mainly used for writing and for typing (e.g., onto a virtual keyboard). In contrast, some activities are more intuitive using the fingers (e.g., rotating content).

space Four questions focused on the space and workspace:

Interactive Tables

• • • •

Q10: I could easily see all images and all details of the images on the table. Q11: I mostly focused on my private workspace. Q12: I mostly focused on the common workspace. Q13: The size of the interactive table was too small.

Table 2 summarizes the average results on space and workspaces using the paper and interactive table condition. During the tests, we recognized that most of the participants mainly focused on their private workspace working on the interactive table. The public workspace was only used by one group. They discussed the selected image placed in the public workspace and used different scale levels and rotations to solve the task. We tracked the movements of the participants in both setups and measured the “level of interactivity,” by using a camera mounted on top of the setups. Figure 12 depicts the results of the movement analysis over the five minutes. The black areas represent the movements and the interactions of the participants. The red areas show the movements standing around the table. The green area is depicting the interaction area on the table. Under the paper condition, the participants were grouping to one position; see section (a) of Figure 12. The participants arranged the paper images in front of them and chose a common location to access them. Interaction across the table only

happened at the beginning the focus then shifted to the near area directly in front of them. In contrast, in the interactive table setup, the main interaction happened in front of each participant; see section (b) of Figure 12). Participants stayed at the position of their workspaces and we can distinguish three separate areas. After the experiment was completed, the participants were also briefly interviewed about their experience. The participants were more active during the discussion using the interactive table; see section (b) of Figure 12. In general, people neither changed their position on the interactive table, nor did they walk around it. Users commented that the private workspace should be larger and not have a fixed position. One of the participants noticed that the printed pictures were too small, which forced him to actually pick-up the pictures so that he could recognize the hidden shapes. This, however, brought the focus away from the shared workspace. Although we used the same table size under both conditions, we found a significant difference in the assessment of the size of the table, t(58) = 9.227, p < 0.05. While those working in the Shared Design Space agreed that the table was rather a bit too small ( x = 2.69, SD = 0.46), those using the paper setup confirmed that the table was too large ( x = 3.58, SD = 0.67). Finally, it was not immediately clear to everyone that the whole table was interactive. Some people just thought that they could interact with their private workspace.

Table 2. Average results on space and workspaces Condition

Paper

Interactive Table

Q10

3.63 (SD = 1.21)

3.47 (SD = 1.17)

Q11

3.50 (SD = 1.38)

3.98 (SD = 0.99)

Q12

2.91 (SD = 1.16)

2.68 (SD = 1.00)

Q13

3.58 (SD = 0.67)

2.96 (SD = 0.45)

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Figure 12. Participants’ movements and interactions in paper scenario (a) and using the interactive table setup (b). While the red areas mark the location of the participants, the green areas visualize the main interaction areas

(a)

(b)

Awareness and Collaboration We asked two questions about the awareness and collaboration both in the interactive table as well as the paper condition: • •

Q14: How often were you aware of what your partner was doing? Q15: Coordinating with your partner was easy.

Table 3 shows the average results for each of these questions. The t-test found no significant difference for question Q14 and a significant difference for question Q15, t(58) = 5.606, p < 0.05. Participants felt that they were more aware of what their partners were doing under the paper condition. It was interesting to see that under the paper condition the participants got really close to each other to solve the problem; see section (a)

of Figure 12). Initially, most of the participants were standing around the table and had a larger distance to each other. Once they had to solve the problem, they immediately moved away from their initial position and got closer to each other to focus on the same image. In most cases, one person became the leader of the session and the other two mostly agreed or disagreed with their comments. In contrast to the paper condition, more people got actively involved under the interactive table condition. Nevertheless, due to the longer distances and the fixed private workspaces, people were less aware of what their partner was doing. An average of four correct images was found by the participants using the interactive table setup. In contrast, the users found mostly different solutions under the paper condition (an average of only one common solution was found by all participants). Although most of them thought to have

Table 3. Average results on collaboration and awareness

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Condition

Paper

Interactive Table

Q14

3.00 (SD = 1.13)

2.92 (SD = 0.85)

Q15

3.58 (SD = 0.67)

2.96 (SD = 0.46)

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Figure 13. The colors and icons of the color box can become hardly to read once digital content is projecting on the tangible interface

a single solution, we noticed that the annotations of the printouts were mostly different. Thus, the participants thought they were talking about the same thing, but in reality they were not.

CoNClUSIoN AND FUTURE WoRK Although there are already several projects focusing on ubiquitous environments, it is still challenging to design a digital table which is seamlessly embedded in an existing environment. In this chapter, we mainly focused on design requirements we achieved from discussions with our customers. After the requirements, we presented the most important related projects highlighting both their display and tracking technologies. Finally, we described the Shared Design Space, a scalable tabletop setup, which allows for development of a very large interactive table at low cost. The interaction is realized using digital pens with embedded cameras which track a special pattern (with tiny dots) mounted

on the table surface. The installation provides a cooperative and social experience by allowing multiple face-to-face participants to interact easily around a shared workspace, while also having access to their own private information space and a public presentation space combining both virtual and real sketches. The project is different from typical screen-based collaboration because it uses advanced interface technologies to merge the personal and task space. Using the Anoto paper pattern creates a flexible, easy-to-use, and cheap interface. Very large displays can easily be built, while keeping the price low. Using no special surface allows users to sit freely around the table and to put additional physical objects onto the table surface without interfering the tracking. Moreover, the system can identify who is interacting with the interface without any additional hardware requirements, for example, capacitive measurement on the chairs (Dietz & Leigh, 2001). One drawback with the digital pens is the lack of free fingers for simple user gesture inputs (e.g., the movement of digital

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paper can be done easily and faster using the hand instead of using digital pens). Although a lot of people were not disturbed by self-occlusions and shadows which occur using a tabletop projection setup, we recognized that real objects were hard to detect on the table once they were illuminated by the projector’s image (Figure 13). One possible solution for this problem would be to mask the real objects and not project digital content on these areas. The most critical questions are the rendering engine and the framework. Using an OpenGL/ DirectX based approach always raises the question about the possible combination with desktopbased applications (e.g., the integration of Powerpoint slides, Excel sheets, or PDF documents). For each of these formats, we would have to write a separate viewer (renderer). Similarly, the lack of multi-user support is a serious problem which can not be solved easily. Another problem is an adequate user interface for the large surface. The Windows-based applications are mainly optimized for desktop setups. Therefore, applications cannot just be projected on the table without modifications. How can we create new content on the table efficiently? Which user interfaces should we use to achieve an optimal performance? How can we move the data around the table efficiently? In the future, we would like to employ our setup in a collaborative remote setup and conduct more in-depth user studies. Moreover, we will develop further applications to explore other aspects of collaborative applications.

ACKNoWlEDGMENT The authors gratefully acknowledge the entire research lab of the Upper Austria University of Applied Sciences. In particular, we would like to thank Peter Brandl, Daniel Leithinger, Jakob Leitner, Thomas Seifried, and Jürgen Zauner. This

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work is funded by the FFG FHPlus Program (No. 811407) and by Voestalpine Informationstechnologie GmbH. We would like to acknowledge Anoto and Maxell for their technical support. Finally, we would also thank all participants for joining the user study.

reFerences Bimber, O., & Raskar, R. (2005). Spatial augmented reality: Merging real and virtual worlds. A K Peters LTD. Blinn, J. (1990, November/December). The ultimate design tool. IEEE Computer Graphics and Applications, 10(6), 90-92. Buxton, W. (1992). Telepresence: Integrating shared task and person spaces. In Proceedings of Graphics Interface ‘92 (pp. 123-129). Dietz, P.H., & Leigh, D.L. (2001, November). DiamondTouch: A multi-user touch technology. In ACM Symposium on User Interface Software and Technology (UIST) (pp. 219-226). ISBN: 1-58113-438-X. Fiala, M. (2005, June 20-26). ARTag, a fiducial marker system using digital techniques. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’05) (Vol. 2) (pp. 590-596). Washington, DC: IEEE Computer Society Hall, E. (1966). Distances in man: The hidden dimension. Garden City, NY: Double Day. Haller, M., Billinghurst, M., Leithinger, D., Leitner, J., & Seifried, T. (2005, December 5-8). Coeno, enhancing face-to-face collaboration. In 15th International Conference on Artificial Reality and Telexistence (ICAT). Christchurch, New Zealand. Haller, M., Leithinger, D., Leitner, J., Seifried, T., Brandl, P., Zauner, J. et al. (2006, August). The

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shared design space. In ACM SIGGRAPH 2006, Emerging Technologies, Boston, MA. Inkpen, K. (1997). Adapting the human computer interface to support collaborative learning environments for children. Ph.D. Dissertation, Department of Computer Science, University of British Columbia. Ishii, H., Underkoffler, J., Chak, D., Piper, B., BenJoseph, E., Yeung, L. et al. (2002). Augmented urban planning workbench: Overlaying drawings, physical models and digital simulation. In IEEE and ACM International Symposium on Mixed and Augmented Reality ACM Press, Darmstadt, Germany. Kakehi, Y., Iida, M., Naemura, T., Shirai, Y., Matsushita, M., & Ohguro, T. (2005). Lumisight table: Interactive view-dependent tabletop display surrounded by multiple users. IEEE Computer Graphics and Applications, 25(1), 48-53. Liao, C., Guimbretière, F., & Hinckley, K. (2005, October 23-26). PapierCraft: A command system for interactive paper. In Proceedings of the 18th Annual ACM Symposium on User interface Software and Technology, Seattle, WA (pp. 241-244). New York, NY: ACM Press. Mackay, W.E., Velay, G., Carter, K., Ma, C., & Pagani, D. (1993, July). Augmenting reality: Adding computational dimensions to paper. Communications of the ACM, 36(7), 96-97. Morris, M.R. (2006, April). Supporting effective interaction with tabletop groupware. Ph.D. Dissertation, Stanford University Technical Report. Morris, M.R., Paepcke, A., Winograd, T., & Stamberger, J. (2006). TeamTag: Exploring centralized versus replicated controls for co-located tabletop groupware. In Proceedings of CHI. Morrison, G. (2005, July/August). A camerabased input device for large interactive displays. IEEE Computer Graphics and Applications, 25(4), 52-57.

Parker, J. K., Mandryk, R. L., & Inkpen, K. M. (2006, September). Integrating point and touch for interaction with digital tabletop displays. IEEE Computer Graphics and Applications, 26(5), 28-35. Ramsborg, G. (2002). Dynamics of seating arrangements: Identification of points of power around a conference table. Retrieved January 27, 2008, from http://www.ramsborg.com/etopic/ Nov_2002/index.html Rekimoto, J. (1997, October 14-17). Pick-and-drop: A direct manipulation technique for multiple computer environments. In Proceedings of the 10th Annual ACM Symposium on User Interface Software and Technology, Banff, Canada (pp. 31-39). Rekimoto, J. (2002). SmartSkin: An infrastructure for freehand manipulation on interactive surfaces. In CHI 2002. Rekimoto, J., & Saitoh, M. (1999). Augmented surfaces: A spatially continuous work space for hybrid computing environments. In CHI ‘99, Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Scott, S.D., Carpendale, M.S.T., & Inkpen, K.M. (2004). Territoriality in collaborative tabletop workspace. In Proceedings of CSCW 2004 (pp. 294-303). Scott, S.D., Grant, K.D., & Mandryk, R.L. (2003, September). System guidelines for co-located, collaborative work on a tabletop display. In Proceedings of ECSCW’03, European Conference Computer-Supported Cooperative Work 2003. Shen, C., Vernier, F.D., Forlines, C., & Ringel, M. (2004, April 24-29). DiamondSpin: An extensible toolkit for around-the-table interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vienna, Austria (pp. 167-174). New York, NY: ACM Press.

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Streitz, N., Prante, P., Röcker, C., van Alphen, D., Magerkurth, C., & Stenzel, R. (2003). Plewe ambient displays and mobile devices for the creation of social architectural spaces: Supporting informal communication and social awareness in organizations. In Public and situated displays: Social and interactional aspects of shared display technologies (pp. 387-409). Kluwer Publishers. Tse, E. (2004, November). The single display groupware toolkit. M.Sc. Thesis, Department of Computer Science, University of Calgary, Calgary, Alberta, Canada. Wellner, P. (1992, November 11-13). The DigitalDesk calculator: Tactile manipulation on a desktop display. In Proceedings of UIST’92, the ACM Symposium on User Interface Software and Technology, Hilton Head, SC. New York: ACM. Yeh, R., Liao, C., Klemmer, S., Guimbretière, F., Lee, B., Kakaradov, B. et al. (2006). ButterflyNet:

A mobile capture and access system for field biology research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI ‘06 (pp. 571-580). New York, NY: ACM Press.

endnotes 1 2 3

4 5 6 7 8 9 10

http://www.smarttech.com/ www.anoto.com http://grouplab.cpsc.ucalgary.ca/cookbook/ index.php?n=Toolkits.TableTopFramework http://www.smarttech.com/DViT/ http://www.nextwindow.com/ http://www.mimio.com http://www.e-beam.com http://www.anoto.com http://www.clanlib.org http://SourceForge.net/projects/JazzUp/

This work was previously published in Ubiquitous Computing: Design, Implementation, and Usability, edited by Y. Theng; H. Duh, pp. 266-287, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Section IV

Utilization and Application

This section introduces and discusses the utilization and application of ubiquitous and pervasive computing technologies. These particular selections highlight, among other topics, pervasive healthcare, the utilization of handheld computers, and m-commerce. Contributions included in this section provide coverage of the ways in which technology increasingly becomes part of our daily lives through the seamless integration of specific tools into existing processes.

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Chapter 4.1

Pervasive Healthcare: Problems and Potentials Niels Boye University of Aalborg, Denmark

abstract Pervasive healthcare is a vision for the future of healthcare. Healthcare provisions can be delivered with high quality at low cost along with higher patient-experienced quality and satisfaction as a service on top of a pervasive computing infrastructure, which can be built by integrating communicating computer-power into industrial products and fixed structures in urban and rural spaces. For pervasive healthcare, integration with on body networks sensors and actuators may also be needed. This chapter discusses the prerequisites of this vision from a point of a healthcare professional. A number of parallel advances in concepts have to take place before pervasive healthcare (PH) is matured into a general method for delivering healthcare provisions. The contemporary, most widespread model of healthcare provisions as industrial products with consumer-goods characteristics has to mature into the concepts of welfare economics. New market models have to be developed for PH to pervade society and add value to the health aspects of an individual’s life. Ethical and legal aspects must

also be further matured. Maturation of technology is needed. This includes all the components of the “pervasive loop” from sensors to the central intelligence back to the actuators. The “virtual patient/healthy human” as an operational digital representation of the “object/subject of care” also has to be developed. Pervasive healthcare (or the European Union term: ambient assisted living) is a promising field, that has potential to integrate health considerations and health promoting activities for patients and non-patients in their everyday conduct and provide added value to life quality for individuals.

IntroductIon The author of this chapter will discuss, from the point of view of a person seeing patients every day (i.e., a health professional), the prerequisites and needs for successful implementation of a computer-supported, universal healthcare delivery system. The author will also suggest possible concepts for the future maturation of technology

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Pervasive Healthcare

and services for the purpose of creating such a pervasive healthcare system. More specifically human aspects of pervasive healthcare computing are considered. The topics discussed in this chapter will be more abstract than that of simply describing developed solutions and current research approaches; and it is not a comprehensive overview of “state of the art” in pervasive computing or pervasive computing for health. Future directions will be discussed briefly at the end of the chapter. There are three parts to this book chapter: the humansocietal perspective, the clinical perspective on architecture and services, and briefly the future trends in pervasive healthcare.

Pervasive Computing Pervasive computing is at present a vision for the future of healthcare. The word pervasive itself is derived from the Latin word per vas meaning to go through. Pervasive computing can be defined as a ubiquitous, computer-based service. Pervasive computing could be used to service both individuals and society as a backdrop for providing with information. Pervasive computing architectures are achieved by integrating, networking, and enabling communication between computers and humans, humans and computers, and between computers themselves. Such interactions could exist among (nearly) every industrial product and more fixed structures in the environment such as buildings, bus shelters, and poster stands used for advertising. Pervasive computing could also include wearable computing devices. Wearable computing is a term that describes body area networks (BANs) (or PAN = personal area networks). Here, computational power interacts with a pervasive infrastructure, the user, and/or his or her physiologic functions through on-body sensors and actuators. Intelligent textiles are also a part of this interaction network. Other terms, such as ubiquitous computing or the European Union’s term for pervasive computing, ambient

intelligence, have been used more or less synonymously with pervasive computing.

Pervasive healthcare “Pervasive healthcare” (PH) refers to the “invisible, omnipresent” networked, interoperable, computational power-structure that is employed for the purpose of adding to the quality of life and health and wellness of every citizen (whether they consider themselves healthy or not). Pervasive healthcare involves individualized interaction between health services layered “on top” of a pervasive computing infrastructure. In the future, pervasive healthcare could have a public health dimension as well, providing more general, invisible social science information via surveys and context sensitive advice and information aimed at prophylaxis and the collective health of groups of individuals. In European Union terms, pervasive healthcare is equivalent to the term “ambient assisted living.” In computer science, “context sensitivity” is a term that describes a computer system as being “aware” of a number of physical circumstances (the current user, the location, the task to be worked on… etc.). In order for pervasive healthcare to become a reality a health-oriented, individual, “intellectual, health context sensitivity” needs to be developed. This health or “clinical context sensitivity” will be discussed in a later chapter in terms of the current research and work in the areas of the object of work—or the object of interest (OOI).

SECTIoN oNE: ThE huMan-socIetal perspectIve Societal Aspects of healthcare One of the main or fundamental pillars of a welfare society is its healthcare services. In welfare societies, healthcare services present as organized, industrial, institutions with a concentration of

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potentials, know-how, and technology grouped into hierarchical organizations. This mode of healthcare service organization and delivery of the provision of healthcare is in essence independent of how healthcare services are financed (i.e., public, private or by insurance). Although modified by health maintenance organizations (HMO), governments or legislation, healthcare can be considered to be a basic and necessary function in a developed society, organized around citizen welfare principles (as some educational or social services are). Thus, healthcare could be regarded as a societal structure and not just a method of production that is used to address a market, consumer, or a customer need. This is in contrast to current approaches to the provision of healthcare services. Current approaches to providing healthcare view healthcare as an industrial product that is consumable by customers (i.e., patients) and subject to ordinary market economics. The English-Indian Nobel prize winner in economics in 1998, Amartya K. Sen, offers a further discussion of welfare economics and their relations to healthcare. He clearly demonstrates that healthcare provisions lack characteristics pivotal to consumer products This dilemma between the nature of healthcare provisions and our current industrial approach to delivering healthcare may be the main obstacle to the dissemination of pervasive healthcare. Even though pervasive computing supports Sen’s view of healthcare as an omnipresent service that is provided to citizens by society in a developed welfare society or state. A discussion about the societal aspects of welfare economics and the provision of healthcare may seem to be a distant one in a chapter on the use of ubiquitous computing in healthcare. Alternatively, pervasive healthcare (and pervasive computing) has not yet matured to the degree that it can be applied as a universal right or principle as part of healthcare delivery. Both in an immature and a more mature state, pervasive computing is an

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activity that has to be financed, and is of importance to the development of sustainable business models for the delivery of healthcare. Thus, short term financing of pervasive healthcare can be difficult to obtain, although long term societal and individual gains can be expected with its use. As different stakeholders become involved and models of healthcare are used it may not be possible to apply pervasive healthcare as a universal principle to a sufficient level from a clinical point of view using current healthcare business models and in the current healthcare context. It may even be necessary to make modifications to existing technical standards such as HL7 to ensure reference models use concepts that recognize the “roles of health professionals in participating in healthcare acts” (since one can bill for acts). Apart from sustainable business models the speed with which pervasive computing has matured in healthcare has been mitigated by user experiences both technical (and clinical1). User experiences include the richness and quality of service, size of service, “pervasiveness,” the ubiquity of devices and the interaction possibilities associated with the use of pervasive healthcare.

Realistic Expectations on a Societal level Biological variability, the relative complexity of healthcare, the individual nature of the provision of healthcare combined with the level of maturity of current computer technologies has prompted health professionals and scientists in the field of pervasive healthcare to develop and use business cases with the expectation that pervasive healthcare will have a societal effect. More specifically, there will be a need to assess the “the (overall) impact factor” of PH. There will still be an ever increasing number of people that will demand traditional care, either due to the need for special technology, knowledge, and manual skills or because the patient is not able to join a PH group for some reason. On the other hand, for

Pervasive Healthcare

those individuals, that can engage in PH the added value in life quality and flexibility associated with using pervasive healthcare systems may have a significant impact on the individuals health and wellness (see the scenario later in this chapter). Pervasive healthcare will not be the savior of the healthcare system. There will likely be a large gap between the number of potential customers and actual or real customers that will benefit for pervasive healthcare. For example, the Center for Disease Control and Prevention (CDC) in Atlanta Georgia in the United States of America reported: From 1980 through to 2004, the prevalence of diagnosed diabetes increased in all age groups. In general, throughout the time period, people aged 65-74 had the highest prevalence, followed by people aged 75 or older, people aged 45-64 years, and people less than 45 years of age. In 2004, the prevalence of diagnosed diabetes among people aged 65-74 (16.7%) was approximately 12 times that of people less than 45 years of age (1.4%). In other words, this means that in a group of individuals 65 years of age and older, the frequency of diabetes is approaching 20 percent due to: (1) a real increase, and (2) another way of defining diabetes (which of course makes these new patients or “recruits” potential “customers” as well). A number of different diseases and stages of disease with different needs occur in conjunction with diabetes. Many diabetics are elderly and only a few of these individuals would likely use information and communication technologies (ICT). Therefore, the real customers for PH are few in each “market-segment” of a disease. Another factor to consider is that the majority of patients in this age group are type II diabetics. Although diabetes is their primary diagnosis, troublesome subjective symptoms that diabetic patients often note are not entirely associated with high blood glucose levels. These troublesome,

subjective symptoms arise from the health issues associated with diabetes such as cardiovascular and kidney disease as well as the diseases of other organs (these organs are not in general connected to diabetes although complications may arise as a result of living with diabetes over the long-term). Therefore, a PH solution for type II diabetes in the elderly should address a far more complex and individual range of disease manifestations in order to have an impact upon the management of disease and to be able to “do the healthcare delivery job.” There are nearly 15 million (known) diabetics in the United States and, of course, all of these individuals are potential customers for a diabetes PH setup, but all these individuals cannot be included in a business case or in a justification for a grant application.

Will Ph Enable Societal healthcare Services to Enter the Information Age? Will pervasive computing provide more information to organizations and promote the function of healthcare well into the future? Will computer based pervasive healthcare induce changes in roles, acts, possibilities and responsibilities of health professionals and patients who work in and are a part of the provision of healthcare? The implementation of pervasive computing healthcare technologies should enable new actors and new business models to emerge as well as facilitate the use of conventional approaches to the provision of healthcare and actors to be distributed in other and more flexible ways. Unfortunately, such a situation is not “just around the corner.” There is a need for an infrastructure to be built, software needs to mature, and technical standards need to be developed. Infrastructure, software and technical standards are well below what is needed and expected by healthcare services to address the complex, individualistic and mature needs of patients in the hyper-complex society (Qvortrup, 2003). The hyper-complex society is perceived as the current evolution of the information society. 767

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Expectations and Values of the Post-Modernistic Patient The human aspect of the hyper-complex society is the post-modernistic perspective of the individual, as an individual with unique values and expectations. Earlier the value for the single human was expressed more collectively through his or her membership of a society, a work-community, a religion, a family, or a tribe. The post-modernistic philosophic movement was rooted in France in the 1970s and 80s on top of the 1968-youth rebellion. It is an ideological showdown with the modern, industrial, and uniform society and that all problems have a “single solution.” It is a promotion of complexity, individuality and lifestyle. The postmodernistic perspective of individuality creates expectations and values by the consumer of health that potentially could be fulfilled by PH. In brief, post-modernistic patients are taken care of by empowerment not by passive treatment.

In Conclusion: The human-Societal perspective We are at present in a catch-22 situation. We lack the technical maturity to develop a new healthcare delivery system, and we also lack the financing needed to achieve technical maturation. This means that PH will be limited to disease, condition, and project oriented healthcare delivery and is dependent on technological drive from other markets that are dissimilar to healthcare. On the other hand, the only way to promote pervasive healthcare is to demonstrate a return for society, for patients and/or healthcare professionals (or at least two of the three). Building the necessary PH infrastructure will demand a solid “business case” for every stakeholder, which for a number of reasons discussed above, is difficult to build, especially if healthcare provisions are perceived as being industrial products.

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Healthcare delivery by computer-power will not be a universal principle until other factors are addressed, such as a lack of healthcare professionals, or when pervasive computing capabilities of sufficient maturity are built for other purposes. The total “project of pervasive healthcare” has yet to be completed, and as always for the purpose of scientific-based progress, there is a need for incremental, problem-specific, PH projectorganized development (in this phase), with knowledge sharing of results and experiences which is generalizable and can be used to address people’s needs and increase the collective wisdom of society in general. Thus, healthcare is far from entering the information age and servicing the hyper-complex society with its post-modernistic inhabitants with pervasive services, due to industrial like business models and lack of maturity of soft and hardware. However, the evolution of PH will continue through project-organized research and development.

Experimental Computer Science as Social Science As discussed previously, pervasive healthcare research and projects are dependent of a number of human and societal aspects, but there are aspects of experimental computer science in pervasive healthcare projects as well, although here experimental computer science has entered the domain of the social sciences. Therefore, there is a need to adapt the evaluation methods and scientific standards of the social sciences among other factors taking into account the biological range of human individuality. Experimental computer scientists cannot engage in “a proof of concept” in a laboratory setting alone. Instead, there is also a need for a clinical testing phase involving the proper use of qualitative and quantitative methodologies and evaluation using appropriate clinical endpoints.

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healthcare Provision Composition

Teamwork

An analytical breakdown of a healthcare provision may be of use in development of solutions that support ubiquitous delivery of health services. In the chapter by Nohr and Boye (2008), it is argued that the provision of healthcare is made up of four to five principle components (when analyzed for the purpose of computer support or complete computerization). These principle components include:

In classical medicine patients are considered “the object of care.” Patients are more so the subject of care today. Ideally, the patient is the most hardworking and valuable team member in his or her health-support-team. Pervasive healthcare will probably work best in cases where the patient (or their proxy) has the mental and cognitive abilities to participate actively in their care.

• • • • •

Knowledge Teamwork—including the patient and their family Technology utilization—including the use of computer power Manual skills Organization (could be included as the fifth component)

Not every component may be present in every single provision of healthcare, but it should be possible to evoke all of the components when appropriate.

Knowledge as Power or a Shared Resource In the industrial society “knowledge” was considered to be a source of power. Knowledge in the information society should now be considered as a universal distributed resource. This means, that power is attributed to those who have the ability to access, use, and filter knowledge. The ability to make knowledge become operational and useful is foremost important (i.e., the ability to “make it work”). For example, you can use the Internet to acquire some knowledge of brain surgery, but not everybody is able to utilize that knowledge in a beneficial way. Knowledge activation or use (power) demands specific prior knowledge, special technology and/or skills.

Technology Utilization and Manual Skills At present, knowledge support and healthcare teamwork are the only fundamental components in a provision of healthcare that is electronically transportable. The computer-support for the components of technology utilization and manual work will be developed further through research in the next few years and when combined with the advantages associated with other technologies applied in healthcare (e.g., robotics, miniaturization, electronics, nanotechnology, and the use of composite materials), it has the potential to advance healthcare significantly.

SECTIoN TWo: ThE ClINICAl perspectIve on archItecture and servIces Clinical Context Sensitivity Currently there is no defined term for clinical context sensitivity. In the next few years it will be necessary to develop and define clinical context sensitivity for PH in order to elevate it to a more universal, clinical method of healthcare delivery. In this chapter clinical context sensitivity will be defined as a multi-axial, individual form of information space (MIIS) that communicates with a model framework such as “the virtual patient”

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or perhaps “the virtual healthy citizen” (to be discussed later in this chapter). The MIIS around each person issues automatic notifications, subscribes, filters, and applies (personal) weights to information. Hence, MIIS modulates and individualizes decision support for the patient. The MIIS is the patient stand-in in the PH-knowledge space. There is a publication rate of around 6,000 biomedical papers on average each day. This means that the amount of available health information is huge, rapid changing, and for the single individual, on the whole, not of interest for the most part. To take advantage of the current medical knowledge each individual may benefit from pre-defined information filters defined by the scope of the PH-system and the nature and character of the patient’s disease, state or condition.

Disease Stage Context The American National Library of Medicine PubMed database, which indexes nearly all biomedical papers, defines the following categories for clinical queries: etiology (or aetiology—the study of causation), diagnosis, therapy, prognosis, and clinical prediction guides. Transferred to the personal information space, this could be expressed as the continuum that one (i.e., the patient) may pass through during the natural course of a chronic disease, from: • • • • • • • • • • • •

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Healthy Staying healthy Feeling ill Feeling sick Do I have a disease? Seeing your doctor (crossing the iatrotropic threshold) Before confirmed diagnosis Newly diagnosed Seeking second opinions Acute Prognosis Chronic ongoing

•

•

Maintenance and compensatory measures (tertiary prophylaxis against late complications) Home care (devices for delivery of care or drugs)

Other categories along these axes could include general information about the condition, epidemiology, demography, and/or being a relative of the person diagnosed with the disease.

Competency and Resource Sensitivity The terms “novice” and “expert” are used and defined in other chapters of this book. It is usually difficult to use these terms when assessing competencies in chronic patients. This is due to a natural lack of “academic distance” to ones own personal state of health. This lack of distance makes it difficult to utilize even expert knowledge about underlying disease mechanisms. The phenomenon is so general and human, that most doctors, when they contract a health condition (even in their own area of expertise) are unable to “cope” with the disease in an “expert-way.” It could be that the expert shifts his usual decision pattern from forward reasoning to subjective, empathetic-reasoning; hence such an individual may say “this is not the case for me.” Other chapters of in this book will provide more in-depth information about clinical reasoning and knowledge handling methods employed by individuals. One of the great potentials for PH is its ability to provide a rational, academic distance and analytical perspective with arguments that minimize the empathy component in decisions on health. Therefore, PH allows the patient to obtain a “better, personal health understanding or reasoning.” The virtual patient (see later) has to also include models having a very interesting synthesis of computer science, clinical reasoning, human factors, (clinical) knowledge handling, and cognitive psychology.

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Self-Care as the Primary healthcare provision The different needs of patients in “the disease stage context” have in a traditional, industrial society been sequential in nature and have been “matched” to differing types of provisions of healthcare. Conventional types of provisions of healthcare include the following acts: diagnosis, treatment, monitoring, training and rehabilitation, personal care, prophylactic treatment and lifestyle change or modulation. PH has the potential to combine some of these types of provisions of healthcare into a single “self-care (fused) provision.” Replacing the current sequential approach to care with an information-society based approach, where acts are occurring in parallel. Furthermore, the continuum between health, wellness and compensatory measures for patients would be augmented by PH. PH would potentially help patients to maintain a feeling of healthiness despite having a disease. The aim of society should be to make PH-supported self-care the primary method of providing healthcare. Society (or appointed representatives of the organization such as HMOs) are “topping up” with the necessary professional provisions including PH to complete the job of helping the patient to self-manage their disease. Other Clinical perspective on architecture will be illustrated with a scenario (see Figure 1).

scenario Karen is seven years old and has been a diabetic for three months. She is now on holiday in a relatively remote tourist and fishing village at Skagen in Denmark, which is not her native country. Her parents were a little anxious to bring her, since her disease has not been stable, and the “family pool” of knowledge about diabetes is still not sufficient. Karen carries her MIIS-device in the physical form of a small teddy bear, which communicates with the PH-infrastructure, when she presses

its nose. The MIIS organizes the information in different profiles, such as general characteristics, emergency information for paramedic use, information for healthcare professionals, food, physical activities, medications etc.…. The family decides to go to a restaurant and Karen approaches the sign outside. It reads her general profile of information, lowers to her height, and then displays the “children’s diabetes menu.” When inside and ordering an item from the menu—the “virtual patient” calculates the insulin-dose, and the corresponding physical activity advice relative to Karen’s food intake and displays the different options on the menu in the restaurant for Karen and her parents to see and to help with their decision-making. The family wants to take a walk in the dunes after the meal. When the food arrives the “virtual patient” reprograms her insulin pump to deliver the appropriate dose of medication based on her current insulin sensitivity and her usual reaction to moderate physical exercise and the meal chosen from the menu.

Comments about the Scenario Since Karen is carrying a device, an insulin pump, it would be natural to integrate the MIIS-device in the pump that will act both as a sensor and an actuator in the PH-architecture. From a human perspective there are several reasons that make it feasible for people to “conFigure 1. Scenario illustrating the potential impact of pervasive healthcare

Childrens menu

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trol” their own information “physically,” hence the teddy bear in the scenario. Someone could, of course, obtain the same by a distributed information architecture and identification, such as secure-RFID-tags or similar technology, but the personal dimension would be lost and the individual needs for interfacing the MIIS to any “personal gadget” (such as pedometers, bikes, exercise-monitoring devices,) for personal ways of collecting data may be more cumbersome. MIIS could be an enabling-technology of individual, personal paraphernalia—a gadget for patients and for the healthy—embedded in for example mobile phones, Swiss army knives, teddy bears, Gucci purses, Rolex watches, and so on. A scenario may also illustrate the everyday gains associated with the use of PH. Karen’s quality of life might improve as hopefully she will have fewer long-term complications associated with diabetes in 15 to 30 years. Karen could have received diabetic care without PH, but she might not have been able to go on holiday in another country so shortly after being diagnosed. This illustrates that gains of PH are mainly on the individual aspects of quality of life and care and the societal gain may be more difficult to demonstrate—at least in the near future. The near future societal gain of PH applications may be the ability to distribute healthcare services in times of need (e.g., natural disasters), given that a new and effective PH self organising infrastructures could be deployed in a short period of time

The “Virtual Patient:” “The Virtual living and Staying healthy Person” For years, the following question has been asked: “why is healthcare so special, we use sophisticated computer technology to fly, fill, and maintain airplanes and space rockets, handle complicated financial transactions, and so forth so it must be the arrogance and resistance of health professionals to change, that makes it difficult to penetrate the healthcare market?” This maybe so, but there

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are some other reasons as well that prevent PH from being adopted: •

• • • •

•

•

The expectations and values of the postmodernistic patient and the nature of disease processes. The current representation of the object of work or the object of interest (OOI). The nature of our current “models” of the OOI and data foundation problems. The specificity of sensors and actuators (especially in relation to PH). The participatory nature of health-decisions between caregiver, relatives, and objects/ subjects of care. The non-algorithmic nature of health decisions (covered in other chapters of this book). The state of information technology maturity.

The Current Representation of the Object of the Work We need a “virtual patient” to bring healthcare into the information age. Computer technology has developed from a tool that was used by the military and astronomers2 to a tool that is now used everyday by many people in offices professionally and publicly. The most successful information technology applications represent “the object of work” in a constructive and productive manner to the office worker. This allows the office worker to do the work on the computer. Current electronic clinical systems represent the patient indirectly by some facts, considerations and descriptions of procedures done by healthcare professionals. This is not a coherent and productive model of “the object of work” neither for the patient nor for the healthcare worker. The patient’s condition will not improve by an entry in his or her electronic patient record, cases are not considered to be done by entries in the record. Instead, the record is at present used as a necessary communication and

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memory aid, but not as a cure for disease. PH is concerned with healthcare interactions between the two (i.e., the patient and the healthcare worker) and in that sense closer to cure than to documentation, but would gain additional benefit by the development of “the virtual patient” as the digital representative of the patient.

“Models” of the Object of Work (or OOI) Models are simplified representations of reality that may accentuate certain aspects of reality. They usually show relevant objects and describe the relationships between these objects. There are two types of variation in the provision of healthcare: (1) unwanted variation due to lack of evidence, materials or methods, and (2) wanted individuality in the provision of healthcare arising from the very (post-modernistic) human nature of the patient and the provider. The overall aim of quality assurance in healthcare is to minimize the unwanted variation and promote the wanted variation arising from the individuality and uniqueness of the single patient. This is different from other industries (e.g., aviation) where there are attempts to reduce individuality, uniqueness and variation. To minimize unwanted variation in medical practice and learning, models of the non-existent, average patient that has specific types of health conditions are used in descriptions of best practices and standard operating procedures (SOP). Healthcare professional use their clinical knowledge to tailor and modify SOPs to the individual patient’s needs and concurrent medical, social or other problems during the actual delivery of healthcare. Therefore, implicitly, it is not a good idea to transfer the restrictive nature of a standard SOP-average-patient model to a computer environment when creating a virtual patient. There is a need for a more “true” representation of a patient for use in PH decision support. A model framework needs to be developed. The virtual patient must be sensitive to the clinical context and to other

human, and individual factors such as: decision modes, motivation, resources, concurrent health, social, and mental health problems. The model must also provide a linkage to “the back-office” platform and databases that have fundamental digital, genetic, biochemical, anatomical, physiological, and behavioral data, and be sensitive to information in the patient-MIIS (see Figure 2). The virtual patient is linked to the front-end user and provides context sensitive data fused with “human data” (context sensitive “translations” and displays) of information that form the basis for end-user knowledge acquisition within a healthcare context. The end-users are health professionals, patients, healthcare professionals, family members and other interested parties. The presented information would probably require new ways of “browsing” complicated multi-axial information-structures in relation to each other (see Figure 2).

Data Foundation Challenges As an example, let us examine genetic information about humans and micro-organisms in a virtual patient framework: a dream for the future. In less than five years, the cost of documenting a near complete version of the human genome (i.e., DNA sequence) with its six-fold coverage may drop from $10-20,000,000 to $100-200,000. In 10-15 years, the cost may be as low as $1,000. Such dramatic advances will come through the use of novel, ultra-low cost sequencing (ULCS) techniques. Such prototypes are just starting to appear (Shendure, Mitra, Varma, & Church, 2004). Equally, predictable is the advancement of techniques for functional and structural characterization of the human genome. The impact of ULCS on biomedical research and public health will be profound. When the costs for ULCS drop to below $100,000, human genomes and genotyping will probably be readily available to be used in large hospitals to provide quicker and more precise identification about

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Figure 2. A simplified diagram of a (clinical) PH-architecture

genetic variations that cause disease. The ability to compare complete genomes for normal and neoplastic malignant cells will allow for cataloguing of cellular function perturbations that cause inappropriate transformations. This will be the first step towards finding specific cures for some disease. A $1,000 genome offers the potential for ”individualized healthcare” in a clinic, including diagnosis, learning about prognoses for particular inherited diseases, risk assessments, prevention strategies, sensitivities to certain drugs, and the design of drugs for specific individuals. Epidemiological data could also be connected. Old samples (e.g., cancer biopsies) could be (re-) analyzed, and much more could be possible. This could form the basis for the development of a more specific body of knowledge about the transfer of information targeted towards providing advice about diseases with a genetic component while employing the distributed and counseling power of pervasive healthcare technology and it would provide the foundation for intelligent PH services beyond the capabilities of our current healthcare structures.

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The same genetic information could be used to diagnose and fight diseases caused by microorganisms (which may be the case for some cancers) through the process of rapid sequencing. For example, in the future, one might see the use of rapid sequencing of bacterial RNA from a tissue sample as being used to give a precise diagnosis of an infection. This could probably be done in a few minutes rather than a few days as is currently the case (i.e., it takes several days to grow bacteria on agar-plates for the identification and functional characterization of bacteria as well as to determine the type of bacterial resistance that a bacteria has to different types of antibiotics). However, vastly improved genetic testing alone will not necessarily translate into great benefits for patients. For that to happen, the data have to be available, accessible, and consistent in various ways. Data has to be inter-connected, navigatable, and smoothly integrated with the many ways the user (e.g., clinician, patient) looks at the patient data It is far from clear at this point, which measures can and should be taken to en-

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sure that new scientific and technical advances actually help with fighting disease. Pervasive infrastructure and technology alone are not an adequate prerequisite.

Data Issues A number of questions or issues currently exist in the domains of clinical and biomedical informatics as well as the areas of practical genomic software and data integration. The following are some examples of questions and/or issues in these areas (there are undoubtedly several more examples): 1.

2.

Will more data create more inconsistency? A consequence of the widespread availability of cheap technology, there are many more sites that can afford to generate their own data, using separate names, protocols and formats. Therefore, the answer is probably yes. If so, what political, organisational and practical steps can be taken to prevent such additional chaos? Comment: At this moment in time are we ready to begin proactive standardization of names, protocols and formats? Will consistent, perhaps global, data repositories be needed? How critical will they be? In the future a doctor will be able to match a patient’s genetic profile against profiles typical for certain diseases and receive a list of diseases to contrast with other clinical evidence. This will only occur if the data are consistent and comparable. A repository with consistent data will need to be in a well defined network locations, and will need to have at minimum high quality data. For example, consider the simple question “return all expression data for gene A, B, and C in a given tissue.” Currently, this question is difficult to answer, since there are at least the following major sites with data:

1. 2. 3. 4.

5. 6. 7.

Gene Expression Omnibus (NCBI, USA) Stanford Microarray Database (Stanford University, USA) ArrayExpress (European Bioinformatics Institute, England) Whitehead Institute Center for Genome Research (Cambridge Massachusetts, USA) Gene Expression Atlas (Novartis Research Foundation, Switzerland) RNA Abundance Database (University of Pennsylvania, USA) Public Expression Profile Resource (Children’s National Medical Center, USA)

Some of the data sets appear in more than one of the collections listed. In some cases the same data are given different names, formats, and experimental protocols. As well, data names, formats and experimental protocols may change at any time without notice. This is clearly not the best situation. Several questions emerge when considering the above outlined issues: How much will it cost to make these changes? How much would it cost to avoid these issues (if at all possible)? Yet, there are still several single nucleotide polymorphism (SNP) repositories and many other kinds of not as yet coordinated data sites. Therefore, for example, one might instead want to ask a simpler question “what new bacterial genomes are out there?” and “how difficult is that to determine?” Two very difficult questions to answer. GenBank and other major sites collect genomic data and save it in the most consistent ways. There is much more genomic data available in smaller sites, where the micro-organism’s name, is not precise and/or consistent. There are many such examples of data that are “available but not accessible.” This means that emerging genetic micro-organism detection methods will not be accessible for PH applications in the imminent future.

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There are a number of obstacles that prevent genetic data from being connected to clinical observations. Today, genotype/phenotype relations are mostly found in journal articles. Therefore, researchers (or patients/relatives) must mine the research literature to find this data. This process is slow and error prone. There does not seem to be a well organized repository of genotype/phenotype data. Most of this essential data is described in other ways. Therefore, genetic databases will need to use high quality clinical annotations such as: • • • •

Disease description (histology, physiology, etc.) Disease genotype, prognostic markers Experimental evidence for marker usefulness Literature and links for those genomic databases to be useful to patients, families and clinicians

Cancer is a good example of disease where genetic data are collected. The collection of genetic data in databases should provide information about complex as well as single-gene information that causes disease. A genetic cancer data repository should support searches by disease, return information about the known markers of disease, and provide research based evidence for treatment so that there is some diagnostic value associated with collecting such information. As well, searches should return information about genes or genomic regions so that disease related information is returned along with information about associated markers. Several other questions can also be asked: What are the most needed types of linkable data? Do nomenclature issues create critical disconnects? Such questions are important as linking expression data with disease phenotypes in a consistent nomenclature is critical. Presently, there are significant losses of information due to nomenclature issues and/or incomplete ontologies. Researchers also need to ask: How much genetic

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data is currently covered by ontologies? Are the ontologies of sufficient quality for practical use? Are there enough ontologies available? Are they freely (i.e., the ontologies) usable? Which database projects are the most critical to coordinate with? What data is proprietary and what would be the effect of making proprietary data public?

Software Design Researchers need to address the issue of local versus remote data. At present, patient data cannot travel across the Internet to be analyzed at a few central servers on a routine basis due to confidentiality requirements and bandwidth limitations associated with handling such data. On the other hand, all the world’s data that are relevant to a given “case” cannot reside on a local server. The solution is a combination approach. Here, data of local interest resides locally and general data centrally like the idea behind the MIIS. Researchers also need to determine who should decide where the data resides and how it should be monitored.

Scalable Viewers If today’s genomics user interfaces were used with a thousand times more data. The user would drown in the details. Viewers are needed that offer higher level views (i.e., that scale well). Growing desires to project patient data into a larger genomic context will definitely occur if it can be done in a seamless, confidential and secure way.

Ethical Issues Before genetic information from repositories can be used to serve individual health issues in a PH structure, practical ethical organizational guidelines and responsibilities that protect the patient need to be established. For example, can an HMO demand a genetic profile and refuse to insure an individual for potential future genetic

Pervasive Healthcare

diseases? As well, can an employer use genetic or lifestyle information to refuse benefits, promotion or compensation to employees? Is the citizen bound to provide comprehensive genetic and health related (the MIIS) information to an HMOs, employers, or society?

The Specificity of Sensors and Actuators The simplified architecture of PH forms a loop from sensor to a computer-supported “intelligent” environment, to an actuator giving an appropriate response to the condition sensed. The appropriateness of the corrective response by the PH is dependent upon: •

• • •

The specificity, sensitivity, and relevant response-characteristics of the sensor relative to the signal The specificity of the computer-supported “intelligence” relative to the signal The specificity and appropriateness of the actuator to correct the condition A proper balance between the three components giving a flexible and clinically relevant response to a range of possible conditions

One of the potential benefits of PH could be what the European Commission in its research programmes calls: “ambient assisted living (AAL)” which means that cognitive and physical impairments in specific patient populations (e.g., in elderly persons) are compensated for by technology and extend the patient’s normal, active, independent lifestyle as long as possible. Problems with PH are not limited to specific sensors with appropriate sensitivities and actuators for an appropriate response. Technology has not yet been developed to support the needs of a physically dependent, cognitively impaired elderly individual, living alone in their own home that forgets to turn off the stove on occasion. Maybe an unspecific “behavioral” sensor could

be constructed by fusing data inputs from a number of different types of sensors. Massachusetts Institute of Technology (MIT) has constructed a “living lab” in the form of a home that has several thousand sensors incorporated in its structures. People are invited to live in the home for a period. Sensors and actuators are tested to determine how data can be fused. Since the specificity and sensitivity of sensors and actuators for the majority of potential PH applications is missing, it is difficult to construct a “computer-intelligence” that targets the patient’s healthcare problem. Sensors are unspecific for a complex health condition such as Alzheimer’s disease but specific to heartbeat and other distinct electrophysiological sources.

The Participatory Nature of Health-Decisions Between Caregivers, Relatives, and Object/Subjects of Care As discussed in part one of this chapter, healthcare is a participatory process that takes place between a patient, their family and healthcare professionals. This process is fundamental and differs from built models of human behavior in other domains, where models in general do not leave room for participatory decision activity, second opinions, and alternative routes and plans between service providers and recipients of service.

Altered Production Settings in Ph The provision of healthcare is for the most part regarded as industrial production. This despite the fact that provision of healthcare does not have the usual characteristics of an industrial product that can be serially produced, stocked, transported, sold, and consumed independently. Paradoxically, PH can bring healthcare into the information age (by means of “the virtual patient”) and at the same time enable a more industrial like mode of production. PH has the potential to bring unique, on-demand, individ-

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ually-produced provisions of healthcare based upon manufactured (computer-intelligence) of raw material (knowledge). This implies that PH can bring the provision of healthcare closer to the industrial product by de-coupling production and consumption and PH has the potential to stock and transport semi-manufactured provisions of healthcare for individual consumption (at a chosen time and place) by an individual citizen by means of a computer-supported self-care environment.

Infrastructure Discussion Figure 3 gives a rough picture of the needed infrastructure for distributing the components in a provision of healthcare (as outlined previously and explained in more detail in Nohr’s and Boye’s chapter in this book). PH-structure has three zones of influence: the delivery zone, the distribution zone and the knowledge zone (“raw materials for production”). The knowledge zone houses all the usual stakeholders of healthcare (e.g., physicians and nurses). It pro-

vides the healthcare stakeholders with an interface to the “ubiquitous” zones (i.e., the distribution and delivery zones). The client (patient, citizen) is present in both the knowledge zone and the delivery zone, since the primary PH-healthcare provision is self-care in a computer-supported context sensitive, knowledge intensive environment. It also underlines the participatory involvement of the patient in the healthcare team. A universal distribution zone for PH with ambient facilities does not exist at present. Each PH project has so far constructed specific solutions for only the distributive aspect of PH. The predicted distribution zone incorporates “the virtual patient” (i.e., a universal model of health, behavior, reasoning, lifestyle-advice, compensatory and disease modifying mechanisms). The interaction between the model and the patient starts with the instantiation of the model within “the clinical context” using a MIIS-device and other relevant context information that is needed to produce a response. The PH healthcare-loop is established between the patient-proxy (MIIS),

Figure 3. PH infrastructure Persons in primary organisations Gateway

(Health) Collaboration grid

Hospital/ Health centre GP/ Homecare

Sensors/ Actuators

Independent providers

Local manual skills

Distribution Services

Relations Client

The Client Delivery-zone

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Knowledge -zone

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sensors, the intelligence, and actuators. The use of additional technology and manual skills to complete the provision of healthcare must be obtained from outside the general PH structure, but, ideally, those who provide healthcare should be able to communicate within the PH structure. The “virtual patient-model” could be housed in a computer-supported teamwork system (i.e., software) (in Figure 3 this is called the “health collaboration grid”). Building, running and maintaining the “health collaboration grid” would be difficult. Building a health collaboration grid could have a similar impact upon healthcare, business, computer-methods, and secondary healthcare technology as putting a man on the moon had for the physical and astronomical sciences, materials, and communication (even though the mission would not be as spectacular as the endeavor for the moon!).

SECTIoN ThREE: CURRENT trends and Future research dIrectIons The history of scientific and clinical knowledge acquisition in medicine gives foundation for speculation about how the future maturation of PH might occur on a higher level of abstraction. Medical knowledge started on the whole-body level observing disease and speculating on reasons for its occurrence. In different isolated parts of the world diverse disease-models were developed—a kind of “virtual,” abstract patient. In China the model was build around the meridians in the body where energy was flowing, and disease was considered the result of wrong energy flows, which could be corrected by traditional Chinese medicine, including acupuncture. In the old world during the 16th and 17th century the Western Medical disease model was gradually formed by observing anatomy, physiology genetics and biochemistry gradually going into more and more detail. The search for details in bio-medicine is the basis of

the rate of approximately 6,000 new publications in average a day. One could say: it is a “top-down approach” from gross anatomy and behavioral patterns to biochemistry and genetics. In bio-informatics and model building for pervasive healthcare one starts with the details (i.e., in the algorithms, classifications, and gene-maps) and has to compile this massive amount of data into a coherent and balanced model in the virtual world of the object of interest (the human). It is a “bottom up approach” as complicated as the biomedical research “top-down” approach. State of the art of pervasive healthcare technologies (as of the year 2006) are coming mainly from a computer science perspective—described in a recent book edited by Bardram, Mihailidis and Dadong (2006). From this book it also appears— from a clinician’s point of view—that PH is not yet a mature and general method to deliver welldefined health-care provisions. It is however still a promising and exciting experimental field where multi-disciplinary collaboration is producing the foundation that shows most potential. A distinction between telemedicine and pervasive healthcare has not been made and is maybe not possible. In the mind of the author “telemedicine” is more about monitoring and surveillance health and “pervasive healthcare” is more about computer-based servicing of health needs—intelligent—demonstrating the whole loop from sensor(s) via intelligence to an actuator responding to the sensed condition. Another distinction that arises is the following— who is in charge? One interpretation could be the following: in telemedicine it is the institution operating the system, in pervasive healthcare it is the team-member that is the object and subject of care, that is, the patient. Research and development is occurring rapidly in the field and since the technology is still immature it is mostly very specific at this moment focused on single components of the information architecture. The Internet is the appropriate way to stay informed about industry research and products, conferences, and framework programs

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from national and international organizations and agencies. Since a regular overview will be outdated quickly some fields of study (that can be used as keywords for search) are listed thematically according to the components of the pervasive loop.

The pervasive loop •

•

•

•

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Sensors: Sensors are a field of intensive research, ranging from study of materials in nanotechnology to incorporation in “intelligent band aid” and “intelligent textiles,” to physiological response characteristics and algorithms (embedded software), to ways of transferring (coupling) the sensor signal to a network. Miniaturization is an important part of the efforts. Many sensors are currently built on electro-physiological principles sensing signals from the brain, nerves, heart or muscles, but other principles will be matured and developed and in the near future. Network: Major developments in recent years have been seen in body-area networks (BAN), personal-area-networks (PAN) components and power-supply, gateways and transmission, short-range-radiowave technologies (RFID, Bluetooth, Zigbee, others), security, and integration in structures and industrial products. Intelligence: This includes data fusion algorithms, information presentation (Virtual-reality), decision support models and methods, “the virtual patient” (starting with physiology) and other OOI-representations. Actuators: Very few actuators for pervasive healthcare have actually been developed and in the market. This is a field of great commercial potential although the rest of the “pervasive loop” must be in place for actuators to make a difference in any individual’s life.

•

Integration in society: There is still not sustainable business models developed for PH, as pointed out in the start of this chapter. Ethical and legal aspects are insufficiently developed for the concept to be marketable. Pervasive healthcare will need technical and semantic integration with other health related data repositories. As pointed out in the chapter with the example of genetic data, this is not a trivial exercise to carry out. Models and standardized vocabularies (e.g., SNOMED CT) and interface standards in healthcare (e.g., HL7) must be developed to maturity, where they may serve the needs of all the actors, including patients, professionals and organizations.

reFerences Bardram, J.E.., Mihailidis, A., & Dadong, W. (2007). Pervasive computing in healthcare (1st ed.). New York: CRC Press. Nohr, C., & Boye, N. (2008). Towards computer supported clinical activity: A roadmap based on empirical knowledge and some theoretical reflections. In A.W. Kushniruk & E. Borycki (Eds.), Human, social and organizational aspects of health information systems. Hershey, PA: IGI Press. Qvortrup, L. (2003). The hypercomplex society— Digital formations (vol. 5). New York: Peter Lang Publishing. Shendure, J., Mitra, R. D., Varma, C., & Church, G. M. (2004). Advanced sequencing technologies: Methods and goals. Nat.Rev.Genet., 5, 335-344.

addItIonal readIng IEEE Pervasive Computing is a quarterly magazine, advancing research and practice in mobile and ubiquitous computing in a clear and acces-

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sible format. It is published by the IEEE computer society and has many papers dealing with health issues and occasional thematic volumes on health. Core technologies such as sensors, actuators and networks are covered. A recent book that describes a range of aspects of pervasive computing in healthcare is the following:

endnotes 1

2

Bardram, J.E., Mihailidis, A., & Dadong, W. (2007). Pervasive computing in healthcare (1st ed.). New York: CRC Press.

The term clinical will in this chapter be used of every healthcare related activity were a core provision is transferred from a system or person (including the patient) to a person or group of persons—despite the location and mode of transfer. In 1943, the chairman of IBM said: “I think there’s a world market for maybe five computers” – computers were something else at that time, but the quotation brings perspective to any prediction of the future (with computers) including this chapter.

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by A. Kushniruk; E. Borycki, pp. 84-101, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 4.2

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching Masoud Mohammadian University of Canberra, Australia Ric Jentzsch Compucat Research Pty Limited, Australia

abstract

IntroductIon

Radio frequency identification (RFID) is a promising technology for improving services and reduction of cost in health care. Accurate almost real time data acquisition and analysis of patient data and the ability to update such a data is a way to improve patient’s care and reduce cost in health care systems. This article employs wireless radio frequency identification technology to acquire patient data and integrates wireless technology for fast data acquisition and transmission, while maintaining the security and privacy issues. An intelligent agent framework is proposed to assist in managing patients’ health care data in a hospital environment. A data classification method based on fuzzy logic is proposed and developed to improve the data security and privacy of data collected and propagated.

Research into the use of developing and evolving technologies needs to be expanded in order that society as a whole can benefit. Radio Frequency Identifiers (RFID) have been around for many years. Their use and projected use has only begun to be researched in hospitals [Fuhrer, P. and Guinard, D. 2007]. This chapter research considers the use of RFIDs and its potential in hospitals and similar environments. Furthermore RFIDs are used to collect data at its source while developing profiles for patients and their care. There are four areas where using RFIDs and their data collection can have significant positive effects in hospitals. These four areas are: •

Care tracking: this is getting the right care to the right patient at the right time.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

• •

•

Quality of care: improving the services given to the right patient at the right time in a timely manner. Cost of care: finding ways to be effective in the use of available resources such that the cost per patient per incident does not adversely increase to the cost of the resources. Service of care: better, more timely information for a more informed decision making process, to provide more knowledgeable individual tailored care.

RFID tags and readers are most commonly are associated with tracking goods in manufacturing and warehousing, but hospitals are starting to apply RFID to new purposes [Kowalke, M. 2006]. RFID technology does not require contact or line of sight for communication, like bar codes. RFID data can be read through the human body, through clothing, read wirelessly, and through non-metallic materials. Both research and practical application of the use of RFIDs in hospitals continues to be of importance. For hospitals this has meant the potential of managing inventories in a more efficient manner. Inventories in hospitals take on a variety of differences than to manufacturing. The nature of the inventory and assets in a hospital can include various types of equipment (that is often very expensive, comes in many sizes, and uses), drugs (that come in a variety of sizes, shapes, color, and governing regulations), beds, chairs, patients (the primary reason hospitals exist), and staff. The percentage of worldwide radio frequency identification (RFID) projects concerning peopletagging has increased from eight percent to 11 percent since 2005 [ Tindal, S. 2008]. However, the healthcare sector has yet to quantify or provide evidence of the benefit to people-tagging. Human chipping is not new but does bring up a lot of ethical questions [Angeles, R. 2007]. RFIDs are used in hospitals for tracking highvalue assets and setting up automated maintenance

routines to improve operational efficiencies. However the use of RFIDs in tracking beds and tracking mobile equipment is in its infancy. RFIDs is used to monitor equipment for example how long a bed was used at a particular location to determine a sterilization schedule as well as bed location tracking. However RFID technology is already being deployed across the pharmaceutical industry to combat drug counterfeiting, drugs shelf life tracking [Kowalke, M. 2006]. Managing expensive, often difficult to replace, and legal drugs can only be improved using RFIDs. The management of patients and their condition is paramount in a hospital. RFIDs can assist in asset and personnel tracking, patient care, and billing where unnecessary expenses will be cut, the average length of stay of a patient is reduced, where more patient lives will be saved due to timely efficient services, and where patient records are actively continuously updated to provide better patient care. [Kowalke, M. 2006]. An RFID chip stores the wearer’s data that can be accessed by a hand-held reader. This makes patient identification more reliable, provides updated patient condition nearly instantly, and improves the cost of health care. The health sector is already taking up peopletagging where it allows nurses to radio their location if they are being assaulted, reduce mother baby mismatches and baby theft, help severe diabetics with getting correct treatment, and monitoring disoriented elderly patients without the need for a dedicated member of staff [Tindal, S. 2008]. The need is not to keep track of staff but be able to locate the staff with the particular skills that are needed at the right time and place. Staff wearing badges with RFIDs embedded can be found to help provide that needed and timely care that a patient may need. However privacy concerns have been aired over patient tracking using RFIDs. A patient upon arrival to a hospital can be is issued with an RFID tag which can contain information

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

concerning the patient such as their surname, first name, reason admitted to hospital, date of admission, their doctor’s name, a patient number and a section for monitoring. Monitoring could include heart rate, blood pressure, and some other vital signs. Monitoring would be settable to the need of the patient. For example once an hour might be sufficient for most patients, but for others every 15 minutes might be sufficient. This illustration shows how a particular section of a hospital might be configured according to the needs of that section. Each patient has a patient-tag. Each patient’s bed has a bed-tag. Spaced out within rooms, hall ways, and hospital staff stations are receivers. Every 15 minutes the receiver interrogates the bed and the patient tags. The patient’s vital signs are sent to the patient database where the patient’s condition is recorded. The patient care profile is then updated with this information. If anything is out of range or an exception is identified the nearest nurse station to the patient is then contacted. There is a need for more research into applications and innovative architectures for secure access, retrieval and update of data in healthcare systems [Finkenzeller. K. (1999), Glover. B and Bhatt H. (2006), Hedgepeth W. O. (2007). Lahiri, S. (2005), Schuster E. W., Allen S. J. and Brock D. L. (2007). Shepard S. (2005), Angeles, R. (2007), Pramatari, K.C., Doukidis, G.I. and Kourouthanassis, P. (2005), Qiu R, Sangwan R. (2005), Mickey, K. (2004), Whiting, R. (2004), Weinstein, R. (2005)]. Although many organizations are developing and testing the possible use of RFIDs the real value of RFID is achieved in conjunction with the use of intelligent software agents for processing and monitoring data obtained via RFIDs. Thus the issue becomes the integration of these two great technologies for the benefit of assisting health care services. This article considers a framework using RFID and Intelligent Software Agents for managing patients’ health care data in a hospital environment. A fuzzy data classification system is also developed to improve the application of regulatory data requirements for security and privacy

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of data exchange. The chapter is divided into four main sections. Next section considers issues relate to data collection and profiling. Section three is based on the patient to doctor profiling and intelligent software agents. The fourth section covers RFID background and provides a good description of RFIDs and their components. This section discusses several practical cases of RFID technology in and around hospitals. It will also list three possible applicable cases assisting in managing patients’ medical data. The final section discusses the important issue of maintaining patients’ data security and integrity and relates that to RFIDs.

data collectIon Large amount of health care data such as patients, doctors, nurses, institution itself, drugs and prescriptions, diagnosis, and many other areas is collected and stored in hospitals. It is not feasible or effective to use RFID to collect and retrieve such large amount of data. This chapter concentrates on a subset with the understanding that all areas could, directly or indirectly, benefit from the use of RFID and intelligent software agents in a health care environment. The RFID [Bhuptani, M., & Moradpour, S. (2005)] provides the passive vehicle to obtain the data via its monitoring capabilities. The intelligent software agent provides the active vehicle in the interpretation profiling of the data and reporting capacity. By investigating and analyzing collect patient data the patient’s condition can be monitored and abnormal situations can be reported on time. Using this information an evolving profile of each patient can be constructed and analysed. Analyzing the data can assist in deciding what kind of care a patient requires, the effects of ongoing care, and how to best care for this patient using available resources (doctors, nurses, beds, etc…) for the patient. The intelligent software agent builds a profile of each patient as they are admit-

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

ted to the healthcare institution by analyzing the recorded and stored data about each patient. The same way a profile for each doctor is developed based on stored data about each doctor. Therefore patients and doctors profile can be correlated to obtain the specialization and availability of the doctors to suit the patients.

Patient Profiling Profiling is combined with personalization, and user modeling [Wooldridge, M. and Jennings, N. (1995)]. The use of profile in hospitals and healthcare so far has been limited. Tracking of information about consumers’ interests by monitoring their movements online is considered profiling or user modeling in e-commerce systems. By analyzing the content, URL’s, and other information about a user’s browsing path/click-stream a profile of a user behavior is constructed. However patient profiling differ from user profiling in e-commerce systems. The patient profiling is useful in a variety of situations such as providing a personalized service based on the patient and not on symptoms or illness to a particular patient as well as assisting in identifying the medical facilities in trying to prevent the need for the patient to return to the hospital any sooner than necessary. Patient profiling also assist in matching a doctor’s specialization to the right patient. A patient profile can also assist in providing information about the patient on continuous bases for the doctors so that a tailored and appropriate care can be provided to the patient.

patIent to doctor proFIlIng A patient or doctor profile is a collection of information that can be used in a decision analysis situation between the doctor, domain environment, and patient. A static profile is kept in pre-fixed data fields where the period between data field updates is long such as months or years. A dynamic

profile is constantly updated as per evaluation of the situation in which the situation occurs. The updates may be performed manually or automated. The automated user profile building is especially important in real time decision-making systems. Real time systems are dynamic. The profiling of patient doctor model is based on the patient / doctor information. These are: •

•

•

The categories and subcategories of doctor specialization and categorization. These categories will assist in information processing and patient / doctor matching. Part of the patients profile based on their symptoms (past history problems, dietary restrictions, etc.) can assist in prediction of the patients needs specifically. The patients profile can be matched with the available doctor profiles to provide doctors with information about the arrival of patients as well as presentation of the patients profile to a suitable, available doctor.

A value denoting the degree of association can be created form the above evaluation of the doctor to patient’s profile. The intelligent agent based on the denoting degrees and appropriate, available doctors can be identified and be allocated to the patient. In the patient / doctor profiling the agent will make distinctions in attribute values of the profiles and match the profiles with highest value. It should be noted that the agent creates the patient and doctor profiles based on data obtained from the doctors and patient namely: • •

Explicit profiling occurs based on the data entered by hospital staff about a patient. Implicit profiling can fill that gap for the missing data by acquiring knowledge about the patient from its past visit or other relevant databases if any and then combining all these data to fill the missing data. Using legacy data for complementing and updating the

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Figure 1. Agent profiling model using RFID

patient data

Patient database

Staff database

RFID patient data Patient profile

staff data

Staff profile Patient Profile Agent Engine

Staff Profile Agent Engine Rule Base

Rule Base

Profile Agent Engine Rule Base

Matching Profile Agent

user profile seems to be a better choice than implicit profiling. This approach capitalizes on user’s personal history (previous data from previous visit to doctor or hospital). The proposed agent architecture allows user profiling and matching in such a time intensive important application. The architecture of the agent profiling systems using RFID is given in Figure 1. Profile matching done is based on a vector of weighted attributes. To get this vector, a rule based systems can be used to match the patient’s attributes (stored in patient’s profile) against doctor’s attributes (stored in doctor’s profile). If there is a partial or full match between them then the doctor will be informed (based on their availability from the hospital doctor database). Profile matching [Doan, A-H. Lu, y. Lee, T. Han, J (2003)] performed is based on a vector of weighted

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attributes using an intelligent agent system. To get this vector, the intelligent agent uses a rule-based system to match the patient’s attributes (stored in patient’s profile) against doctor’s attributes (stored in doctor’s profile). If there is a partial or full match between them then the doctor will be informed (based on their availability from the hospital doctor database). Such a rules based system is built based on the knowledge of domain experts. This expert system is extensible as new domain knowledge can be added to its knowledge base as rules. Large amount of research in the area of profiling in e-commerce, schema matching, information extraction and retrieval has been shown promising results [(Do, H. Rahin, E. (2002), Doan, A-H. Lu, y. Lee, T. Han, J (2003)]. However profiling in healthcare is new and innovative. Staff and patient / doctor profiling and profile matching could be the missing link in providing more tailored healthcare professionals and facility to patients in a hospital environment.

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Profile matching may consist of: •

• •

determining the matching algorithms required for matching patient / doctor profile, determining the availability of staff and facilities required for a given patient. understanding of government policies related patient healthcare,

Another issue related to patient / doctor profiling is defining the level of matching of the patient and doctor profile. There is not always possible to provide the services of the doctor/s and facilities identified as exact match for a patient healthcare because the matching doctor may be unavailable or unreachable. Some guidelines include issues such as the critical nature of the patient illness, its level of sensitivity and regulatory rules. As such the rules that govern patient / doctor profile matching can be expressed in human linguistic terms which can be vague and difficult to represent formally. Fuzzy Logic (Zadeh, L. A., 1965) has been found to be useful in its ability to handle vagueness. As such the profiling patient / doctor matching is based on fuzzy logic. The profiling matching system consists of a fuzzy rule based system uses and inference engine to a weighted value between a patient profile and doctor/s profile. The matching between a patient profile and doctor/s is then divided into the following classes: “total match”, “medium match”, “low match” and “no match”. Based on these class categories a weighted match of patient / doctor profile can be identified. The doctors then can be categorized and ranked based on the matching profile value. The doctors can be classified into classes based on their matching profile as well as their availability such as “highly available”, “more and less available” and “not available”. Of course the data about availability of the doctors are obtained and updated and the profiling agent continuously checks such information from the staff database.

The matched doctors then can be ranked as: “high”, “medium” and “low”. A fuzzy rule the profile matching system then may look like: IF patient_doctor_profile is total match and doctor_availability is highly available Then doctor_ranking = high The integration of RFID capabilities and intelligent agent techniques provides promising development in the areas of performance improvements in RFID data collection, inference and knowledge acquisition and profiling operations. Due to the important role of intelligent agents in this system, it is recognized that there is a need for a framework to coordinate intelligent agents so that they can perform their task efficiently. Intelligent agent coordination [Wooldridge, M. and Jennings, N. (1995). Odell, J. and Bigus, J. P. and Bigus, J. (1998). Shaalana, K. El-Badryb,M. and Rafeac, A. (2004)] has shown to be promising. The Agent Language Mediated Activity Model (ALMA) agent architecture currently under research is based on the mediated activity framework. We believe that such a framework is able to provide RFID with the necessary framework to profile a range of internal and external medical/patient profiling communication activities performed by wireless multi-agents.

rFId descrIptIon RFID or Radio Frequency Identification is a progressive technology that has been said to be easy to use and well suited for collaboration with intelligent software agents. Basically an RFID can: • • • • • •

be read-only; volatile read/write; or write once / read many times RFID are: non-contact; and non-line-of-sight operations. 787

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Being non-contact and non-line-of-sight will make RFIDs able to function under a variety of environmental conditions and while still providing a high level of data integrity [Finkenzeller. K. (1999), Glover. B and Bhatt H. (2006), Hedgepeth W. O. (2007). Lahiri, S. (2005), Schuster E. W., Allen S. J. and Brock D. L. (2007). Shepard S. (2005)]. Next section will discuss the environment that RFIDs operate in and their relationship to other available wireless technologies such as the IEEE 802.11b, IEEE 802.11g, IEEE 802.11n etc… in order to fulfill their requirements effectively and efficiently. RFID or Radio Frequency Identification is a progressive technology that has been said to be easy to use and well suited for collaboration with intelligent software agents. Basically an RFID can be read-only, volatile read/write; or write once / read many times. RFID are non-contact; and non-line-of-sight operations. Being non-contact and non-line-of-sight will make RFIDs able to function under a variety of environmental conditions and while still providing a high level of data integrity [Finkenzeller. K. (1999), Glover. B and Bhatt H. (2006), Hedgepeth W. O. (2007). Lahiri, S. (2005), Schuster E. W., Allen S. J. and Brock D. L. (2007). Shepard S. (2005)]. A basic RFID system consists of four components namely, the RFID tag (sometimes referred to as the transponder), a coiled antenna, a radio frequency transceiver and some type of reader for the data collection.

transponders The reader emits radio waves in ranges of anywhere from 2.54 centimeters to 33 meters. Depending upon the reader’s power output and the radio frequency used and if a booster is added that distance can be increased. When RFID tags (transponders) pass through a specifically created electromagnetic zone, they detect the reader’s activation signal. Transponders can be on-line or off-line and electronically programmed with unique information for a specific application or

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purpose. A reader decodes the data encoded on the tag’s integrated circuit and passes the data to a server for data storage or further processing. There are four major frequency ranges that RFID systems operate at. As a rule of thumb, low-frequency systems are distinguished by short reading ranges, slow read speeds, and lower cost. Higher-frequency RFID systems are used where longer read ranges and fast reading speeds are required, such as for vehicle tracking, automated toll collection, asset management, and tracking of mobile equipment.

Coiled Antenna The coiled antenna is used to emit radio signals to activate the tag and read or write data to it. Antennas are the conduits between the tag and the transceiver that controls the system’s data acquisition and communication. RFID antennas are available in many shapes and sizes. They can be built into a doorframe, book binding, DVD case, mounted on a tollbooth, embedded into a manufactured item such as a shaver or software case (just about anything) so that the receiver tags the data from things passing through its zone [Finkenzeller. K. (1999), Glover. B and Bhatt H. (2006), Hedgepeth W. O. (2007). Lahiri, S. (2005), Schuster E. W., Allen S. J. and Brock D. L. (2007). Shepard S. (2005).]. Often the antenna is packaged with the transceiver and decoder to become a reader. The decoder device can be configured either as a handheld or a fixed-mounted device.

Types of Rfid transponders RFID tags can be categorized as active, semiactive, or passive. Each has and is being used in a variety of inventory management and data collection applications today. The condition of the application, place and use determines the required tag type. Active RFID tags are powered by an internal battery and are typically read / write. Tag data

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Box 1. Frequency ranges for RFID systems Frequency

Range

Applications

Low-frequency

3 feet

Pet and ranch animal identification; car key locks

3 feet

library book identification; clothing identification; smart cards

25 feet

Supply chain tracking: Box, pallet, container, trailer tracking

100 feet

Highway toll collection; vehicle fleet identification

125 - 148 KHz High-frequency 13.56 MHz Ultra-high freq 915 MHz Microwave: 2.45GHz

can be rewritten and / or modified as the need dictates [Finkenzeller. K. (1999), Glover. B and Bhatt H. (2006)]. The semi-active tag comes with a battery. The battery is used to power the tags circuitry and not to communicate with the reader [Shepard S. (2005)]. Passive RFID tags operate without a separate external power source and obtain operating power generated from the reader. Passive tags, since they have no power source embedded in themselves, are consequently much lighter than active tags, less expensive, and offer a virtually unlimited operational lifetime. However, the trade off is that they have shorter read ranges, than active tags, and require a higherpowered reader.

hospital Environment In a hospital environment, in order to manage patient medical data we need both types; fixed and handheld transceivers. Also, transceivers can be assembled in ceilings, walls, or doorframes to collect and disseminate data. Hospitals have become large complex environments. In a hospital nurses and physicians can retrieve the patient’s medical data stored in transponders (RFID tags) before they stand beside a patient’s bed or as they are entering a ward. Given the descriptions of the two types and their potential use in hospital patient data management we suggest that:

•

•

It would be most useful to embed a passive RFID transponder into a patient’s hospital wrist band; It would be most useful to embed a passive RFID transponder into a patient’s medical file;

Doctors should have PDAs equipped with RFID or some type of personal area network device. Either would enable them to retrieve some patient’s information whenever they are near the patient, instead of waiting until the medical data is pushed to them through the hospital server. After examining both ranges for Active and Passive RFID tags, we can suggest the following: •

•

Low frequency range tags are suitable for the patients’ band wrist RFID tags. Since we expect that the patients’ bed will not be too far from a RFID reader. The reader might be fixed over the patient’s bed, in the bed itself, or over the door-frame. The doctor using his/her PDA would be aiming to read the patient’s data directly and within a relatively short distance. High frequency range tags are suitable for the physician’s tag implanted in their PDAs. As physicians move from one location to another in the hospital, data on their patients could be continuously being updated.

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transceivers The transceivers / interrogators can differ quite considerably in complexity, depending upon the type of tags being supported and the application. T\ he overall function of the application is to provide the means of communicating with the tags and facilitating data transfer. Functions performed by the reader may include quite sophisticated signal conditioning, parity error checking and correction. Once the signal from a transponder has been correctly received and decoded, algorithms may be applied to decide whether the signal is a repeat transmission, and may then instruct the transponder to cease transmitting or temporarily cease asking for data from the transponder. This is known as the “Command Response Protocol” and is used to circumvent the problem of reading multiple tags over a short time frame. Using interrogators in this way is sometimes referred to as “Hands Down Polling”. An alternative, more secure, but slower tag polling technique is called “Hands Up Polling.” This involves the transceiver looking for tags with specific identities, and interrogating them in turn. Hospital patient data management deals with sensitive and critical information (patient’s medical data). Hands Down polling techniques in conjunction with multiple transceivers that are multiplexed with each other, form a wireless network. The reason behind this choice is that, we need high speed for transferring medical data from medical equipment to or from the RFID wristband tag to the nearest RFID reader then through a wireless network or a network of RFID transceivers or LANs to the hospital server. From there it is a short distance to be transmitted to the doctor’s PDA, a laptop, or desktop through a WLAN or wired LAN. The “Hand Down Polling” techniques as previously described, provides the ability to detect all detectable RFID tags at once (i.e. in parallel). Preventing any unwanted delay in transmitting medical data corresponding to each RF tagged

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patient. Transponder programmers are the means, by which data is delivered to write once, read many (WORM) and read/write tags. Programming can be carried out off-line or on-line. For some systems re-programming may be carried out on-line, particularly if it is being used as an interactive portable data file within a production environment, for example. Data may need to be recorded during each process. Removing the transponder at the end of each process to read the previous process data, and to program the new data, would naturally increase process time and would detract substantially from the intended flexibility of the application. By combining the functions of a transceiver and a programmer, data may be appended or altered in the transponder as required, without compromising the production line. It can be concluded from this section that RFID systems differ in type, shape, and range; depending on the type of application, the RFID components shall be chosen. Low frequency range tags are suitable for the patients’ band wrist RFID tags. Since we expect that the patients’ bed not to be too far from the RFID reader, which might be fixed on the room ceiling or door-frame. High frequency range tags are suitable for the physician’s PDA tag. As physicians move from on location to another in the hospital, long read ranges are required. On the other hand, transceivers which deal with sensitive and critical information (patient’s medical data) need the Hands Down polling techniques. These multiple transceivers should be multiplexed with each other forming a wireless network.

Applications of the RFID Technology in a hospital The following section describes steps involved in the process of using RFID in hospital environment for patient information management: 1.

A biomedical device equipped with an embedded RFID transceiver and program-

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

mer will detect and measure the biological state of a patient. This medical data can be an ECG, EEG, BP, sugar level, temperature or any other biomedical reading. After the acquisition of the required medical data, the biomedical device will write this data to the RFID transceiver’s EEPROM using the built in RFID programmer. Then the RFID transceiver with its antenna will be used to transmit the stored medical data in the EEPROM to the EEPROM in the patient’s transponder (tag) which is around his/her wrist. The data received will be updated periodically once new fresh readings are available by the biomedical device. Hence, the newly sent data by the RFID transceiver will be updated (and may be accumulated as needed) to the old data in the tag. The purpose of the data stored in the patient’s tag is to make it easy for the doctor to obtain medical information regarding the patient directly via the doctor’s PDA, tablet PC or laptop. 2. Similarly, the biomedical device will also transfer the measured medical data wirelessly to the nearest WLAN access point. Since high data rate transfer rate is crucial in transferring medical data, IEEE 802.11b or g is recommended for the transmission purpose. 3. Then the wirelessly sent data will be routed to the hospitals main server; to be then sent (pushed) to: i. Other doctors available throughout the hospital so they can be notified of any newly received medical data. ii. To an on-line patient monitoring unit or a nurse’s workstation within the hospital. iii. Or the acquired patients’ medical data can be fed into an expert (intelligent agent) software system running on the hospital server and to be then compared with other previously stored abnormal patterns of medical data, and to raise an

4.

5.

6.

7.

alarm if any abnormality is discovered. Another option could be using the in-builtembedded RFID transceiver in the biomedical device to send the acquired medical data wirelessly to the nearest RFID transceiver in the room. Then the data will travel simultaneously in a network of RFID transceivers until reaching the hospital server. If a specific surgeon or physician is needed in a specific hospital department, the medical staff in the monitoring unit (e.g. nurses) can query the hospital server for the nearest available doctor to the patient’s location. In our framework an intelligent agent can perform this task. The hospital server traces all doctors’ locations in the hospital through detecting the presences of their wireless mobile device; e.g. PDA, tablet PC or laptop in the WLAN range. Physicians may also use RFID transceivers built-in the doctor’s wireless mobile device. Once the required physician is located, an alert message will be sent to his\her PDA, tablet PC or laptop indicating the location to be reached immediately including a brief description of the patient’s case. The doctor enters into the patient’s room or ward according to the alert he/she has received. The doctor wants to check the medical status of a certain patient and interrogates the patient’s RFID wrist tag with his RFID transceiver equipped in his\her PDA, tablet PC or laptop, etc.

classIFIcatIon oF data For encryptIon Data security, availability, privacy and integrity [McGraw, G. (2006)] are of paramount important in healthcare and hospital environment. Data security and privacy policies in healthcare are governed by hospital, medical requirements and government regulations. These requirements

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

demand not only for data security but also for data accessibility and integrity. Implementing data security using data encryption solutions remain at the forefront for data security. Data encryption algorithms are implemented to protect the actual data. However data is stored and transferred through several devices and simply protection of data by data encryption fails to secure the resource on which it is stored or transferred. On the other hand the issue of keys and overall process of data encryption process remains complex. The best data encryption solutions are those that balance information protection with on-demand access to that encrypted data for security professionals or other designated individuals within an organization. Since managing the keys for encrypted data can become difficult many large organizations choose to encrypt their regulated data. Once data is transmitted wirelessly, security becomes a more crucial issue. Unlike wired transmission, wirelessly transmitted data can be easily sniffed out leaving the transmitted data vulnerable to many types of attacks. For example, wireless data could be easily eavesdropped on using any mobile device equipped with a wireless card. In worst cases wirelessly transmitted data could be intercepted and then possibly tampered with, or in best cases, the patient’s security and privacy would be compromised. Hence emerges the need for data to be initially encrypted from the source. Two main layers of encryption can be used when using RFID’s in hospital environment, they are, Physical (hardware) layer encryption and Application (software) layer encryption. This means encrypting all collected medical data at the source or hardware level before transmitting it. Thus, we insure that the patient’s medical data would not be compromised once exposed to the outer world on its way to its destination. So even if a person with a malicious intent and also possessing a wireless mobile device steps into the coverage range of the hospitals’ WLAN, this intruder will gain actually nothing since all medical data is

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encrypted, making all intercepted data worthless. In application (software) layer encryption all collected medical data at the destination or application level is encrypted once it is received. Application level encryption runs on the doctor’s wireless mobile device (e.g. PDA, tablet PC or laptop) and on the hospital server. Once the medical data is received, it will be protected by a secret pass-phrase (encryption\decryption key) created by the doctor who possesses this device. This type of encryption would prevent any person from accessing patient’s medical data if the doctor’s wireless mobile device gets lost, or even if a hacker hacks into the hospital server via the Internet, intranet or some other mean. Sensitive and mission critical data are stored in databases, in server applications, and middleware. However many solutions to data encryption at this level are expensive, disruptive, and demanding intensive resource. Using a data classification process organizations can identify and encrypt only the relevant data thereby saving time and processing power. Without data classification organizations using encryption process would simply encrypt everything and consequently impacting users more than necessary (Cline, J., 2007; Butterfield, R., 2007). Understanding the value of the data is significant information for an organization in determining and deploying the proper data classification, security and risk assessment. Data classification is essential and can assist organizations with their data security, privacy and accessibility needs. Such a classification process needs to be able to determine the value, sensitivity, privacy, government regulations and corporate strategic objectives. However data classification is a major difficulty for many organizations as it is an expensive and time consuming task. Classification of and data process may consist of: •

Determining the business and corporate objectives and the level of protection required for data in that organization (e.g. security

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

• •

• •

•

•

measures required, intellectual property protection, strategic use of data, privacy policies), Understanding of government policies for data protection and accessibility, Determining the corporate vales of the data and its sensitivity (private business critical data, internal usage of data, public release of data), Determining who needs access to the data (e.g. user security level), Determining the processes that manipulate the data (internal processes, external applications, mix internal-external applications), Determine the life time of the data and issues related to storage, backups and removal of data (tape, sever, outsourcing), and Determining the level of protection required for the audience that may view the organization’s data.

Another issue related to data classification is defining the level of classification for data. There is no exact and firm rule on the level of classifications. Some guidelines include issues such as the data type, its level of sensitivity and corporate objective and regulatory rules. Such data classification will be different for different organizations based on policies of each organization and government regulatory polices. As such policies are expressed in human understandable language and are vague and difficult to represent formally. The excessive gap between precision of classic logic and imprecision and vagueness in definition of polices creates difficulty in representing this policies in formal logic. Fuzzy Logic [Zadeh, L. A. (1965).] has been found to be useful in its ability to handle vagueness. In this article a data classification method based on fuzzy logic [Zadeh, L. A. (1965)] is presented to determine data classification levels for data in an organization. The level of sensitivity and corporate objective and regulatory rules are determined using this classification method.

Classification levels could divide data into classes such as “top secret”, “secret”, “confidential”, “mission critical”, “not critical”, “private but not top secret”, and “public”. Based on these class categories the business processes and individuals that access and use the data and the level of encryption can be identified. The users can be categorized to determine access to any of these data classes. The users can be classified into classes based on need-to-know such as “very high”, “high”, “medium” and “low” users. The need for encryption level of the data can also be determined to be high, medium, zero (not necessary). To classify data with minimal resources impact and without needing to re-design databases one option is to add extra information to each data item by adding meta-data information to the attributes of each entity in relational-data bases and domains in classes in object-oriented databases. These meta-data information could be the value or degree of security, privacy or other related policies for that data item. This can be demonstrated using a simple relational database as described below. For example consider the following entities of a relational database system: Patient(PatientID, Name, Address, TelNo, InsuranceID) Insurance(InsuranceID, Type, InsuranceProviderID) InsuranceProvider(InsuranceProviderID, Name, Address, TelNo, FaxNo) Doctor(DoctorID, Name, OfficeNo, TelNo, PagerNo) PatientDoctor(PatientDoctorID, PatientID, DoctorID, VisitDate, Notes) Adding meta-data values can then be used for adaptation and implementation of classification of data in databases for an organization. The meta-data values can be can be obtained from the knowledge workers of the organization based on organization policies, procedure and business rules as well as government requirements for

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Figure 2. Sample patient-doctor relational database with and associate fuzzy set

data privacy and security. For example table 1 shows the metadata value related to security of attributes of table Patient based on organization’s security policy and government security and privacy policy. The values are in the range of 0 to 70, where zero indicates the meta-data for a data item that is public and 70 indicates the meta-data for a data item that is top secret (note that other meta data values are also possible and for this application we have chosen between values zero to seventy). Now assume that the following domain meta-data values for these linguistic variable, TP = top secret, SE = “secret”, CO =“confidential”, MC = “mission critical”, NC = “not critical”, PR = “private but not top secret”, PU = “Public”. Assume that the linguistic terms describing the meta-data for the attributes of entities in the above database has are: TP = [58,..,70], SE = [48,..,60], CO =[37,..,50], MC = [28,..,40], NC = [16,..,30], PR = [8,..,20], PU = [0,..,10]. Based on the metadata value for each attribute the membership of that attribute to each data classification can be calculated. In the Figure 3

794

triangular and trapezoidal fuzzy set was used to represent the data security classifications (e.g. Data security classification levels: TP = “top secret”, SE = “secret”, CO =“confidential”, MC = “mission critical”, NC = “not critical”, PR = “private but not top secret”, PU = “Public”). The membership value of PatientID based on its meta-data can be calculated for all these classification using the formulas in Box 2. Where x is metadata value for the attribute PatientID and α1, α2 and α3 are the lower middle and upper bound values of the fuzzy set data security classification. The degree of membership value of the attribute PatientID to fuzzy set security classification based on meta-data from Table 1 can then be calculated as shwon in Figure 3. Now that the data can be classified and categorized into fuzzy sets (with membership value), a process for determining precise actions to be applied must be developed. This task involves writing a rule set that provides an action for any data classification that could possibly exist. The formation of the rule set is comparable to that of an expert system, except that

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

the rules incorporate linguistic variables with which human are comfortable. We write fuzzy rules as antecedent-consequent pairs of If-Then statements. For example: IF Organizational_Security_Classification is TopSecret and Government_Security_ Classification is Confidential Then Level of Encryption is High The overall fuzzy output is derived by applying the “max” operation to the qualified fuzzy outputs each of which is equal to the minimum of the firing strength and the output membership function for each rule. Various schemes have been proposed to choose the final crisp output based on the overall fuzzy output. In this article a type of inference method called centre of gravity and illustrated in Figure 4. Securing medial data seems to be uncomplicated, yet the main danger of compromising such data comes from the people managing it, e.g. doctors, nurses and other medical staff. For

that, it is noted that even though the transmitted medical data is classified and encrypted, doctors have to run application level encryption on their wireless mobile devices in order to protect this important data if the devices gets lost, left behind, robbed, etc. Nevertheless, there is a compromise. Increasing security through using multiple layers, and increasing length of encryption keys decreases the encryption\decryption speed and causes unwanted time delays, whether we were using application or hardware level of encryption. As a result, this could delay medical data sent to doctors or on-line monitoring units.

Future trends RFID in medical environment is an innovative and applicable idea. Linking RFID’s and wireless technologies will provide the required information to achieve timely services to patients as fast as possible. It also will pave the way for future paperless hospitals.

Box 2. Formulas for calculation triangular fuzzy memberships

m A ( x ) = 0 , x < a1

mA ( x ) =

x − a1 , a1 ≤ x ≤ a2 a2 − a1

mA ( x ) =

a3 − x , a2 ≤ x ≤ a3 a3 − a2

m A ( x ) = 0 , x > a3

Formulas for calculation trapezoidal fuzzy memberships

m A ( x ) = 0 , x < a1

mA ( x ) =

x − a1 , a1 ≤ x ≤ a2 a2 − a1

m A ( x ) = 1 , a2 ≤ x ≤ a3 mA ( x ) =

a4 − x , a3 ≤ x ≤ a4 a4 − a3

m A ( x ) = 0 , x > a4

Table 1. Metadata values for table customer Patient Table

Meta-data Value base on organization

Meta-data Value base on government

policy

regulatory policy

PatientID

68

39

Name

64

70

Address

30

60

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Figure 3. Fuzzy membership of metadata value of PatientID based on μ(PatientID)

TP

SE

CO

MC

NC

PR

PU

0.8

0

0

0

0

0

0

(a) Organization policy

μ(PatientID)

TP

SE

CO

MC

NC

PR

PU

0

0

0.3

0.16

0

0

0

(b) government regulatory policy

RFID technology has many potential important applications in hospitals. With the progress the RFID technology is currently gaining, it seems to become a standard as other wireless technologies, and eventually manufacturers building them in electronic devices; biomedical devices with reduced cost.

CONCLUSION Managing patients’ data wirelessly can prevent errors, enforce standards, make staff more efficient, simplify record keeping and improve patient care. This research in the wireless medical environment introduces new ideas in conjunction to what is already available in the RFID technology and wireless networks. Linking both technologies

Figure 4. Center of gravity inference method A 1

A 2

B 1

X

Y

A 3

w 1

A 4

Z B 2

w 2 X x

y

Y

Z

intersection

M ax

orm in

z (Centroidforaea)

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

with each other to achieve the research main goal, delivering patients medical data as fast and secure as possible, to pave the way for future paperless hospitals. With the reduction in cost of radio frequency identification (RFID) technology, it is expected the increased use of RFID technology in healthcare in monitoring patients and assisting in health care administration. A fuzzy logic based for profile matching and data classification system is also developed to improve the application of regulatory data requirements for security and privacy of data exchange. Finally it should be noted that the world of RFIDs could be frustrating. Often potential users have to wade through product information to select the right RFID technology for their needs. The terminology, concepts, and uses seem to be vendor driver rather than potential user driver. The RFID vendor websites often describe how and where to use RFIDs using arcane and unfamiliar language.

Acknowledgment The author would like to acknowledge the initial research work performed on this project at University of Canberra by MIT students.

References Angeles, R. (2007), An empirical study of the anticipated consumer response to RFID product item tagging, Industrial Management & Data Systems, Vol. 107 No. 4, pp. 461-583. Bhuptani, M., & Moradpour, S. (2005). RFID Field Guide - Deploying Radio Frequency Identification Systems. NJ: Prentice Hall.

Bigus, J. P. and Bigus, J. (1998). Constructing Intelligent software agents with Java – A Programmers Guide to Smarter Applications, Wiley, ISBN: 0-471-19135-3. Butterfield, R. (2007). “Data classification: A prerequisite to ILM”, http://www.snwonline.com/ implement/data_class_05-30-05.asp Cline, J. (2007). “Growing pressure for data classification”, http://www.computerworld.com/action/ article.do?articleId=9014071&command=viewA rticleBasic, Last accessed on 22/11/2007 Do, H. Rahin, E. (2002), “Coma: A System for Flexible Combination of Schema Matching Approaches”, Proceedings of 28th Conference on Very Large Databases, Morgan Kaufmann, pp 610-621USA. Doan, A-H. Lu, y. Lee, T. Han, J (2003), “ProfileBased Object Matching for Information Integration”, IEEE Intelligent Systems Magazine, pp 54-59. USA. Finkenzeller. K. (1999), RFID Handbook, John Wiley and Sons Ltd, USA. Fuhrer, P. and Guinard, D. (2007). Building a Smart Hospital using RFID technologies: Use Cases and Implementation. rfidehealth.pdf Glover. B and Bhatt H. (2006), RFID Essentials, O’Reilly Media, Inc.USA, (ISBN: 100596009445) Gruber, T.R. (1993) A translation approach to portable ontology specifications. Knowledge Acquisition, 5:199-220. Hedgepeth W. O. (2007). RFID metrics: decision making tools for today’s supply chains.Boca Raton, FL : CRC Press, (ISBN: 9780849379796) Kowalke, M. (2006) TMCnet. Wireless Mobility Blog. (2006). RFID vs. WiFi for Hospital Inventory Tracking Systems. http://blog.tmcnet.com/ wireless-mobility/rfid-vs-wifi-for-hospital-inventory-tracking-systems.asp, October 23, 2006.

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Lahiri, S. (2005), RFID Sourcebook, IBM Press, USA, (ISBN: 10-0131851373 ) Last accessed on 22/11/2007 Lewan, T. (2007). ZDNet Australia. Human RFID chipping invading the US?. 23 July 2007. McGraw, G. (2006), “Software Security”, Addison-Wesley, USA. Messmer, E. (2006). Network World. Hospital finds a bloody good RFID application. http://www.computerworld.com.au/index.php/ id;2117167769;fp;4;fpid;18. Mickey, K. (2004), RFID grew in 2002. Traffic World 5: 20-21. Odell, J. (Ed.), (2000). Agent Technology, OMG Document 00-09-01, OMG Agents interest Group, September 2000 Pramatari, K.C., Doukidis, G.I. and Kourouthanassis, P. (2005), “Towards ‘smarter’ supply and demand-chain collaboration practices enabled by RFID technology”, in Vervest, P., Van Heck, E., Preiss, K. and Pau, L.F. (Eds), Smart Business Networks, Springer, Germany, pp. 197-210. Qiu R, Sangwan R. (2005), Toward collaborative supply chains using RFID. CIO Wisdom II, more best practices. New Jersey: Prentice-Hall; pp. 127–44.

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Schuster E. W., Allen S. J. and Brock D. L. (2007) Global RFID : the value of the EPCglobal network for supply chain management. Berlin ; New York : Springer, (ISBN: 9783540356547). Shaalana, K. El-Badryb,M. and Rafeac, A. (2004) , “A multiagent approach for diagnostic expert systems via the internet”, Expert Systems with Applications, Volume No. 27 Issue No. 1, Elsevier Publishing, USA pp1-10. Shepard S. (2005). RFID : radio frequency identification, New York : McGraw-Hill, (ISBN: 0071442995). Tindal, S. (2008). ZDNet Australia.. One in ten RFID projects tag humans. http://www.zdnet.com.au/ news/hardware/soa/RFID-people-tagging-benefits-health-sector-/0,130061702,339285273,00. htm. 21 January 2008. Weinstein, R. (2005). RFID: A Technical Overview and Its Application to the Enterprise. IT Professional Magazine, 7(3), 27-33.Whiting, R. (2004), MIT = RFID + Rx. Information Week 988: 16. Wooldridge, M. and Jennings, N. (1995). Intelligent software agents: Theory and Practice. The Knowledge Engineering Review. vol. 10, no 2, pp 115-152, USA. Zadeh, L. A. (1965). “Fuzzy sets”, Information and control, Vol. 8. pp 338-352.

Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

Appendix Fuzzy Logic Data Classification Knowledgebase: IF Organizational_Classification is Top Secret and Government_Classification is Top Secret Then Level of Encryption isl High IF Organizational_Classification is Top Secret and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Top Secret and Government_Classification is Confidential Then Level of Encryption is High IF Organizational_Classification is Top Secret and Government_Classification is Mission Critical Then Level of Encryption is High IF Organizational_Classification is Top Secret and Government_Classification is Not Critical Then Level of Encryption is High IF Organizational_Classification is Top Secret and Government_Classification is Private but not Top Secret Then Level of Encryption is High IF Organizational_Classification is Top Secret and Government_Classification is Public Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Top Secret Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Confidential Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Mission Critical Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Not Critical Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Private but not Top Secret Then Level of Encryption is High IF Organizational_Classification is Secret and Government_Classification is Public Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Top Secret Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Confidential Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Mission Critical Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Not Critical Then Level of Encryption is High

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

IF Organizational_Classification is Mission Critical and Government_Classification is Private but not Top Secret Then Level of Encryption is High IF Organizational_Classification is Mission Critical and Government_Classification is Public Then Level of Encryption is High IF Organizational_Classification is Not Critical and Government_Classification is Top Secret Then Level of Encryption is High IF Organizational_Classification is Not Critical and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Not Critical and Government_Classification is Confidential Then Level of Encryption is High IF Organizational_Classification is Not Critical and Government_Classification is Mission Critical Then Level of Encryption is High IF Organizational_Classification is Not Critical and Government_Classification is Not Critical Then Level of Encryption is Medium IF Organizational_Classification is Not Critical and Government_Classification is Private but not Top Secret Then Level of Encryption is Medium IF Organizational_Classification is Not Critical and Government_Classification is Public Then Level of Encryption is Zero IF Organizational_Classification is Private but not top secret and Government_Classification is Top Secret Then Level of Encryption is High IF Organizational_Classification is Private but not top secret and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Private but not top secret and Government_Classification is Confidential Then Level of Encryption is Medium IF Organizational_Classification is Private but not top secret and Government_Classification is Mission Critical Then Level of Encryption is Medium IF Organizational_Classification is Private but not top secret and Government_Classification is Not Critical Then Level of Encryption is Medium IF Organizational_Classification is Private but not top secret and Government_Classification is Private but not Top Secret Then Level of Encryption is Medium IF Organizational_Classification is Private but not top secret and Government_Classification is Public Then Level of Encryption is Medium IF Organizational_Classification is Public and Government_Classification is Top Secret Then Level of Encryption is High IF Organizational_Classification is Public and Government_Classification is Secret Then Level of Encryption is High IF Organizational_Classification is Public and Government_Classification is Confidential Then Level of Encryption is Medium IF Organizational_Classification is Public and Government_Classification is Mission Critical Then Level of Encryption is Medium IF Organizational_Classification is Public and Government_Classification is Not Critical Then Level of Encryption is Zero

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Intelligent Agent Framework for Secure Patient-Doctor Profiling and Profile Matching

IF Organizational_Classification is Public and Government_Classification is Private but not Top Secret Then Level of Encryption is Medium IF Organizational_Classification is Public and Government_Classification is Public Then Level of Encryption is Zero

This work was previously published in International Journal of Healthcare Information Systems and Informatics, Vol. 3, Issue 3, edited by J. Tan, pp. 38-57, copyright 2008 by IGI Publishing (an imprint of IGI Global).

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Chapter 4.3

Using RFID to Track and Trace High Value Products: The Case of City Healthcare Judith A. Symonds Auckland University of Technology, New Zealand David Parry Auckland University of Technology, New Zealand

executIve suMMary

oRGANIZATIoN BACKGRoUND

Certain businesses call for a high level of traceability to track high value products. This case study of City Healthcare,1 New Zealand, focuses on the complex management issues related to the initial decisions to use radio frequency identification (RFID) technology on such a product, instead of a barcode. RFID devices are effectively tiny memory storage devices that can be read and sometimes written to from a distance using radio waves through an appropriate interrogation device. RFID devices have been touted as a replacement for barcodes in supply-chain applications. Issues and challenges investigated here include the ability of RFID to replace barcodes, business benefit from technology investment, technology adoption, and the role of external regulations in the adoption process.

City Healthcare is a designer and manufacturer of healthcare devices. The factory is the only production site for the organisation and overseas offices are supported from here. Sales offices are located in Australia, U.S., UK, France, Germany, and India. All City Healthcare manufacturing line products are uniquely identifiable using barcodes. RFID (radio frequency identification) was not used anywhere in the factory at the time of the case study analysis although management was aware that RFID tags could be used in place of barcodes. City Healthcare entered the respiratory care market in 1971 with the development of a respiratory humidifier system for use in critical care. It has since developed humidification technologies and now offers products for use in intensive care respiratory medicine, neonatal care,

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Using RFID to Track and Trace High Value Products

operating rooms, and the treatment of obstructive sleep apnoea (OSA). City Healthcare is the tenth biggest company on the New Zealand stock exchange and has 830 staff in New Zealand. The company sells products to 90 markets in Europe, North America, UK, Australia, and Asia, achieving sales of NZ $241 million annually. City Healthcare spends around 7% of its revenue on R&D and consistently produces new lines and products. The company continually enhances its existing products and develops new, related products, focuses on new medical applications for their technologies and expands their sales network, with the focus on achieving a better patient outcome.

settIng the stage As a much cheaper alternative and a requirement by many healthcare governing bodies, barcoding still has precedence amongst healthcare companies over other technologies for identifying products (Best, 2005). RFID may be seen as a replacement for barcodes but manufacturers of medical devices have a lot to consider when adopting RFID technology. Issues include standards, cheaper alternatives, and regulations. For example, in the U.S., medical device manufacturers must get third party approval from regulatory bodies such as the Food and Drug Administration2 (FDA) to allow them to sell their products. FDA requirements mainly include safety, quality, and standardisation. Having mastered barcoding technology, companies are in a position to consider the functionality of RFID. In healthcare particularly, RFID is considered more suitable for locating people and products than barcoding and has many potential advantages such as field reading, as opposed to line-of-sight reading. RFID devices can store more data than barcodes and some RFID tags can have data written to them by the interrogator.

There has been a great deal of interest recently in the use of RFID in the supply chain (Singh & Lai, 2007), and a number of major projects are underway.

readiness for RFID in healthcare Medical healthcare devices are often high value products manufactured in low volumes with supporting processes that must comply with regulations. There could be catastrophic consequences for a healthcare device manufacturer if a product was involved in a serious accident or other bad publicity. Therefore, regulatory bodies require medical device manufacturers to individually label every product manufactured. Unique identification makes it possible to achieve full traceability and archive test data records in the supply chain. All these factors can affect RFID adoption in the industry and initially suggest that medical device manufacturers have the margins and pressure from external actors to motivate them to invest in the technology (Brooke, 2005). Brooke (2005) identified several aspects of the healthcare industry where RFID can be beneficial, including the ability to trace high value assets in the hospital and the ability to track assets over time, thus verifying that certain procedures have been completed (in this case, decontamination of surgical instruments).

Tracking and Tracing The supply chain is described by Christopher (2005) as a network (supply chain network) where different actors and functions are working together to control, manage, and improve the flow of material and information from suppliers to end customers. The underlying philosophy behind the supply chain is the logistic concept of planning and co-ordinating the flow of material through a supply chain as a series of dependent activities within functions, with the overall aim of sending the right product, to the right destination, at the right time. 803

Using RFID to Track and Trace High Value Products

Business has long recognised that the key to success of the supply chain is the use of information technology. Information technology has in many ways transformed the way different actors in the supply chain can connect with each other. Information has always been central to increasing supply chain efficiency. Now, enabled by information technologies, the complex flow of materials, parts, subassemblies, and finished products can be well co-ordinated and monitored (Christopher, 2005). The ability to track and trace the flow of material across the supply chain network is enabled through the use of information systems (Walker, 2005). Tracking means following products as they move through the supply chain, stage by stage. Tracing means getting information about a specific product and is mostly done at the end of the supply chain. An example of a traceability problem is the need to find out which actor has been involved in the manufacturing of a product. The information in this case is traced backwards (upstream). The central issue in tracking and tracing is collecting information about products both internally and when the material crosses organisational borders. Figure 1 shows tracking and tracing capabilities possible in both directions. Achieving end-to-end tracking and tracing through the supply chain network is enabled through information technologies. The barcode has been used to enable end-to-end tracking but

is not without problems. For example, when a manufacturer wishes to store several pieces of information about a product in its supply chain, the size restrictions of barcode become limiting. Newer technologies are not as limiting, as discussed by Walker (2005). For example, RFID has the potential to store several pieces of information such as store lot code, date code, serial number, and expiration date. A richer information flow gives more real time information about the products. The integration of functions becomes more possible when the end-to-end view makes it possible for any actor to see individual product movement (Heinrich, 2005).

Identification and Data Collection Auto identification and data collection (AIDC) includes several identification systems using barcodes, magnetic strips, or RFID technology (RFID Journal, 2005). An RFID system uses RFID tags and RFID readers to collect data. The RFID system can be compared to the barcode system using the scanner and the barcode label. Instead of the barcode label, the RFID tag is attached to an object and the scanner is the RFID reader. Barcode systems use infrared to communicate, but RFID systems use radio frequency for data transmission. The data flow necessary for an RFID reader to access the data on an RFID tag is shown in Figure 2.

Figure 1. Track products and trace information about products Track downstream

Supplier

Purchasing

Manufacturing

Trace upstream 804

Distribution

Customer

Using RFID to Track and Trace High Value Products

Figure 2. Data flow of RFID reading (Wang & Liu, 2005) Control commands Application

Control

Received Data

tag

Transmitted Data

HF Interface Antenna

Table 1. Benefit analysis (Source: Garfinkel & Rosenberg, 2005; Heinrich, 2005; Bose & Pal, 2005; Sarma, 2004) Area

Benefit

The RFID tag

- small size - uniquely identifiable - memory capacity - reading range - write capability

Non line-of-sight

- penetrate material - independent of tag orientation - read multiple tags - process improvements (speed up)

Better information

- more information (frequent reading) - accurate information - end-to-end view (track & trace)

The advantages of RFID technology are summarised in Table 1. In a quantitative performance evaluation of printing supply chains, Hou and Huang (2006) measured the time required for item identification using barcode and RFID technology and found that to identify 1000 items by barcode took 33 minutes compared with 1 minute and 40 seconds using RFID. They translate this time saving into an increased operational efficiency for each stage of the supply chain and provide lookup tables to allow managers to forecast cost savings from the implementation of RFID. When compared to the barcode, vastly improved performance in item identification is the most attractive benefit of

RFID. Lee, Cheng, & Leung (2004) quantify the indirect benefits of RFID use within supply chains using a simulation model. They identify inventory accuracy, more efficient shelf replenishment, and inventory visibility, resulting in an improved supply chain fill rate.

RFID Compared to the Barcode RFID is often said to be an alternative to today’s product barcodes. It may seem that the advantages of using RFID are already accomplished by barcodes, but barcodes have limited capabilities (Sarma, 2004). Barcodes cannot identify individual product items; they only identify a

805

Using RFID to Track and Trace High Value Products

product group. Also, although barcode reading is much faster than manual reading, it still requires human intervention and reads only one at a time. RFID can make operations more automatic and can attach uniquely updateable information to the product that the information refers to. As a result, each RFID tag will be read many more times during its lifetime compared to a barcode, which is used mainly only at check out. Barcodes get much of the blame for the limitations in current business processes by Lucket (2004), who implies that business processes today are optimised as far as the barcode will allow. Table 2 summarises RFID disadvantages and barcode advantages. Lai & Hutchinson (2005) discuss some of the disadvantages of RFID in their study of RFID adoption in China. The largest challenge for companies contemplating RFID implementation is that of standards because it is not clear which will be chosen as an international standard. Markets that have no intellectual property rights to standards are required to pay a high price per unit for standards patents. This has lead to nations like China choosing neither UID (unique ID) nor EPC (electronic product code) and developing their own RFID standards. Another disadvantage of RFID technology according to Lai & Hutchinson (2005) is the prohibitively high cost of tags which is still as high as U.S. $0.30 per tag compared to the cost of barcodes at about U.S. $0.0024 per label. Another challenge for Chinese companies according to Lai and Hutchinson (2005) is that in order to effectively use the information collected by RFID it must be connected to backend systems

such as ERP (enterprise resource planning), CRM (customer relationship management), and DSS (decision support systems). This requires the presence of such systems and the ability of management to use such systems. However, as reported by Jones, Clarke-Hill, Shears, Comfort, and Hillier (2004), many companies have difficulty interrogating consumer buyer patterns and consumer reward databases. A final challenge according to Lai and Hutchinson (2005) is that of the security and privacy of systems. RFID has security problems of its own, which may serve to aggravate information systems that are not adequately secure. As discussed by Ohkubo, Suzuki, and Kinoshita (2005), if items are identified by a unique identification number, then a person’s physical movements can be tracked over a period of time. Tags can be ‘killed’ after one use, but this negates the advantages of using RFID tags. Ohkubo et al. (2005) suggest that the likely solution is encryption of the unique tag ID. As highlighted by Jones et al. (2004), RFID tag read ranges are currently limited to within a short range of the reader, and therefore, to achieve any accurate surveillance there would have to be sensors placed everywhere. However, the technology is capable of much longer read ranges and these are likely to be implemented in the future.

case descrIptIon The scope of the case study was limited to the range of devices that provide continuous positive airway pressure (CPAP) as well as a range of in-

Table 2. Disadvantages of RFID and advantages of barcode system (Source: Piasecki, 2005; Heinrich, 2005; Lucket, 2004; iStart, 2005; Staake, Thiesse, & Fleisch, 2005; Gunther & Spiekermann, 2005)

806

Disadvantages of RFID

Advantages of Barcode

ROI (return on investment) uncertainty Lack of universal standards High cost of individual tags High failure rate in reading in some environments Obstructive materials interfere with reading Privacy issues with info stored on tags

Mature technology and therefore less risky to implement Low cost per product application

Using RFID to Track and Trace High Value Products

terface solutions, for the treatment of obstructive sleep apnoea (OSA), offered by City Healthcare. All applications in the series are portable and designed to be used by the patient in the home. Refer to Appendix A for an overview of the management structure of City Healthcare. However, a general overview of the IT infrastructure at City Healthcare is provided first.

IT Infrastructure City Healthcare has recently moved to a purposebuilt factory premises and has installed a 10 Gbps ethernet telecommunications backbone. The main end-users are PC (personal computer) based. The telecommunications network bandwidth allows for large CAD (computer aided design) file transfers. In the future, City Healthcare intends to integrate a voice over IP (Internet protocol) solution with the existing network infrastructure.

The Internal Supply Chain within the Factory The internal supply chain for the CPAP consists of a warehouse of raw material, a manufacturing line (including four subassembly lines, a testing line, and a packaging stage), and a warehouse of finished goods (Appendix B). Before entering the testing line, the subassembly lines are integrated and the different components are all assembled into a fully assembled CPAP. During the transformation stages in the testing line, test runs are performed on a whole unit rather than checking each component, as in the subassembly lines. In total, five test runs are recorded during the manufacturing line, one within the PCB subassembly line and four in the testing line (Appendix C). The test records from these test runs are stored on a runcard (five test runs in total, but four related to the testing line). If the unit fails any of the tests, it must be sent back to the beginning of the manufacturing line for rework. The operator is not allowed to retest

the unit; therefore, the testing process must be accurate. After the testing line, the CPAP as well as the other features such as the breathing tube, coming from the tube line, are all packaged together into a box in the packaging line. These boxes are loaded on pallets for delivery. The pallets are picked up by staff from the finished goods warehouse, the final function within the factory.

Runcard and Barcodes At the beginning of the testing line, a piece of paper is attached to each individual CPAP and this is known as a runcard (Appendix D). The runcard is a checklist which the operator signs after each test run (Appendix E). For each test, the date that the test was run, the signature of the operator, and a pass or fail result are recorded. The runcard is the quality record that complies with regulations for accurate auditing and traceability and assures the customer that City Healthcare products have been tested for quality control. The runcard is attached to the CPAP throughout the whole testing line until the CPAP is loaded into the box and loaded on a pallet. In the packaging stage the runcard and the CPAP are separated. The runcard stays at the factory and is stored at the manufacturing line for 10 years. The runcard must be able to be produced at a later date as evidence verifying that a particular device passed all quality controls. A unique barcode attached to the runcard and the CPAP is used to match test results once the device has left the manufacturing line. Appendix Fshows how the information about the product in the field is traced back to the runcard stored in the manufacturing line. Runcards must be kept for the duration of the life of the product and therefore are stored indefinitely. The cards are collected together and archived off-site after the initial 10 year period. Runcards are not retrieved often, but when they are, there can be difficulty in locating the specific runcard in a timely manner. Tracing starts at the customer end and goes

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back to the manufacturing line in order to find the runcard for that specific product. Additional test data measurements from each test run are stored in each test machine’s own information system. The test machines will produce a record for each individual test and store all the test data measurements taken during the test. The information from the tests is stored in a flat file in a file directory and is identified by the name of the file. The four information systems for the test machines in the testing line are not linked (see Appendix C). That is, when a unit enters a test machine, the machine has no knowledge of whether previous tests have been passed. This is up to the operator who will check the runcard attached to the unit. Three major requirements override everything else in the manufacturing line within City Healthcare, to manufacture a product of quality, with processes that comply with regulations (safety and efficiency issues) and with full traceability. Obstructive sleep apnoea is a potentially lifethreatening disorder. It is important that products from City Healthcare are of a high quality so the product will work properly in any circumstance. When it comes to healthcare products, one incident related to safety for a medical product has the potential to damage the brand when the risk endangers a person’s life. A certain level of quality for medical products is established by following criteria set by regulatory bodies. Different markets are controlled by several regulatory bodies. The U.S. market is important to City Healthcare. The regulatory body in the United States is the Food and Drug Administration (FDA). Medical device manufacturers have to be approved by the FDA in order to be allowed to sell medical products on the U.S. market. Regulations demand that certain quality controls are conducted. The regulations affect the processes in the manufacturing line within City Healthcare. When asked about his work, the production manager mentions, among other things, making sure the

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activities within the manufacturing line and throughout the supply chain comply with regulations. One major concern he points out is the traceability requirement, he says: Basically we (City Healthcare) have to document a lot of data that other companies might not have to do, like full traceability of our products. From the raw materials right through the assembly line (manufacturing line), to stores, out to the customers. As for any medical device manufacturer, City Healthcare must fulfil the demands of regulatory bodies concerning traceability and archive test data records for their products. The regulations also set criteria for test machine design, validation processes, and for how complaints are investigated. The FDA requirements for electronic records are summarised by Mercuri (2003) as: • • • •

•

use validated equipment and computer systems secure retention of records for instant analysis reconstruction user-independent, computer generated, timestamped audit trails system and data security, data integrity, and confidentiality through authorized system access use of secure electronic signatures for open and closed systems and digital signatures for open systems.

current ChAllENGES/PRoBlEMS FacIng the organIsatIon The manufacturing line stores test data from each test run in two places, on the runcard and in separate information systems (see Appendix C). Test data needs to be stored in both places because digital storage does not comply with regulations

Using RFID to Track and Trace High Value Products

while the runcard does. At City Healthcare, there are two motivations to change the runcard system; increased efficiency and integration of test data storage. The production manager can see the potential time saved with every unit tested:

could improve the manufacturing line, he said:

If we can find ways of doing things faster, I guess ultimately, if we could get rid of that run card… well that’s another 3 or 4 seconds that we save on every single unit, now, on every test… runs faster.

However, there are a number of issues that would need to be addressed in an RFID enabled system. These are: compliance with regulations, information requirements met by the current system, the need for a reliable audit trail, usability for customers, difficulties with digital authentication, and usability of computerised systems within the factory. In particular, it would be essential for the unique ID on the RFID to be secure, as it is important that information cannot be modified by any actor in the supply chain. The signature on the runcard is also an important aspect of the quality system and if the runcard became electronic, biometric authentication may be needed as the security of password and swipe card systems can be breached simply by the sharing of codes or cards between operators. Ultimately, innovation at City Healthcare is driven by customer demand (distributors and retailers of healthcare products). Customers are not looking for RFID specifically, but they are looking for increased stock visibility. City Healthcare has come to a standstill in their investigation of RFID and its potential benefits for their business. It is obvious that they need to take stock of the issues and develop a plan for a way forward. But which way next? The most obvious alternative to introducing RFID is changing the use of the current information system to enable the run card data to be stored there. However, this is more risky from a data integrity point of view, as the data will only be accessible via City Healthcare’s internal information system. The possibility of there being more than one electronic version of the runcard is especially serious for the FDA regulations. The data needs to be accessible by appropriate par-

Barcode technology currently enables the managers in City Healthcare to record the information they need to be able to trace the serial number back to the manufacturing line, to be able to find the runcard for that specific product. The barcodes seem to fulfil the purpose of traceability requirements. There are two main reasons for City Healthcare to retain the current system; proven reliability and the convenience of the runcard method. However, there are three vulnerabilities in the current runcard system using barcode technology; reading problems caused by poor reader alignment or printing problems, the lack of integration of the test information systems, and the reliance on the operator to fulfil their obligations. For example, it is against quality regulations to test a unit twice. If a unit fails, it must be sent back to service for rework. Compliance with such regulations is checked by internal quality officers and external auditors. Each testing machine has two control lights; a green (pass) and a red light (fail). The operator checks the light and fills in the runcard as appropriate. There is nothing to say an operator will make the right call every time, everyday, when no electronic check is done. However, as the unit passes each test, all the operators will check the runcard to verify that the unit has passed the previous test. RFID technology has the potential to enable automation of the runcard process. When the researcher asked the production manager if RFID

I’d say yes, I would say the big one would be replacing or getting rid of the paper run cards, that would be the biggest one.

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ties such as the FDA for the life of the machine, and even beyond the lifetime of the company. An RFID-based system could slot into the current arrangement, without major changes to the information system, and supports the possibility of identifying components and assemblies without having to take the device apart. RFID technology is relatively cheap to deploy and the use of such an approach may avoid the necessity of redesigning the whole information system.

reFerences Andrews, J. (2005). The debate: RFID versus barcoding. Retrieved January 17, 2006, from www.healthcareitnews.com Best, J. (2005). RFID spending to rocket in healthcare. Retrieved January 17, 2006, from http://networks.silicon.com Bose, I., & Pal, R. (2005). Auto-ID: Managing anything, anywhere, anytime in the supply chain. Communications of the ACM, 48(8), 100-106. Brooke, M. (2005). RFID in healthcare: a fourdimensional supply chain. Quality Digest, August. Retrieved March 3, 2006, from www.avatarpartners.com/QualityDigest_ RFIDinHealthcare.pdf Christopher, M. (2005). Logistics and supply chain management. London: Prentice Hall. Garfinkel, S., & Rosenberg, B. (2005). RFID applications, security, and privacy. New York: Addison-Wesley. Gunther, O., & Spiekermann, S. (2005). RFID and the perception of control: The consumer’s view. Communications of the ACM, 48(9), 73-76. Heinrich, C. (2005). RFID and beyond. Indianapolis: Wiley.

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Hou, J. L., & Huang, C. H. (2006). Quantitative performance evaluation of RFID applications in the supply chain of the printing industry. Industrial Management & Data Systems, 106(1), 96-120. Jones, P., Clarke-Hill, C., Shears, P., Comfort, D., & Hillier, D. (2004). Radio frequency identification in the UK: Opportunities and challenges. International Journal of Retail & Distribution Management, 32(3), 164-171. Lai, F., & Hutchinson, J. (2005). Radio frequency identification (RFID) in China: Opportunities and challenges. International Journal of Retail and Distribution Management, 33(12), 905-916. Lee, Y. M., Cheng, F., & Leung, Y. T. (2004). Exploring the impact of RFID on supply chain dynamics. In R. G. Ingalls, M. D. Rossetti, J.S. Smith, & B. A. Peters (Eds.), Proceedings of the 2004 Winter Simulation Conference. Retrieved from informs-sim.org. Lucket, D. (2004). Supply chain. BT Technology Journal, 22(3), 50-55. Mercuri, R. T. (2003). On auditing audit trails. Communications of the ACM, 46(1), 17-20. Ohkubo, M., Suzuki, K., & Kinoshita, S. (2005). RFID privacy issues and technical challenges. Communications of the ACM, 48(9), 66-71. Piasecki, D. (2005). The basics, the Wal-Mart mandate, EPC, privacy concerns, and more. Retrieved on December 19, 2005, from www. inventoryops.com/RFIDupdate.htm RFID Journal (2005). What is RFID? Retrieved on December 9, 2005, from www.rfidjournal.com Sarma, S. (2004). Integrating RFID. Queue, 2(7), 50-57. Singh, N., & Lai, K. (2007). Intra-organizational perspectives on IT-enabled supply chains. Communications of the ACM, 50(1), 59-65.

Using RFID to Track and Trace High Value Products

Staake, T., Thiesse, F., & Fleisch, E. (2005) Extending the EPC network—the potential of RFID in anti-counterfeiting. ACM Symposium on Applied Computing, 1607-1612. Walker, W. (2005). Supply chain architecture: a blueprint for networking the flow of material, information, and cash. London: CRC Press. Wang, F., & Liu, P. (2005). Temporal management of RFID data. In Proceedings of the 31st Very Large Databases (VLDB) Conference (pp. 11281139). Trondheim, Norway

endnotes 1 2

Not the real name. In April 2005 the Food and Drug Administration (FDA) set regulations that require every medical device produced to have a bar code (Andrews, 2005). However, to date, no such regulations have been put in place to support RFID technology (FDA, 2006).

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appendIx a. ManageMent structure oF cIty healthcare Cheif of IT System Application Specialist (Supports the Manufacturing Knowledge system)

Software Development Manager

Product Development Engineer

Product Departments Other Departments

Obstructive Sleep Apnea Department Continuous Positive Airway Pressure (CPAP) Team

Software Team

Blower Team

appendIx b. physIcal Flow oF MaterIal to a cpap wIthIn the FACToRY (SoURCE: AUThoR) Manufacturing line PCB

Blower Warehouse Raw materials

Testing line Case part

Heater

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Packaging line

Warehouse Finished goods

Using RFID to Track and Trace High Value Products

appendIx c. ManuFacturIng lIne Packaging

Testing line

Subass Warehouse lines finished goods PCB

Blower

Test 2

Test 3

Test 4

Test 5

Packaging

Case part

Warehouse Finished Goods

Runcard 1. .. 2. .. . . 5. .

Heater

Information system 2

Information system 3

Information system 4

Information system 5

* Note that Test 1 occurs during the PCB subassembly process and is not part of the testing line.

appendIx d. the runcard Series Information (select one) PRODUCTION ROUTINE CARD TESTING CHECKLIST Test 1 Test 2 Test 3 Test 4 Test 5

Component 1

PASS/FAIL

Component 2

PASS/FAIL

Component 1

PASS/FAIL

Component 2

PASS/FAIL

Component 1

PASS/FAIL

Component 2

PASS/FAIL

Component 1

PASS/FAIL

Component 2

PASS/FAIL

Component 1

PASS/FAIL

Component 2

PASS/FAIL

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appendIx e. the runcard Is sIgned along the testIng lIne Test 2

Test 3

Test 4

Test 5

Runcard 1. .. 2. .. . Packaging . 5. .

APPENDIx F. TRACEABIlITY FRoM CUSToMER BACK To ManuFacturIng (traceabIlIty upstreaM) Factory Supplier

Warehouse of raw

Manufacturing

Warehouse of finished goods

Sales office

Customer

This work was previously published in Journal of Cases on Information Technology, Vol. 10, Issue 1, edited by M. KhosrowPour , pp. 1-13, copyright 2008 by IGI Publishing (an imprint of IGI Global).

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Chapter 4.4

Implementing RFID Technology in Hospital Environments Marlyn Kemper Littman Nova Southeastern University, USA

IntroductIon A promising approach for facilitating cost containment and reducing the need for complex manual processes in the healthcare space, RFID (Radio Frequency Identification) technology enables data transport via radio waves to support the automatic detection, monitoring, and electronic tracking of objects ranging from physicians, nurses, patients, and clinical staff to walkers, wheelchairs, syringes, heart valves, laboratory samples, stents, intravenous pumps, catheters, test tubes, and surgical instruments (Karthikeyan & Nesterenko, 2005). RFID implementations streamline hospital applications and work in concert with WLANs (wireless local area networks) and mobile devices such as cellular phones and personal digital assistants (PDAs). RFID technology also safeguards the integrity of the drug supply by automatically tracing the movement of medications from the manufacturer to the hospital patient.

This article begins with a discussion of RFID development and RFID technical fundamentals. In the sections that follow, the work of standards organizations in the RFID space is introduced, and capabilities of RFID solutions in reducing costs and improving the quality of healthcare are described. Descriptions of RFID initiatives and security and privacy challenges associated with RFID initiatives, are explored. Finally, trends in the use of RFID-augmented wireless sensor networks (WSNs) in the healthcare sector are introduced.

BACKGRoUND RFID technology traces its origins to 1891 when Guglielmo Marconi first transmitted radio signals across the Atlantic Ocean, and demonstrated the potential of radio waves in facilitating data transport via the wireless telegraph. During the

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Implementing RFID Technology in Hospital Environments

1930s, Alexander Watson Watt discovered radar, and illustrated the use of radio waves in locating physical objects. Initially used in World War II in military aircraft in what is now called the first passive RFID system, radar technology enabled identification of incoming aircraft by sending out pulses of radio energy and detecting echoes (Want, 2004). Libraries have used RFID technology for electronic surveillance and theft control since the 1960s. Present-day RFID solutions track objects ranging from tools at construction sites and airline baggage, to dental molds and dental implants. RFID systems monitor the temperature of perishable fruit, meat, and dairy products in transit, in order to ensure that these goods are safe for consumption, and facilitate the detection of package tampering and product recalls (Want, 2005). The U.S. Department of Defense (DoD) mandates the use of RFID tags as replacements for barcodes for tracking goods (Ho, Moh, Walker, Hamada, & Su, 2005), and requires suppliers to use RFID tags in equipment and clothing shipped to military personnel. RFID technology is widely used by major retailers that include Home Depot and WalMart in the U.S., and Marks and Spencer in the United Kingdom to track inventory. In the transportation and education sectors, credit cards that incorporate RFID technology enable automatic transactions at gas stations and toll plazas and at university bookstores, libraries, and cafeterias. RFID systems also facilitate building access, port security, vehicle registration, and supply chain management; verification of the identity of preauthorized vehicles and their drivers at security checkpoints; and reduction in the circulation of counterfeit goods and paper currency (Garfinkel, Jules, & Pappu, 2005). Developed by the U.S. Department of Energy Oak Ridge National Laboratory (ORNL), the RFID-enabled Protected Asset Tracking System is the first instance of RFID technology installed at the National Nuclear Security Administration site (Oak Ridge National Laboratory, 2007).

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Capabilities of the ORNL-sponsored RFID accountability system (RAS) in accurately tracking assets and monitoring the location of personnel were validated in a pilot test conducted in a large-sized facility at the Washington Navy Yard. This initiative also demonstrated the role of RFID technology in facilitating the safety of rescue workers responding to crisis situations and national emergencies in federal facilities, and the importance of using RFID tags on miners, firefighters, and other workers in hazardous occupations, so their location and safety can be monitored as well.

rFId technIcal FundaMentals RFID systems consist of RFID tags or transponders, and interrogators or readers. Classified as passive, semiactive, or active, a RFID tag is an extremely small device containing a microchip, also called a silicon chip or integrated circuit that, at a minimum, holds digital data in the form of an EPC (Electronic Product Code). RFID tags are affixed to or incorporated into objects, such as persons or products (Weinstein, 2005). A RFID tag is also equipped with an antenna for enabling automatic receipt of and response to a query from an RFID interrogator, via radio waves (Myung & Lee, 2006). The RFID communications process involves the exchange of an electromagnetic query and response, thereby eliminating RFID dependency on direct line-ofsight connections. Subsequent to transmission of the EPC from the RFID tag to the RFID interrogator, the tagged object can be monitored and traced. Passive RFID tags are inexpensive and limited, in terms of functions supported (Weinstein, 2005). In terms of transmission, a passive nonbattery operated RFID tag makes use of incoming radio waves when it is within range of a RFID interrogator to transmit a response. A passive RFID tag contains the EPC in the form of eight-bit

Implementing RFID Technology in Hospital Environments

data strings associated with a distinct object and several bits of memory for storing data describing the tagged object. When multiple passive RFID tags transmit EPCs concurrently in response to RFID interrogators, collisions occur, thereby disrupting information flow. Designed to support passive RFID tag operations, the adaptive binary splitting (ABS) collision arbitration protocol diminishes the occurrence of collisions, thereby significantly reducing delay and communications overhead in the transmission process (Myung & Lee, 2006). As with passive tags, semiactive and active RFID tags also feature EPCs or unique identifiers, and utilize the RF spectrum for data transmission. Batteries in semiactive RFID tags remain dormant until signals are received from interrogators. When sufficient power is available, semiactive tags initiate data transmissions in response to interrogator queries. An active RFID tag features an onboard battery that serves as its own power source for performing operations, and transmitting the EPC and related data on-demand in response to interrogator queries. An active tag also supports security functions, and can contain an environmental sensor (Karygiannis, Eydt, Barber, Bunn, & Phillips, 2006). Moreover, an active tag enables transmissions over longer distances than passive and semiactive tags. However, an active tag is limited in sustaining continuous operations as a consequence of battery constraints. Since active tags are larger and more costly to implement than passive and semiactive tags, these tags typically monitor large items. Classified as active RFID systems, wireless sensor networks (WSNs) contain sensors for monitoring the environment (Philipose, Smith, Jiang, Mamishev, Roy, & Sundara-Rajan, 2005). To conserve available power and provision efficient operations, active RFID tags that are part of WSNs are also equipped with battery-powered four-byte or eight-byte processors, and employ cryptographic algorithms to facilitate secure

transmissions. Next-generation sensors are expected to operate without batteries by harvesting power from ambient sources. RFID operations are typically carried out in the unlicensed portion of the RF spectrum, and are dependent on the availability of suitable frequencies and bandwidth at affordable costs. Generally, active RFID tags operate in allocated spectrum in the UHF (ultra high frequency range) between the 300 MHz (megahertz) and 3000 MHz frequencies, and the SHF (super high frequency) between the 3 GHz (gigahertz) and 30 GHz frequencies. These tags carry more data, and support operations over longer distances than passive RFID tags operating in allocated spectrum in the LF (low frequency) between the 30 kHz (kilohertz) and 300 kHz frequencies, and the HF (high frequency) between 3 MHz and 30 MHz frequencies (Littman, 2002). It is important to note that while increased transmission rates and operating range are advantageous in the UHF and SHF spectrum, data transmitted via RFID systems in these frequencies are also vulnerable to interception. Spectrum allocation for RFID utilization differs between countries and regions, and between member states in the European Union.

rFId specIFIcatIons Developed by EPCglobal, the Electronic Product Code (EPC) replaced barcodes developed during the 1970s. An EPC is a unique number that corresponds to an individual product or a container of products. EPCs are embedded in RFID tags that are affixed to or incorporated into an object. As a consequence, the terms EPC tag and RFID tag are used interchangeably. With an EPC and access to the EPC network, detailed information about a product, item, or package, such as dates of manufacture and expiration, can be determined. Designed for companies using RFID technology for supply chain management, the EPC network provisions real-time access to RFID data via the

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Internet. The EPC network also enables product authentication and exchange of product information among authenticated participants (Staake, Thiesse, & Fleisch, 2005). EPCglobal is a participant in GS1, an organization with members in more than 100 countries that supports the design and implementation of global technologies and supply chain standards. The GSI HUG (Healthcare User Group) supports development of automatic product identification specifications for EPC-compliant RFID devices to enhance patient safety and facilitate efficiencies in supply chain management. In addition to EPCglobal, entities active in establishing RFID standards include the European Telecommunications Standards Institute (ETSI) and the International Telecommunications Union—Telecommunications Standards Sector (ITU-T).

rFId InItIatIves In the healthcare space RFID initiatives in the healthcare sector vary in scope and complexity, and support a broad array of applications. For instance, RFID systems consisting of ultra-low power RFID tags monitor patients with medical implantations such as pacemakers, insulin pumps, and defibrillators. The medical identifier embedded in the medical implantation also provides access to the medical history and relevant data including the name of nonverbal or unconscious patients. Hospitals increasingly mandate the use of wristbands embedded with RFID tags for verification of a patient’s identity. RFID tags for surgical patients contain additional data including the type of procedure and the name of the surgeon to ensure optimal outcomes. RFID systems reduce medical errors including incidents of drug over dosage by ensuring the accurate administration of medications given to hospitalized patients. RFID technology also effectively monitors the location and movement of patients, hospital staff, and resources in the

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triage area in hospital emergency departments, and facilitates data collection and analysis. At U.S. hospitals and medical centers—such as Massachusetts General Hospital, New YorkPresbyterian Hospital, Georgetown University Hospital, Beth Israel Deaconess Medical Center, and Stanford University Medical Center— RFID solutions track patients and their physical medical records and monitor expiration dates of time-sensitive products including medications. In Korea, Won Ju Christian Hospital employs password-protected PDAs (personal digital assistants) as interrogators that work in concert with RFID tags embedded in wrist bracelets for mothers, and ankle bracelets for their infants, for securing newborns and eliminating mother and baby mix-ups at discharge (Dalton, Kim, & Lim, 2005). To contain the outbreak of SARS (severe acute respiratory syndrome), RFID technology was used by health officials in Singapore to monitor the movements of every person entering and leaving Singapore General Hospital (Gopalakrishna, Choo, Leo, Tay, Lim, Khan, et al., 2004). Staff and visitors were required to wear credit card-sized RFD tags that transmitted their locations to sensors placed in hospital ceilings. As a consequence of the SARS outbreak, Singapore General Hospital also implemented an online physiotherapy program for enabling physical therapists to remotely monitor their patients, and teach them new exercises at their homes. RFID tags on birds are used to contain outbreaks of avian flu. RFID tags on herds of cattle stem the spread of mad cow disease. The United States Department of Agriculture (USDA) supports a national registry that contains RFID data on infected livestock, such ad deer and bison. This information serves as a foundation for establishing quarantines to prevent widespread epidemics. Developed by the USDA, the National Animal Identification System (NAIS) also enables animal health officials to respond to emergencies and reduce disease spread.

Implementing RFID Technology in Hospital Environments

prIvacy and securIty consIderatIons RFID technology plays a vital role in monitoring the health and safety of patients in hospitals and medical centers. Nonetheless, the ability to obtain real-time detailed information as a consequence of RFID deployment also raises concerns about security (Nath, Reynolds, & Want, 2006). Possible abuse of RFID tracking capabilities also raises questions about potential violations of personal privacy (Ohkubo, Suzuki, & Kinoshita, 2005). Generally, patient-related information collected by RFID systems in the healthcare space is extremely sensitive, and contains personal information that is protected by the Health Insurance Portability and Accountability ACT (HIPAA), and requires the enforcement of strict privacy controls (Karygiannis et al., 2006). Data obtained from RFID tags embedded in medical implantations and patients’ wristbands for one purpose may be covertly used for monitoring individuals without their knowledge or consent. Data obtained from RFID tags embedded in consumer items such as shoes and clothing can potentially be used by employers to monitor surreptitiously the work of employees and terrorists to target attacks against specific political and ethnic groups. As a consequence of RFID system abuse and its potentially adverse impact on data confidentiality and privacy, groups such as CASPIAN (Consumers Against Supermarket Privacy Invasion and Numbers) have launched protest campaigns against manufacturers and retailers worldwide. To counter these concerns, government entities such as Japan’s Ministry of Economy, Trade, and Privacy, and consumer advocate organizations— including the Electronics Frontier Foundation and Electronic Privacy Information Center—have issued RFID privacy and security guidelines to safeguard the integrity of data collection. In California, legislation to impose limits on the use of RFID technology statewide has been proposed. According to the RFID Bill of Rights

drafted by consumer advocates, consumers should be informed if products they purchase contain RFID tags, and be able to remove, deactivate, and/or destroy these tags and the data they contain. (Garfinkel, 2004). RFID privacy protection is also supported by encryption, key-exchange protocols and security specifications, such as the ITU X.509 standard. Methods for disabling RFID tags to prevent responses to queries from interrogators are in development. A promising approach involves the use of clipped RFID tags that can be used by consumers to separate the RFID microchip from the RFID antenna, thereby deactivating RFID operations (Karjoth & Moskowitz, 2005). According to Juels, Rivest, and Szydlo (2003), utilization of selective blocking tags provides another option for addressing privacy concerns associated with the widespread use of RFID technology in consumer products. Selective blocking tags simulate ordinary RFID tags, but prevent RFID interrogators from reading RFID patient and consumer data. To address public opposition to RFID utilization, EPCglobal has also passed a standard that requires RFID tags to be equipped with a kill command, so that consumers can disable RFID functions. This specification is incorporated into the EPCglobal standard titled Class 1 Generation 2 UHF-Air Interface Protocol (Gen2). Additionally, the kill command also prevents utilization of the RFID tag after a product with a killed tag is resold or recycled (Ohkubo, Suzuki, & Kinoshita, 2005).

recent trends Healthcare advances that contribute to longevity also have resulted in the increase of debilitating age-related diseases including dementia and Alzheimer’s. A new approach for enabling the elderly to remain at home involves utilization of sensor-augmented wireless sensor networks (WSNs) (Ho et al., 2005). In WSNs, self-powered sensors embedded into active and semiactive

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RFID tags are called sensor RFIDs. WSNs operate in the HF and UHF spectral frequencies and support a range of applications in the healthcare space, including the intake of medications by the elderly at regular intervals. By querying RFID tags placed on each pill container, WSNs detect changes in the amount of medicine available, and alert seniors to take their medications via blinking lights and beeping sounds. WSNs consisting of RFID tags affixed to everyday objects such as clothing, shoes, chairs, and beds can also track the daily living activities of the elderly at home and in independent living facilities. With the worldwide population expected to reach 761 million in 2025, WSNs are expected to play an increasingly important role in supporting elder healthcare solutions. At Shin-chon Severance Hospital in Seoul, Korea, RFID-augmented WSNs monitor the temperature of blood bank refrigeration to ensure that this blood is safe to use in transfusions. RFID tags at Shin-chon Severance Hospital also track the location of blood bags in transit, so that the intended patient receives the assigned transfusion, thereby reducing the mortality risk for transfused patients and safeguarding the transfusion process (Brooks, 2005).

conclusIon In the healthcare space, RFID technology is distinguished by its effectiveness in enabling patient identification, data collection, asset tracking, and real-time monitoring of the elderly at home and in independent living facilities (Philipose, Fishkin, Perkowitz, Patterson, Fox, Kautz, et al., 2005). In hospital environments, RFID implementations contribute to patient safety by eliminating incorrectly labeled medications, vials holding blood samples and laboratory specimen, and enable innovative application-specific services for treating infectious diseases such as SARS. RFID

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technology presents new options for healthcare enhancement, as well as new and ongoing security and privacy challenges that are not yet fully addressed.

reFerences Brooks, J.P. (2005). Reengineering transfusion and cellular therapy processes hospitalwide: Ensuring the safe utilization of blood products. Transfusion, 45(4), 159–171. Dalton, J., Kim, I.-H., & Lim, B.-K. (2005). RFID technology in neonatal care. Intel Corporation. Retrieved February 7, 2008, from http://www. intel.com/it/pdf/rfid2.pdf Garfinkel, S. (2004). RFID rights. MIT Technology Review. Retrieved February 7, 2008, from http:// www.technologyreview.com/Infotech/14278/ Garfinkel, S., Juels, A., & Pappu, R. (2005). RFID privacy: An overview of problems and proposed solutions. IEEE Security & Privacy, 3(3), 34–43. Gopalakrishna, G., Choo, P., Leo, Y., Tay, B., Lim, Y., Khan, A., et al. (2004). SARS transmission and hospital containment. Emerging Infectious Disease, 10(3), 395–400. Ho, L., Moh, M., Walker, Z., Hamada, T., & Su, C.-F. (2005). Applications: A prototype on RFID and sensor networks for elder healthcare: Progress report. In Proceeding of the 2005 ACM SIGCOMM Workshop on Experimental Approaches to Wireless Network Design and Analysis (pp. 70–75). Juels, A., Rivest, R., & Szydlo, M. (2003). Selective blocking of RFID tags for consumer privacy. In Proceedings of the 10th ACM Conference on Computer and Communications Security (pp. 103–111).

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Karjoth, G., & Moskowitz, P. (2005). Disabling RFID tags with visible confirmation: Clipped tags are silenced. In Proceedings of the 2005 Workshop on Privacy in the Electronic Society (pp. 27–30). Karthikeyan, S., & Nesterenko, M. (2005). RFID security without extensive cryptography. In Proceedings of the Third Annual ACM Workshop on Security of Ad Hoc and Sensor networks (pp. 63–67). Karygiannis, T., Eydt, B., Barber, G., Bunn, L., & Phillips, T. (2006). Guidance for securing radio frequency identification (RFID) systems. Recommendations of the National Institute of Standards and Technology. Special publication (pp. 800–998) (Draft). U.S. Department of Commerce: National Institute of Standards and Technology. Littman, M.K. (2002). Building broadband networks. Boca Raton, FL: CRC Press. Myung, J., & Lee, W. (2006). Adaptive binary splitting: A RFID tag collision arbitration protocol for tag identification. Mobile Networks and Applications, 11, 711–722. Nath, B., Reynolds, F., & Want, R. (2006). RFID Technology and applications. IEEE Pervasive Computing, 5(1), 22–24. Oak Ridge National Laboratory. (2007). Radio frequency identification (RFID) protected asset tracking system (PATS). Engineering Science and Technology Division Internal Newsletter. Retrieved February 7, 2008, from http://www.ornl. gov/sci/engineering_science_technology/technical_article_for_public_site/RFID/radio.shtml Ohkubo, M., Suzuki, K., & Kinoshita, S. (2005). RFID privacy issues and technical challenges. Communications of the ACM, 48(9), 66–71. Philipose, M., Fishkin, K., Perkowitz, M., Patterson, D., Fox, D., Kautz, H., et al. (2004). Inferring

activities from interactions with objects. IEEE Pervasive Computing, 3(4), 50–57. Philipose, M., Smith, J., Jiang, B., Mamishev, A., Roy, S., & Sundara-Rajan, K. (2005). Batteryfree wireless identification and sensing. IEEE Pervasive Computing, 4(1), 37–45. Staake, T., Thiesse, F., & Fleisch, E. (2005). Extending the EPC network – the potential of RFID in anti-counterfeiting. In Proceeding of the 2005 ACM SIGCOMM Workshop on Experimental Approaches to Wireless Network Design and Analysis (pp. 1607–1612). Want, R. (2004). Que focus: RFID: The magic of RFID. Que, 2(7), 41–48. Weinstein, R. (2005). RFID: A technical overview and its application to the enterprise. IT Professional, 7(3), 27–33.

KEY TERMS Active RFID Tag: Features an onboard battery that serves as its own power source for performing operations. Application-Specific Service: Service that satisfies the performance and functional requirements of either an application or class of applications. Barcode: Consists of lines of different widths that identify an item. Works with a scanner. Passive RFID Tag: Lacks an onboard power source. Makes use of incoming radio waves broadcast by an interrogator to power its response. Radio Frequency Identification (RFID): Wireless identification and data capture technology that consists of a tag or transponder and an interrogator or reader to support applications ranging from airport baggage handling to supply chain management.

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RFID System: Consists of the RFID tag, the RFID reader, and the communications between them.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by N. Wickramasinghe; E. Geisler, pp. 705-710, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 4.5

RFID as the Critical Factor for Superior Healthcare Delivery A. Dwivedi University of Hull, UK T. Butcher University of Hull Logistics Institute (UHLI), UK

IntroductIon Innovations in information and communication technologies (ICTs) have transformed the manner in which healthcare organizations function. Applications of concepts such as data warehousing and data mining have exponentially increased the amount of information that a healthcare organization has access to. Work flow and associated Internet technologies are being seen as instruments to cut administrative expenses. Specifically designed ICT implementations, such as work flow tools, are being used to automate the electronic paper flow in a managed care operation, thereby cutting administrative expenses (Dwivedi, Bali, & Naguib, 2005, p. 44; Latamore, 1999). These recent innovations in the use of ICT applications in a healthcare context have altered the manner in which healthcare institutions exploit clinical and nonclinical data. The pendulum has

shifted from the early 1980s, wherein the emphasis of ICT solutions for healthcare was on storage of data in an electronic medium, the prime objective of which was to allow exploitation of this data at a later point in time. As such, most of the early 1980s ICT applications in healthcare were built to provide support for retrospective information retrieval needs and, in some cases, to analyze the decisions undertaken. Clinical data that was traditionally used in a supportive capacity for historical purposes has today become an opportunity that allows healthcare stakeholders to tackle problems before they arise (Dwivedi et al., 2005). However, simultaneously, a number of studies have noted that most information in healthcare is stored in silos, which do not interact efficiently with each other. Kennedy (1995, p. 85) has quoted Keever (a healthcare management executive) who notes that “Healthcare is the most disjointed industry … in terms of information exchange....

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

RFID as the Critical Factor for Superior Healthcare Delivery

Every hospital, doctor, insurer and independent lab has its own set of information, and … no one does a very good job of sharing it.” This problem is being further acerbated by the fact that healthcare managers are being forced to examine costs associated with healthcare and are under increasing pressure to discover approaches that would help carry out activities better, faster, and cheaper (Davis & Klein, 2000; Dwivedi, Bali, James, & Naguib, 2001; Dwivedi et al., 2005; Latamore, 1999). Consequently, the expectations from modern IT applications in healthcare are for applications which support the transfer of information with context. This, in turn, has led to the emergence of clinical information systems that are led by mobile computing technologies (Dwivedi, Bali, & Naguib, 2007; Dwivedi, Wickramasinghe, Bali, Naguib, & Goldberg, 2007; Meletis, Dwivedi, Gritzalis, Bali, & Naguib, 2006).

BACKGRoUND The last decade has seen the rapid emergence and acceptance of healthcare information systems that support the concept of telemedicine and use technologies like Personal Digital Assistant (PDA), Radio Frequency IDentification (RFID) and other mobile computing technologies. This trend has also been supported by a longitudinal survey (see Table 1) of over 200 U.S. healthcare organisations carried over a three year period, from 2000 to 2002 (Morrissey, 2000, 2001, 2002). As seen in Table 1, cClinical information systems in conjunction with mobile computing have become priority areas for healthcare institutions (see Table 1). Modern day IT applications in healthcare, centred on mobile computing devices like PDA, RFID, and wireless local area network (WLAN)

Table 1. Adapted from Modern Healthcare’s annual survey of information system trends in the healthcare industry (Dwivedi, Wickramasinghe et al., 2007; Meletis et al., 2006; Morrissey, 2000, 2001, 2002) Year Number of healthcare organizations surveyed

Clinical Use of Web technology (Intranets)

2000 224 healthcare organizations

60% - felt that could IT could facilitate data exchange among caregivers, that is, physician ordering of tests and access to test results

Limited use as shown by the following 15% - to share clinical guidelines General Uses of Web and Intranet technology

13% - to access multiple databases simultaneously 33% - as a bridge to other information systems 40% - for network wide communication of any kind

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2001

2002

212 healthcare organizations

255 healthcare organizations

Low interest in maintaining a patient’s personal health record accessible via the WWW and matching patients with clinical research.

Despite acknowledging that medication interaction and dosing alerts are possible within most IS - implementation has not commenced

However there is renewed importance of addressing changes in this area due to regulatory obligations

The few organizations who had made big investments in different HIS (EPR and pharmacy) are reporting substantial returns

Some early success from linking “billing and insurance-query operations to payers via the Web” “Significant interest …in using the Web to improve data exchange with physicians and their office staff” About 50% indicated that they had no plans to try anything Webrelated in the care-management area

33% - Using existing clinical and financial information sources to construct data repositories so as to that help spot trends and improve decision-making Further 22% are working to implement such practices whilst about 13% plan to start implementation of similar activities within a year

RFID as the Critical Factor for Superior Healthcare Delivery

products, have already demonstrated their potential and financial viabilities in a healthcare context (Dwivedi, Bali et al., 2007; Dwivedi, Wichramasinghe et al., 2007; Meletis et al., 2006). Recent studies by Meletis et al. (2006) have noted that WLAN-based mobile computing allows healthcare workers to interact in real-time with the hospital’s host computer system to enter, update, and access patient data and associated treatments from all clinical departments (Meletis et al., 2006). A survey of WLAN healthcare installations found that 97% of customers indicated that “WLANs met or exceeded their expectation to provide…a competitive advantage” and that “if the productivity benefits are measured as a percentage return on the total investment … the return works out to be 48%” (McCormick, 1999, p.13). The use of PDAs by physicians has witnessed rapid acceptance in recent times. Today, about 40% of all physicians use PDAs (Serb, 2002). However, the majority of physicians are using PDAs to perform static functions. Most of them use PDAs to collect reference material with: the most popular method being ePocrates - a drug reference application physicians can look up drugs by name or diagnoses, cross-reference similar medications or generic alternatives, and receive alerts on interactions…and which the Journal of the American Medical Association has described as indispensable. (Serb, 2002, p. 44) A few pioneering physicians have started to use PDAs in an interactive way, that is, to write prescriptions, to keep a record of all daily clinical patient interactions, and for bedside charting. This trend was confirmed by another study by Martin (2003), who noted that more then 50% of physicians working in developed countries and under the age of 35 used a PDA in 2003. Similar findings were also reported in other studies. In a related survey, it was found that between 40-50% of all U.S. physicians (including

junior doctors, i.e., residents) were either using a PDA or had the ability and the knowledge to use a PDA in healthcare settings (Miller, Hillman, & Given, 2004). The above findings have been supported by a detailed study by Carroll and Christakis (2004) who noted that from a random study of 2130 physicians, about 35-40% of physicians were using a PDA and that the most common use (80%) of PDAs was for drug referencing. The recent advances in RFID technologies (RFID Journal, 2005) are further promoting the adoption of mobile computing technologies (e.g., PDA) among healthcare stakeholders who have to deal with patient information (i.e., medication, allergies, etc.) on the location of an incident (Dwivedi, Bali et al., 2007; Meletis et al., 2006).

eMergence oF rFId as the crItIcal Factor For superIor healthcare delIvery The origins of Radio Frequency Identification (RFID) systems can be traced to the emergence and consequent large scale adoption of automatic identification procedures (Auto-ID) in purchasing and distribution logistics, and manufacturing companies. The original objective of automatic identification procedures was to provide detailed information about people, goods, and products during transit, so as to enable better tracking. Barcode labels, which today are omnipresent, were the first automatic identification procedures that triggered a revolution in identification systems. Recently, due to their low storage capacity and the fact that they cannot be reprogrammed, they have become less popular, despite being very cheap (Finkenzeller, 2003). The rapid advances in computer hardware technology enabled the storage of data in a silicon chip. A disadvantage of this system was that smart cards, in order to be read, had to be placed in a smart card reader, which would in turn supply the

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RFID as the Critical Factor for Superior Healthcare Delivery

Figure 1. Radio Frequency Identification System (Davis, 2004; Fanberg, 2004; Wilding & Delgado, 2004) Management System

data

RFID tag

data

RFID reader

smart card with power and a clock pulse, which was necessary for the smart card to function. However, the key drawbacks of smart cards as an automatic identification procedure are (a) smart card readers are expensive to maintain, (b) they tend malfunction, and (c) smart card readers kept in public places are prone to acts to vandalism (Finkenzeller, 2003). These drawbacks in turn led to the evolution of automatic identification procedures wherein ideally the power required to operate the electronic data-carrying device was to be transferred from the reader using contact-less technology, and due to this connotation contact-less automatic identification procedures are called RFID systems (Finkenzeller, 2003). An RFID as demonstrated in Figure 1 consists of three components: (1) a tag, (2) a reader, and (3) a computer network. The tag consists of a microchip that has some specific data that enables easy identification and an antenna, which is used to enable transmission of data. The second component of a RFID system is a reader and the reader uses radio waves to read the tag, and transmit data a computer system (i.e., the third component of a

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RFID system). The computer system enables the processing of information (Davis, 2004; Fanberg, 2004; Wilding & Delgado, 2004). This information system is illustrated in Figure 1. RFID systems enable automatic identification and detail location of physical goods. Individual items or batches of goods carry an RFID transponder or “tag” that transmits a radio frequency signal. This signal can be remotely detected by an RFID “reader.” When connected to a materials management system, the data downloaded from the reader is used to monitor and control the movement of those goods (Davis, 2004; Fanberg, 2004; Wilding & Delgado, 2004). In fact, it is the remote communication capability of RFID which differentiates it from existing traceability technologies. Previous automatic identification technologies such as printed batch cards and bar coding required individuals to read or scan the item/batch specific data at the location of the goods. Previous automatic identification technologies are also time consuming, laborious, and prone to inaccuracies, due to the scale and complexity of typical warehousing and distribution operations. A number of studies (Anonymous, 2006a, 2006b, 2006c; Barlow, 2006; Carroll & Christakis, 2004; Davis, 2004; Fanberg, 2004; Hoppszallern, 2006; Pitts, 2005; Schuerenberg, 2005; Scott, 2006; Wicks, Visich, & Li, 2007; Williamson, 2006) have recommended the use of RFID in a healthcare context. In October 2005, the need to incorporate RFID in a healthcare context gained a big push when the Food and Drug Administration (FDA)—the U.S. based regulatory body— approved the use of RFID devices for medical purposes in humans, and listed RFID devices as a Class II medical device with special controls (Schuerenberg, 2005). Some of the key advantages to be gained by the adoption of RFID in a healthcare context include: (1) tracking and combating counterfeit drugs (Fanberg, 2004; Pitts, 2005), (2) obtaining critical patient medical information, when the

RFID as the Critical Factor for Superior Healthcare Delivery

patient is clinically unable to provide the same (Schuerenberg, 2005), (3) preventing medication errors, (Anonymous, 2006b; Schuerenberg, 2005; Wicks et al., 2007), (4) tracking medical equipment (Anonymous, 2006c; Briggs & Martin, 2006; Scott, 2006), and (5) reducing surgical errors (Williamson, 2006).

rFId healthcare success storIes St. Clair Hospital, a 331-bed hospital based in Pittsburgh, Pennsylvania, was keen to improve patient safety by employing IT to prevent errors in medication administration. In 2004, the hospital introduced a medication verification system that combined PDAs with scanning devices. The scanning devices would fit in the PC card slot on the PDAs. The nurses would use the PDAs to scan bar codes located on: (a) patient wristbands, (b) medications, and (c) their name/identification cards. When the system was introduced, nurses discovered that the entire process of scanning increased the time they had to spend on the process of medication administration (Schuerenberg, 2005). Subsequent analyses revealed that a large part of the increase could be traced to the fact that the hospital’s software required nurses to log in to the hospital system either by scanning the bar code on their ID badge or typing in a password on the PDA via a virtual keyboard. Furthermore, the use of bar codes meant that the nurses had to position the PDA near a patient’s bar coded wristband to scan it and as their and/or patients wristbands became worn out, it would take the nurses several attempts before they could get a good scan on older bar coded wristbands (Schuerenberg, 2005). To resolve this problem, the hospital administrators replaced the bar coded wristbands, patient wristbands, and clinical identification badges with RFID tags and the scanning devices that would fit into the PC card slot on the PDAs with a RFID

tag. The introduction of RFID tags significantly reduced the time spent by the nurses on medication administration whilst also ensuring that medication errors were reduced. In addition, as RFID devices can exchange information without the need for direct device to device contact, as is the case for bar coded wristbands, the patient would not have to disturbed during the process, which was a regular occurrence when the bar coded wrist band was attached to the patient’s arm (Schuerenberg, 2005). Another use of RFID devices in healthcare has been to track clinical devices. In the U.S., as per Joint Commission on Accreditation of Healthcare Organization requirements, hospitals are responsible for ensuring that all their devices are regularly maintained or upgraded (as the case may be). Hospital staff have often complained that they have trouble tracking down medical devices (e.g., ventilators, intravenous pumps, etc.), some of which are regarded as life-saving-medical devices. The problem also has an administrative perspective as they have to tracked in-time for maintenance purposes as per the Joint Commission on Accreditation of Healthcare Organization requirements (Scott, 2006). The paediatric critical care unit at the Vanderbilt Children’s Hospital in Nashville, Tennessee, was concerned about the fact that, when required, a large part of their medical equipment could not be located, and often a search for medical equipment would result in location of their equipment in other parts of the hospital facility. When medical equipment could not be found, rental expenses for replacement equipment would cost the between $3,000 and $6,000 a month (Davis, 2004). Vanderbilt Children’s Hospital commissioned American Biomedical Group Inc. (ABGI), an Oklahoma City-based technology management company to implement a RFID solution with a deadline of 6 months for implementation to tackle their problem at a cost of $500,000. During the project, ABGI discovered that less than 50% of the paediatric critical care unit medical equipment

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RFID as the Critical Factor for Superior Healthcare Delivery

were available at any given time, and adoption of RFID managed to alleviate their problems of equipment tracking (Davis, 2004). Christiana Hospital, based in Newark, New Jersey, was concerned about the staff time lost and the distress caused in the time taken to locate patients who were based in the hospitals emergency rooms. A study in November 2004 found that “multiple phone calls and walking tours had to be carried out” in the hospitals emergency department to locate about 20% of their admitted patients. The hospital was able to resolve this problem by adopting a RFID system and within a 12 month period the hospital noticed dramatic improvements along a number of measurable targets (Briggs & Martin, 2006). Mercy and Unity Hospital, a U.S.-based hospital was concerned about the effectiveness of their security mechanisms for tracking patient suffering from Alzheimer’s Disease. In a pilot scheme, they used RFID tags which were embedded on patient wristbands to track patients suffering from Alzheimer’s Disease, so as to prevent them from leaving the hospital unit. During the 4-month test period, adoption of RFID equipped wristbands helped reduce the number of patient watches by about 60% and saved about $30, 000 in costs (Davis, 2004). The U.S.-based Joint Commission on Accreditation of Healthcare Organizations (JCAHO) has noted that it receives about five to eight new voluntary reports of surgical errors each month and further suspects that more cases of surgical errors are not being reported. The JCAHO further revealed that 76% of voluntary reports of surgical errors involved surgery on the wrong body part or site (Williamson, 2006). In September 2005, Richard Choie, a physician at Washington School of Medicine, St. Louis, Missouri, adapted RFID technology to prevent surgical errors, and has termed the system as CheckSite. The objective of this system is to enforce surgical site marking that in turn will prevent surgical errors. The system has been

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implemented at Barnes Jewish Hospital in St. Louis in the U.S. and consequent to its adoption, “there have been no wrong site surgeries and no near misses” (Williamson, 2006). The CheckSite system requires the physician to mark the surgical site on the patients body— this is done in consultation with the patient or the patient’s representative. After the site is marked, the physician will place a special sticker on the patient’s wristband, which has a RFID chip which will be deactivated by the sticker. If this process is not done before the surgery, the wristband will emit visual or auditory signals, and will page the concerned hospital personnel, thereby preventing surgical errors. This system is also financially quite cheap, as it costs only $2.50 per patient, apart from some installation expenditure (Williamson, 2006). A more expensive and robust application of RFID technology to prevent surgical errors is the SurgiChip. It is the world’s first RFID product approved for marking an anatomical surgical location. The SurgiChip system initially embeds surgical related patient information on an RFID smart label and on a chip that travels with the patient into surgery to help prevent errors. The information is also placed in the patient’s file. Before the operation, the information on the chip is confirmed with the patient’s chart and ID wristband (Williamson, 2006) In February of 2004, the FDA, a U.S. based regulatory body had issued an guideline to pharmacy manufacturers that required them to provide a “pedigree” for each of their products. The origins of the term pedigree came from the art world wherein pedigrees are accepted as proof of authentication (Fanberg, 2004). GlaxoSmithKline (GSK), in 2006, commenced an innovative RFID project to provide a pedigree for Trizivir (an HIV medicine) so as to enhance patient safety. GSK have started to put RFID tags on all bottles of Trizivir distributed in the U.S. These tags when scanned at a close range verify that the medication contained in the bot-

RFID as the Critical Factor for Superior Healthcare Delivery

tle is Trizivir. This mechanism was considered necessary as the U.S.-based National Association of Boards of Pharmacy had listed Trizivir as one of 32 drugs most susceptible to counterfeiting and diversion medication. Currently this project has cost GSK several million dollars and it has allowed GSK to track the genuineness (i.e., pedigree) of the medication as it moves through the distribution chain unto the at the point of dispensation to the consumer (Anonymous, 2006a) The cost of implementing an RFID system in healthcare ranges from about $20,000 to $1 million—the variance dependent upon the size of the area where RFID technology is to be deployed and the application (e.g., patient tracking, equipment tracking etc) for which it is required. A typical return on investment on a RFID in healthcare system typically is less than a year, and in exceptional cases, the annual ROI can be up to 450% a year (Davis, 2004).

Future oF rFId Recent estimates on adoption of RFID systems in healthcare organizations indicate that RFID adoption in healthcare is on the verge of a dramatic rise beginning 2007. This estimate is partly based on a survey of more than 300 respondents. The results from the survey indicated that about 75% of respondents noted that adoption of RFID would lead to significant improvement in to patient safety and this was regarded to be the prime mover behind the projected rise of RFID adoption in healthcare (Anonymous, 2006b). The contention that RFID adoption in healthcare is on the verge of a dramatic rise is also supported by another study which has noted that, despite the fact that currently only 4% of U.S. hospitals use RFID to track moveable equipment, a recent study in 2005 by Hospitals & Health Networks Most Wired Survey indicated that over the next 2 to 3 years almost all hospitals will be adopting RFID based solutions to track medical equipment (Scott, 2006).

In 2006, the FDA commented that it is disappointed with the slow progress shown by drug manufacturers in adopting RFID solutions to enable better tracking of drugs so as to prevent counterfeiting. Since then, the FDA has released a draft compliance policy guide for public comment which makes a statement of origin of prescription drugs, that is, their “pedigree,” compulsory (Anonymous, 2006b). Another key obstacle in the uptake of RFID adoption in healthcare is lack of clear industry or government guidance on standards for adoption of RFID technology in Healthcare and about 60% of respondents indicated that they will delay their decisions on RFID adoption until clear guidelines or industry practices emerged. (Anonymous, 2006b).

conclusIon On December 22, 2005, John Halamka, M.D., and CIO at Boston’s CareGroup Healthcare System was the first person in the world to have an RFID chip embedded on his shoulder for medical use. The chip, whose size is about the size of a grain, was implanted in a painless 15 minute medical procedure under local anaesthesia, and it was inserted in a two-inch area on the arm between the elbow and shoulder. The chip inserted was manufactured by Applied Digital and is known as VeriChip. It contained a 16-digit identification number. The RFID chip can be read via a Pocket Reader (one has to wave the RFID reader over the patient to have access to all of the patient’s medical information available on Boston’s CareGroup Healthcare System (Schuerenberg, 2005). The drawback is that if John Halamka is being treated in any other then Boston’s CareGroup Healthcare System, the physicians would not have access to John Halamka‘s medical data. We believe that in the near future, the John Halamka experience will become the norm. This is based on the success stories of RFID in

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RFID as the Critical Factor for Superior Healthcare Delivery

healthcare and of RFID in other sectors, which clearly demonstrate that there is a clear advantage of using RFID, in both clinical and nonclinical settings. Whilst the use of RFID is not compulsory, unlike legislation for bar coding, RFID has clear advantages (e.g., scan multiple items at the same time, remote scanning, scanned items do not have to be in sight at the time of scanning, etc.) over the traditional bar code as an automatic ID procedure. However, more work is required: (a) to improve the interface and interconnection abilities of RFID with mobile computing devices like handheld PDAs or tablet computers, and (b) to enhance the ability of RFID devices to work seamlessly with mobile computing devices in accessing EPRs.

reFerences Anonymous. (2006a). GlaxoSmithKline starts RFID anti-counterfeit tests. Healthcare Purchasing News, 30(5), 8. Anonymous. (2006b). Healthcare sector survey shows RFID’s popularity. Healthcare Purchasing News, 30(1), 8. Anonymous. (2006c). RFID tracking app helps reduce stays. Health Data Management, 14(1), 35. Barlow, R. D. (2006). Bar coding has earned its stripes. Healthcare Purchasing News, 30(6), 22-23. Briggs, B., & Martin, Z. (2006). Christiana Hospital honored for groundbreaking project. Health Data Management, 14(3), 24-26, 28, 30, 32, 34, 36. Carroll, A. E., & Christakis, D. A. (2004). Pediatricians’ use of and attitudes about personal digital assistants. Pediatrics, 113(2), 238-242. Davis, M., & Klein, J. (2000, February). Net holds breakthrough solutions. Modern Healthcare, 14. 830

Davis, S. (2004). Tagging along. Health Facilities Management, 17(12), 20-24. Dwivedi, A., Bali, R. K., James, A. E., & Naguib, R. N. G. (2001). Telehealth systems: Considering knowledge management and ICT issues. Paper presented at the 23rd Annual International Conference of the IEEE - Engineering in Medicine and Biology Society (EMBS), Istanbul, Turkey. Dwivedi, A. N., Bali, R. K., & Naguib, R. N. G. (2005). Knowledge management for healthcare: Using information and communication technologies for decision making. In M. Jennex (Ed.), Case studies in knowledge management (pp. 328-343). Hershey, PA: Idea Group. Dwivedi, A. N., Bali, R. K., & Naguib, R. N. G. (2007). Telemedicine - the next healthcare delivery medium: Fad or Future? [Special issue on the future of technology in healthcare]. International Journal of Healthcare Technology and Management, 8(3-4), 226-249. Dwivedi, A. N., Wickramasinghe, N., Bali, R. K., Naguib, R. N. G., & Goldberg, S. (2007). Critical success factors for achieving superior m-health success. International Journal of Electronic Healthcare, 3(2), 261-278. Fanberg, H. (2004). The RFID revolution. Marketing Health Services, 24(3), 43-44. Finkenzeller, K. (2003). RFID handbook: Fundamentals and applications in contactless smart cards and identification (2nd ed.). Chichester, England; Hoboken, NJ: Wiley. Hoppszallern, S. (2006). Tracking for time savings. Hospitals & Health Networks, 5(4), 44. Kennedy, M. (1995). Integration fever. Computerworld, 29(14), 81-83. Latamore, G. B. (1999). Workflow tools cut costs for high quality care. Health Management Technology, 20(4), 32-33.

RFID as the Critical Factor for Superior Healthcare Delivery

Martin, S. (2003). More than half of MDs under age 35 now using PDAs. Canadian Medical Association Journal, 169(9), 952. McCormick, J. (1999). Wireless hospitals: New wave in healthcare technology. Health Management Technology, 20(6), 12-13. Meletis, B. A., Dwivedi, A. N., Gritzalis, S., Bali, R. K., & Naguib, R. N. G. (2006). Providing secure m-access to medical information [Special issue on integrating mobility into the healthcare sector: The next generation of mobile health applications. International Journal of Electronic Healthcare, 3(1), 51-71. Miller, R. H., Hillman, J. M., & Given, R. S. (2004). Physician use of IT: Results from the Deloitte research survey. Journal of Healthcare Information Management, 18(1), 72-80. Morrissey, J. (2000). Internet dominates providers’ line of sight. Modern Healthcare, 30(15), 72-92. Morrissey, J. (2001). Wanting more from information technology. Modern Healthcare, 31(6), 66-84. Morrissey, J. (2002). High on tech, low on budget. Modern Healthcare, 32(4), 57-72. Pitts, P. (2005). Identifying counterfeit drugs. Canadian Healthcare Manager, 12(8), 33. RFID Journal. (2005). RFID and emerging technologies guide to healthcare report. Retrieved February 14, 2008, from http://www.rfidjournal. com Schuerenberg, B. K. (2005). Implantable RFID chip takes root in CIO...literally. Health Data Management, 13(3), 14, 20, 22. Scott, M. (2006). So that’s where it is. Hospitals & Health Networks, 80(4), 28, 30. Serb, C. (2002). Healthcare at your fingertips? Hospitals & Health Networks, 76(1), 44-46.

Wicks, A. M., Visich, J. K., & Li, S. (2007). Radio frequency identification applications in healthcare. International Journal of Healthcare Technology and Management, 7(6), 522-540. Wilding, R., & Delgado, T. (2004). The story so far: RFID demystified. Logistics & Transport Focus, 6(3), 26-31. Williamson, J. E. (2006). Surgical errors: New products, protocols help slash the risks. Healthcare Purchasing News, 30(1), 22-24.

KEY TERMS Bar Codes: A barcode can be defined as data that is recorded in a form that is machine-readable, and can be used by machine barcode readers, and are typically used to implement Auto ID Data Capture systems. FDA: Food and Drug Administration is a governmental agency within the United States Department of Health and Human Services. It consists of eight offices: (1) Center for Biologics Evaluation and Research (CBER), (2) Center for Devices and Radiological Health (CDRH), (3) Center for Drug Evaluation and Research (CDER), (4) Center for Food Safety and Applied Nutrition (CFSAN), (5) Center for Veterinary Medicine (CVM), (6) National Center for Toxicological Research (NCTR), (7) Office of the Commissioner (OC), and (8) Office of Regulatory Affairs (ORA). Radio Frequency Identification: Radiofrequency identification (RFID) is an automatic identification method which uses devices called RFID tags. An RFID system consists of three components: (1) a tag, (2) a reader, and (3) a computer network. The RFID tag consists of a microchip that has some specific data that enables easy identification and an antenna, which is used to enable transmission of data. The second

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component of a RFID system is a reader and the reader uses radio waves to read the tag, and transmit data a computer system (i.e., the third component of a RFID system).

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by N. Wickramasinghe; E. Geisler, pp. 1191-1198, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care Dante I. Tapia Universidad de Salamanca, Spain Juan M. Corchado Universidad de Salamanca, Spain

abstract This article describes ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by Ambient Intelligence.

IntroductIon The continuous technological advances have gradually surrounded people with devices and technology. It is necessary to develop intuitive interfaces and systems with some degree of intelligence, with the ability to recognize and respond

to the needs of individuals in a discrete and often invisible way, considering people in the centre of the development to create technologically complex and intelligent environments. This article describes ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients in geriatric residences. Ambient Intelligence (AmI) is an emerging multidisciplinary area based on ubiquitous computing, which influences the design of protocols, communications, systems, devices, etc., proposing new ways of interaction between people and technology, adapting them to the needs of individuals and their environment (Weber, et al. 2005). It offers a great potential to improve quality of life and simplify the use of technology by offering a wider range of personalized services and providing users with easier and more efficient ways to communicate and interact with other people and systems (Weber, et al., 2005; Corchado, et al.,

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

2008b). However, the development of systems that clearly fulfil the needs of AmI is difficult and not always satisfactory. It requires a joint development of models, techniques and technologies based on services. An AmI-based system consists on a set of human actors and adaptive mechanisms which work together in a distributed way. Those mechanisms provide on demand personalized services and stimulate users through their environment according specific situation characteristics (Weber, et al., 2005). One of the most important characteristics of ALZ-MAS is the use of intelligent agents. Agents have a set of characteristics, such as autonomy, reasoning, reactivity, social abilities, pro-activity, mobility, organization, etc. which allow them to cover several needs for Ambient Intelligence environments, especially ubiquitous communication and computing and adaptable interfaces. Agent and multi-agent systems have been successfully applied to several Ambient Intelligence scenarios, such as education, culture, entertainment, medicine, robotics, etc. (Corchado, et al., 2008b; Sancho, et al., 2002; Schön, et al. 2005; Weber, et al. 2005). The characteristics of the agents make them appropriate for developing dynamic and distributed systems based on Ambient Intelligence, as they possess the capability of adapting themselves to the users and environmental characteristics (Jayaputera, et al., 2007). The continuous advancement in mobile computing makes it possible to obtain information about the context and also to react physically to it in more innovative ways (Jayaputera, et al., 2007). The agents in ALZ-MAS are based on the deliberative Belief, Desire, Intention (BDI) model (Jennings & Wooldridge, 1995) (Bratman, et al., 1988; Pokahr, et al., 2003), where the agents’ internal structure and capabilities are based on mental aptitudes, using beliefs, desires and intentions (Bratman, 1987; Erickson, et al., 1995; Geogeff & Rao, 1998). Nevertheless, Ambient Intelligence developments need higher adaptation, learning and autonomy levels than pure BDI model (Bratman, et al.,

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1988). This is achieved by modelling the agents’ characteristics (Wooldridge & Jennings, 1995) to provide them with mechanisms that allow solving complex problems and autonomous learning. An essential aspect in this work is the use of a set of technologies which provide the agents automatic and real time information of the environment, and allow them to react upon it. In the next section, the problem description that motivated the development of ALZ-MAS is presented. Section 3 describes the basic components of ALZ-MAS and the most important technologies used to provide the agents in ALZ-MAS with context-aware capabilities. Finally section 4 presents the results and conclusions obtained.

probleM descrIptIon Dependence is a permanent situation in which a person needs important assistance from others in order to perform basic daily life activities such as essential mobility, object and people recognition, and domestic tasks (Costa-Font & Patox, 2005). There is an ever growing need to supply constant care and support to the disabled and elderly, and the drive to find more effective ways of providing such care has become a major challenge for the scientific community (Nealon & Moreno, 2003). The World Health Organization has determined that in the year 2025 there will be 1 billion people in the world over the age of 60 and twice as many by 2050, with nearly 80% concentrated in developed countries (WHO, 2007). Spain will be the third “oldest country” in the world, just behind Japan and Korea, with 35% of its citizens over 65 years of age (Sancho, et al., 2002). In fact, people over 60 years old represent more than 21% of the European population (WHO, 2007), and people over 65 are the fastest growing segment of the population in the United States of America (Anderson, 1999). Furthermore, over 20% of those people over 85 have a limited capacity for independent living, requiring continuous monitoring

An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

and daily assistance (Erickson, et al., 1995). The importance of developing new and more reliable ways of providing care and support for the elderly is underscored by this trend, and the creation of secure, unobtrusive and adaptable environments for monitoring and optimizing health care will become vital. Some authors (Nealon & Moreno, 2003) consider that tomorrow’s health care institutions will be equipped with intelligent systems capable of interacting with humans. Multi-agent systems and architectures based on intelligent devices have recently been explored as supervision systems for medical care for dependent people. These intelligent systems aim to support patients in all aspects of daily life (Cesta, et al., 2003), predicting potential hazardous situations and delivering physical and cognitive support (Bahadori, et al., 2003). Ambient Intelligence based systems aim to improve quality of life, offering more efficient and easy ways to use services and communication tools to interact with other people, systems and environments. Among the general population, those most likely to benefit from the development of these systems are the elderly and dependent persons, whose daily lives, with particular regard to health care, will be most enhanced (Corchado, et al., 2008a; Van Woerden, 2006). Dependent persons can suffer from degenerative diseases, dementia, or loss of cognitive ability (Costa-Font & Patox, 2005). In Spain, dependency is classified into three levels (Costa-Font & Patox, 2005): Level 1 (moderated dependence) refers to all people that need help to perform one or several basic daily life activities, at least once a day; Level 2 (severe dependence) consists of people who need help to perform several daily life activities two or three times a day, but who do not require the support of a permanent caregiver; and finally Level 3 (great dependence) refers to all people who need support to perform several daily life activities numerous times a day and, because of their total loss of mental or physical autonomy, need the continuous and permanent presence of a caregiver.

Agents and multi-agent systems in dependency environments are becoming a reality, especially in health care. Most agents-based applications are related to the use of this technology in the monitoring of patients, treatment supervision and data mining. (Lanzola, et al., 1999) present a methodology that facilitates the development of interoperable intelligent software agents for medical applications, and propose a generic computational model for implementing them. The model may be specialized in order to support all the different information and knowledge-related requirements of a hospital information system. (Meunier, 1999) proposes the use of virtual machines to support mobile software agents by using a functional programming paradigm. This virtual machine provides the application developer with a rich and robust platform upon which to develop distributed mobile agent applications, specifically when targeting distributed medical information and distributed image processing. While an interesting proposal, it is not viable due to the security reasons that affect mobile agents, and there is no defined alternative for locating patients or generating planning strategies. There are also agents-based systems that help patients to get the best possible treatment, and that remind the patient about follow-up tests (Miksch, et al., 1997). They assist the patient in managing continuing ambulatory conditions (chronic problems). They also provide health-related information by allowing the patient to interact with the on-line health care information network. (Decker & Li, 1998) propose a system to increase hospital efficiency by using global planning and scheduling techniques. They propose a multi-agent solution that uses the generalized partial global planning approach which preserves the existing human organization and authority structures, while providing better system-level performance (increased hospital unit throughput and decreased inpatient length of stay time). To do this, they use resource constraint scheduling to extend the proposed planning method with a coordination mechanism that

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handles mutually exclusive resource relationships. Other applications focus on home scenarios to provide assistance to elderly and dependent persons. RoboCare presents a multi-agent approach that covers several research areas, such as intelligent agents, visualization tools, robotics, and data analysis techniques to support people with their daily life activities (Pecora & Cesta, 2007). TeleCARE is another application that makes use of mobile agents and a generic platform in order to provide remote services and automate an entire home scenario for elderly people (CamarinhaMatos & Afsarmanesh, 2002). Although these applications expand the possibilities and stimulate research efforts to enhance the assistance and health care provided to elderly and dependent persons, none of them integrate intelligent agents, distributed and dynamic applications and services approach, or the use of reasoning and planning mechanisms into their model.

an aMbIent IntellIgence based MultI-agent SYSTEM FoR AlZhEIMER health care ALZ-MAS (ALZheimer Multi-Agent System) (Corchado, et al., 2008a; 2008b) is a distributed Figure 1. ALZ-MAS basic structure

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multi-agent system designed upon Ambient Intelligence and aimed at enhancing the assistance and health care for Alzheimer patients living in geriatric residences. The main functionalities in the system include reasoning and planning mechanisms (Glez-Bedia & Corchado, 2002) that are embedded into deliberative BDI agents, and the use of several context-aware technologies to acquire information from users and their environment. As can be seen on Figure 1, ALZ-MAS structure has five different deliberative agents based on the BDI model (BDI Agents), each one with specific roles and capabilities: •

User Agent. This agent manages the users’ personal data and behaviour (monitoring, location, daily tasks, and anomalies). The User Agent beliefs and goals applied to every user depend on the plan or plans defined by the super-users. User Agent maintains continuous communication with the rest of the system agents, especially with the ScheduleUser Agent (through which the scheduled-users can communicate the result of their assigned tasks) and with the SuperUser Agent. The User Agent must ensure that all the actions indicated by the SuperUser are carried out, and sends a copy of its memory base (goals and plans) to the

An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

•

•

•

•

Admin Agent in order to maintain backups. There is one agent for each patient registered in the system. SuperUser Agent. It also runs on mobile devices (PDA) and inserts new tasks into the Admin Agent to be processed by a reasoning mechanism (Corchado, et al., 2008b). It also needs to interact with the User Agents to impose new tasks and receive periodic reports, and with the ScheduleUser Agents to ascertain the evolution of each plan. There is one agent for each doctor connected to the system. ScheduleUser Agent. It is a BDI agent with a planning mechanism embedded in its structure (Corchado, et al., 2008b). It schedules the users’ daily activities and obtains dynamic plans depending on the tasks needed for each user. It manages scheduled-users profiles (preferences, habits, holidays, etc.), tasks, available time and resources. Every agent generates personalized plans depending on the scheduled-user profile. There is one ScheduleUser Agents for each nurse connected to the system. Admin Agent. It runs on a Workstation and plays two roles: the security role that monitors the users’ location and physical building status (temperature, lights, alarms, etc.) through continuous communication with the Devices Agent; and the manager role that handles the databases and the task assignment. It must provide security for the users and ensure the efficiency of the tasks assignments. There is just one Admin Agent running in the system. Devices Agent. This agent controls all the hardware devices. It monitors the users’ location (continuously obtaining/updating data from sensors), interacts with sensors and actuators to receive information and control physical services (temperature, lights, door locks, alarms, etc.), and also checks the status of the wireless devices connected

to the system (e.g. PDA or Laptops). The information obtained is sent to the Admin Agent for processing. This agent runs on a Workstation. There is just one Devices Agent running in the system. Next, the main technologies used in ALZMAS to provide the agents with context-aware capabilities are presented.

Technologies Used in AlZ-MAS for Context-Awareness The agents in ALZ-MAS collaborate with context-aware agents that employ Radio Frequency Identification, wireless networks and automation devices to provide automatic and real time information about the environment, and allow the users to interact with their surroundings, controlling and managing physical services (i.e. heating, lights, switches, etc.). All the information provided is processed by the agents, specially the Devices Agent which is a BDI agent that that runs on a Workstation. The Devices Agent monitors the users’ location (continuously obtaining/ updating data from the RFID readers), interacts with the ZigBee devices to receive information and control physical services and also checks the status of the wireless devices connected to the system (e.g. PDA). The information obtained is sent to the Admin Agent to be processed. All hardware is some way integrated to agents, providing automatic and real time information about the environment that is processed by the agents to automate tasks and manage multiple services. Next, the main technologies used in ALZ-MAS are presented. Radio Frequency Identification (RFID) technology is a wireless communications technology used to identify and receive information about humans, animals and objects on the move. An RFID system contains basically four components: tags, readers, antennas and software. Tags with no power system (e.g. batteries) integrated are

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called passive tags or “transponders”, these are much smaller and cheaper than active tags (power system included), but have shorter read range. The transponder is placed on the object itself (e.g. bracelet). As this object moves into the reader’s capture area, the reader is activated and begins signalling via electromagnetic waves (radio frequency). The transponder subsequently transmits its unique ID information number to the reader, which transmit it to a device or a central computer where the information is processed and showed. This information is not restricted to the location of the object, and can include specific detailed information concerning the object itself. The most use is in industrial/manufacturing, transportation, distribution, etc., but there are other growth sectors including health care. The configuration used in ALZ-MAS consists of a transponder mounted on a bracelet worn on the users’ wrist or ankle, and several sensors installed over protected zones with an adjustable capture range up to 2 meters, and a central workstation where all the information is processed and stored. Figure 2 shows two Sokymat’s Q5 chip 125KHz RFID wrist bands (left) and a RFID USB Desktop Reader (right) used in ALZ-MAS for people identification and location monitoring. Wireless LAN (Local Area Network) also known as Wi-Fi (Wireless Fidelity) networks, increase the mobility, flexibility and efficiency of the users, allowing programs, data and resources to be available no matter the physical location.

These networks can be used to replace or as an extension of wired LANs. They provide reduced infrastructure and low installation cost, and also give more mobility and flexibility by allowing people to stay connected to the network as they roam among covered areas, increasing efficiency by allowing data to be entered and accessed on site (Hewlett-Packard, 2002). New handheld devices facilitate the use of new interaction techniques, for instance, some systems focus on facilitating users with guidance or location systems (Corchado, et al., 2005) by means of their wireless devices. ALZ-MAS incorporates “lightweight” agents that can reside in mobile devices, such as cellular phones, PDA, etc., and therefore support wireless communication, which facilitates the portability to a wide range of devices. Figure 3 shows the user interface executed in a PDA emulator. The Wi-Fi infrastructure in ALZ-MAS supports a set of PDA for interfaces and users’ interaction; a Workstation where all the high demanding CPU tasks (planning and reasoning) are processed; and several access points for providing wireless communication between distributed agents. ZigBee is another important technology used in ALZ-MAS. ZigBee is a low cost, low power consumption, two-way, wireless communication standard, developed by the ZigBee Alliance (ZigBee Standards Organization, 2006). It is based on IEEE 802.15.4 protocol, and operates at 868/915MHz & 2.4GHz spectrum. ZigBee is designed to be embedded in consumer electron-

Figure 2. RFID Technology used in ALZ-MAS: two RFID wrist bands (left); a USB Desktop Reader (right)

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Figure 3. ALZ-MAS’ PDA user interface showing the location of patients and nurses

door locks, alarms, etc. It is necessary a mesh of these boards to control all these services. All information obtained by means of these technologies is processed by the agents. Figure 5 shows the main user interface of ALZ-MAS. Depending of the system requirements, several interfaces can be executed. The interfaces show basic information about nurses and patients (name, tasks that must be accomplished, schedule, location inside the residence, etc.) and the building (outside temperature, specific room temperature, lights status, etc.).

results and conclusIon

ics, home and building automation, industrial controls, PC peripherals, medical sensor applications, toys and games, and is intended for home, building and industrial automation purposes, addressing the needs of monitoring, control and sensory network applications (ZigBee Standards Organization, 2006). ZigBee allows star, tree or mesh topologies. Devices can be configured to act as: network coordinator (control all devices); router/repeater (send/receive/resend data to/from coordinator or end devices); and end device (send/ receive data to/from coordinator). Figure 4 shows a Silicon Laboratories’ C8051 chip-based 2.4GHz development board which controls heating, lights,

Figure 4. A ZigBee device used in ALZ-MAS

ALZ-MAS is an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. ALZ-MAS takes advantage of the cooperation among autonomous agents and the use of context-aware technologies providing a ubiquitous, non-invasive, high level interaction among users, system and environment. One of the most important features in ALZMAS is the use of complex reasoning and planning mechanisms. These mechanisms dynamically schedule the medical staff daily tasks. Figure 6 (left) shows a window with the general planning process result. It contains the date, time to initiate the task, task description, priority of the task, length of the task, and the patient associated with each task. To generate a new plan, a ScheduleUser Agent (running on a PDA) sends a request to the Agents Platform. The request is processed by the Manager Agent which decides creating a new plan. Then, the solution is sent to the platform which delivers the new plan to all ScheduleUser Agents running. The planning mechanism creates optimal paths and scheduling in order to facilitate the completion of all activities defined for the nurses connected to the system. As can be seen on Figure 6 (right), the information is provided to all nurses and doctors in a user-friendly

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Figure 5. ALZ-MAS main user interfaces: a 2D representation (upper left); a 3D representation with a single floor view (upper right); and a 3D representation with multiple floors (down)

Figure 6. Interface window showing the result of a general planning

format using mobile devices (PDA) to see their corresponding tasks. Several tests have been done to demonstrate the efficiency of ALZ-MAS which consisted on

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collecting data regarding the time spent by the nurses on routine tasks and the number of nurses working simultaneously. The prototype was adopted on June 12th, 2007. The tasks executed by

An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

nurses were divided in two categories, direct action tasks and indirect action tasks. Direct action tasks are those which require the nurse acting directly on the patient during the whole task (medication, posture change, toileting, feeding, etc.). In the indirect action tasks the nurses do not need to act directly on the patients all the time (reports, monitoring and visits). We focused on indirect tasks because ALZ-MAS can handle most of them, so nurses can increase their productivity and the quality of health care. Figure 7 shows the average time spent on indirect tasks by all nurses before implementation, with the previous release of ALZ-MAS and finally the release presented in this article. ALZ-MAS continues reducing the time spent on indirect task. For example, the average number of minutes spent by all nurses on monitoring patients has been reduced from more of 150 daily minutes (before

ALZ-MAS implementation) to approximately 90 daily minutes. Furthermore, this new release of ALZ-MAS performed slightly better than the previous release. Figure 8 shows the average number of nurses working simultaneously each hour of a day before and after the implementation of ALZ-MAS. In these set of tests, there were selected 50 patients and 12 nurses. According to the times spent by the nurses carrying out their tasks before the implementation, it can be seen how ALZ-MAS facilitates the more flexible assignation of the working shifts. The number of nurses working simultaneously before and after the implementation of the system is reduced substantially, especially at peak hours in which the indirect action tasks are more prone to overlap with the direct action task. For instance, from 13:00 to 15:00 there is a reduction of 5 nurses working simultaneously.

Figure 7. Average time (minutes) spent on indirect tasks

Figure 8. Number of nurses working simultaneously

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Figure 9. Number detected accessed to restricted zones

This is achieved because there is an optimal distribution of tasks using ALZ-MAS. The security of the centre has also been improved in two ways: the system monitors the patients and guarantees that each one of them is in the right place, and secondly, only authorised personnel can gain access to the residence protected areas. Figure 9 shows the number of accesses to restricted zones detected before and after the implementation of ALZ-MAS. As can be seen, there were detected almost twice unauthorized accesses. This is an important data because it can be assumed that several accesses were not detected in the past and most of them could lead to risky situations. It is demonstrated that ALZ-MAS can improve the security and health care efficiency through monitoring and automating medical staff’s work and patients’ activities, facilitating working shifts organization and reducing time spent on routine tasks. RFID, Wi-Fi and ZigBee technologies supply the agents with valuable information about the environment, contributing to a ubiquitous, non-invasive, high level interaction among users, system and the environment. Future work consists on improving ALZ-MAS by adding more features and increasing its performance.

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ACKNoWlEDGMENT This development has been supported by the Ministerio de Trabajo y Asuntos Sociales IMSERSO, Project GERMAP 137/2007.

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Cesta, A., Bahadori, S., Cortellesa, G., Grisetti, G., & Giuliani, M. (2003). The RoboCare Project, Cognitive Systems for the Care of the Elderly. iN Proceedings of International Conference on Aging, Disability and Independence (ICADI’03). Washington, DC, USA. Corchado, J. M., Bajo, J., De Paz, Y., & Tapia, D. I. (2008a). Intelligent Environment for Monitoring Alzheimer Patients, Agent Technology for Health Care. Decision Support Systems , In Press. Corchado J M, Bajo J, Abraham A. (2008b). GERAmI: Improving the delivery of health care. IEEE Intelligent Systems, Special Issue on Ambient Intelligence - Mar/Apr ‘08. Vol. 23(2) 19-25. Corchado, J. M., Pavón, J., Corchado, E., & Castillo, L. F. (2005). Development of CBR-BDI agents: A tourist guide application. In Proceedings of the 7th European Conference on Case-based Reasoning 2004, Lecture Notes in Artificial Intelligence (LNAI). 3155, 547-559. Springer-Verlag. Costa-Font, J., & Patxot, C. (2005). The design of the long-term care system in Spain: Policy and financial constraints. Social Policy and Society , 4 (1), 11-20. Decker, K., & Li, J. (1998). Coordinated hospital patient scheduling. In Proceedings of the 3rd International Conference on Multi-Agent Systems (ICMAS’98), 104-111. IEEE Computer Society. Erickson, P., Wilson, R., & Shannon, I. (1995). Years of Healthy Life. Statistical Notes (7). González-Bedia, M., & Corchado, J. M. (2002). A planning Strategy based on Variational Calculus for Deliberative Agents. Computing and Information Systems Journal , 10, 2-14. Hewlett-Packard. (2002). Understanding Wi-Fi. Hewlett-Packard Development Company.

Jayaputera, G. T., Zaslavsky, A. B., & Loke, S. W. (2007). Enabling run-time composition and support for heterogeneous pervasive multi-agent systems. Journal of Systems and Software , 80 (12), 2039-2062. Jennings, N. R., & Wooldridge, M. (1995). Applying agent technology. Applied Artificial Intelligence , 9 (4), 351-361. Lanzola, G., Gatti, L., Falasconi, S., & Stefanelli, M. (1999). A Framework for Building Cooperative Software Agents in Medical Applications. Artificial Intelligence in Medicine , 16 (3), 223-249. Meunier, J. A. (1999). A Virtual Machine for a Functional Mobile Agent Architecture Supporting Distributed Medical Information. In Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems (CBMS’99). IEEE Computer Society, Washington, DC. Miksch, S., Cheng, K., & Hayes-Roth, B. (1997). An intelligent assistant for patient health care. In Proceedings of the 1st international Conference on Autonomous Agents (AGENTS’97) California, USA. 458-465. ACM, New York. Nealon, J. L., & Moreno, A. (2003). Applications of Software Agent Technology in the Health Care domain, 212. (A. Moreno, & J. L. Nealon, Eds.) Basel, Germany: Birkhäuser Verlag AG, Whitestein series in Software Agent Technologies. Pecora, F., & Cesta, A. (2007). Dcop for smart homes: A case study. Computational Intelligence , 23 (4), 395-419. Pokahr, A., Braubach, L., & Lamersdorf, W. (2003). Jadex: Implementing a BDI-Infrastructure for JADE Agents. In EXP - in search of innovation (Special Issue on JADE) , 76-85. Sancho, M., Abellán, A., Pérez, L., & Miguel, J. A. (2002). Ageing in Spain. Second World Assembly on Ageing. Madrid, Spain: IMSERSO.

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Schön, B., O’Hare, G. M., Duffy, B. R., Martin, A. N., & Bradley, J. F. (2005). Agent Assistance for 3D World Navigation. Lecture Notes in Computer Science , 3661, 499-499. van Woerden, K. (2006). Mainstream Developments in ICT: Why are They Important for Assistive Technology? Technology and Disability , 18 (1), 15-18. Weber, W., Rabaey, J. M., & Aarts, E. (2005). Ambient Intelligence. Springer-Verlag New York, Inc.

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This work was previously published in International Journal of Ambient Computing and Intelligence, Vol. 1, Issue 1, edited by K. Curran, pp. 15-26, copyright 2004 by IGI Publishing (an imprint of IGI Global).

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Chapter 4.7

Ubiquitous Healthcare:

Radio Frequency Identification (RFID) in Hospitals Cheon-Pyo Lee Carson-Newman College, USA J. P. Shim Mississippi State University, USA

abstract

IIntroductIon

Ubiquitous healthcare has become possible with rapid advances in information and communication technologies. Ubiquitous healthcare will bring about an increased accessibility to healthcare providers, more efficient tasks and processes, and a higher quality of healthcare services. radio frequency identification (RFID) is a key technology of ubiquitous healthcare and enables a fully automated solution for information delivery, thus reducing the potential for human error. This chapter provides an overview of ubiquitous healthcare and RFID applications. In this chapter, the background of ubiquitous computing and RFID technologies, current RFID applications in hospitals, and the future trends and privacy implications of RFID in hospitals are discussed.

Advances in wireless networking, the Internet, and embedded systems move us toward ubiquitous computing. Ubiquitous computing refers to the creation and deployment of computing technology in such a way that it is embedded in our natural movements and interaction with our environments (Lyytinen and Yoo, 2002). Ubiquitous computing enhances computer use by making computers available throughout the physical environment, while making them effectively invisible to the user (Weiser, 1993). With rapid advances in information and communication technologies, ubiquitous healthcare has become possible. Ubiquitous, or pervasive, healthcare refers to healthcare to anyone, anytime, and anywhere by removing location, time and other restraints while increasing both the

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Ubiquitous Healthcare

coverage and quality of healthcare (Varshney, 2005). radio frequency identification (RFID) is a key technology of ubiquitous healthcare. RFID is a technology used to identify, track, and trace a person or an object without using a human to read and record data and enables the automated collection of important business information (Asif and Mandviwalla, 2005). In hospitals, RFID enables a fully automated solution for information delivery at the patient’s bedside, thus reducing the potential for human error and increased efficiency (ITU, 2005). The use of RFID technology in the healthcare market is on rise. A recent study reports that the global market for RFID tags and systems in the healthcare industry will increase steadily from $90 million in 2006 to $2.1billion by 2016 (Harrop and Das, 2006). The purpose of this chapter is to present an overview of ubiquitous healthcare and RFID in hospitals. Specifically, the chapter introduces the background of ubiquitous computing and RFID technologies, current RFID applications in the healthcare industry, the future trends and privacy implications of RFID, and the impact of RFID use on the healthcare industry.

healthcare Industry and InForMatIon technology Healthcare is one of the world’s largest industries. In the United States, for example, it accounts for 14 percent of GDP (Janz et al., 2005). Healthcare is also arguably the most complex and regulated industry, regularly facing change brought on by federal, state, and local regulation, changing competitive landscapes, mergers and acquisitions, and the pressures of cost control (Finch, 1999). The healthcare industry historically has lagged behind other industries in the adoption of information and communication technologies partially due to healthcare managers and executives struggling to cope with environmental challenges in the healthcare industry (Menon et

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al., 2000). Zukerman (2000) pointed out that it is the dynamic nature of the healthcare industry that leads organizations to struggle to survive in turbulent conditions. While the healthcare industry historically has lagged behind other industries in the adoption of information and communication technologies, this is changing at a faster rate (Finch, 1999). Healthcare industry leaders and decision makers have begun to realize the supporting role of technology in their effort to maintain a focus on quality care while meeting the pressures from regulatory bodies, competition, and achieving business and performance goals. The mobile workstation, which can be used for medical records, diagnostics, charting, pharmacy, admissions, and billing, is an example of recently adopted technologies in hospitals. With mobile workstations, physicians can write prescriptions at the point of care, from their offices or from home computers (Coonan, 2002). While inputting orders, physicians can be prompted about drug interactions, potential alternatives, formulary restrictions and patient limitations. As a result, generally illegible handwriting is not an issue and the electronic support systems at the bedside can deter errors.

ubIquItous coMputIng and rFId Rapidly progressing information and communication technologies have brought about increasingly connected computing devices which are so naturalized within the physical environment that users are not able to view the computers. The computing devices are embedded in the environment, track real-time information on current locations anywhere and anytime, and transmit and receive relevant data regarding the users and the context in which they are being used. Ubiquitous computing places considerable requirements on both hardware and software

Ubiquitous Healthcare

development and support. Currently, numerous technologies including global positioning system (GPS), ultra-wideband (UWB), RFID, and cellular triangulation contribute to building ubiquitous computing. Among them, RFID is considered a key technology of the ubiquitous computing era (Römer et al., 2004). RFID technology has its origins in military applications during World War II, but its commercial applications did not begin to be realized until the early 1980s (AIM, 2001). The theory of RFID was first proposed in 1948 in a conference, and the first patent for RFID was filed in 1973 (Asif and Mandviwalla, 2005). However, technology and cost only recently became favorable for widespread adoption. The widespread adoption of RFID technology was also enhanced by mandates from large retailers and government organizations such as Wal-Mart and the U. S. Department of Defense. These organizations require all suppliers to implement this technology at the pallet level within the next few years (Asif and Mandviwalla, 2005). The most familiar current RFID application is the automated toll-paying systems on highways (Asif and Mandviwalla, 2005). This system has reduced overhead for transport companies and facilitated travel for commuters (ITU, 2005). RFID applications have also been widely used in airport baggage handling, electronic payment, retail theft prevention, library systems, automotive manufacturing, parking, postal services, and homeland security (Smith and Konsynski, 2003). Most recently, RFID applications have been used to help to identify natural disaster victims. The US Disaster Mortuary Operational Response Team and health officials in Mississippi’s Harrison County were implanting human cadavers with RFID chips in an effort to speed up the process of identifying victims and providing information to families (Kanellos 2005). RFID technology has many benefits over the traditional bar coding that many firms have become accustomed to using. First, RFID technology

is superior to barcode technology in that its user does not need to know where an object or person is and does not need to be close in order to collect the data (Smith and Konsynski, 2003). RFID tags can be read at a distance and do not require line-of-sight. Unlike barcode and magnetic strips mostly used inside store, RFID can help with the tracking of inventory inside and outside the facility. In addition, RFID technology has read/ write capabilities to store and change data and an ability to read many tags simultaneously (Smith, 2005). Those features are expected to contribute to the improvement of the efficiency, accuracy, and security of both supply chain and inventory management through cost savings. RFID may also facilitate the improved use of warehouse and distribution center space. Goods will not need to be stored according to product type for manual location because RFID allows them to be stored in the most efficient manner based on size and shape (Jones et al., 2005).

coMponents oF rFId RFID technology consists of three components – a tag, a reader, and a computer network (Fanberg, 2004). The key component of an RFID system is the tag itself. The tag contains a microchip with identification data and an antenna for transmitting its data. The readers use radio waves to read the tag, and the data then connects to some type of networked computer system or database in order to process the information. RFID tags are essentially tiny computers. The most basic simply contain product identification information while the advanced tags include monitors that can be updated with information such as weight, temperature, and pressure. RFID systems are typically classified according to the functionality of their tag (Smith and Konsynski, 2003). For the most part, tags are either active or passive. As such, they are categorized according to the power source used by the tag.

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Instead of a traditional barcode, electronic product codes (EPC) are stored in the RFID tag. Like the barcode, the EPC is a unique number that identifies a specific item in the supply chain and is composed of numbers that identify the manufacturer and product type. However, unlike the barcode, the EPC uses an extra set of digits for a serial number to identify unique items (Lai et al., 2005). Therefore, while barcodes only distinguish among products, the EPC codes are unique to each unit and can provide more detailed information (Figure 1). In a typical RFID system, RFID tags are attached to objects and send out EPC information when detecting a signal from the tag reader (Lai et al., 2005). Tag readers, based on cellular technology, can scan products as needed so that a system can identify what products are located in a particular physical space. During reading, the signal is sent out continually by the active tag whereas in the passive tag, the reader sends a signal to the tag and listens (Asif and Mandviwalla, 2005). Regardless of whether this reader is a read only or read/write device, it is always referred to as a reader (ITU, 2005). Unlike barcode scanning, line of sight is not required and readers can deal with hundreds of tags at the same time (Smith and Konsynski, 2003). The data collected by the RFID reader will be sent to backend databases via middleware to be utilized by enterprise systems. To distribute EPC codes quickly and efficiently, the network system, EPCglobal Network, which allows all parties in the supply chain to receive up-to-minute information about product movement, was designed using the Internet Protocol (IP) (Lai et al., 2005). In this system, when any part of a supply chain needs a product or product movement information, a request for particular EPC information can be sent to the Object Name Service (ONS), which provides a global lookup service to translate an EPC into one or more Internet Uniform Reference Locations (URLs). Then, the URLs provide detailed information in a Product Markup Language

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Figure 1. A 96 Bit electronic product code content

(PML) format mainly based on eXtensive Markup Language (XML) (Angeles, 2005).

rFId and the healthcare Industry In the healthcare industry, RFID can be used in various areas. First, the most practical area, and the one gaining the quickest acceptance among healthcare organizations, is to attach active RFID tags to expensive or vital supplies (ITU, 2005). The items can then be retrieved quickly when needed or monitored. There are almost 100,000 fatalities every year in the US that are a result of errors in dispensing medicine (Gazette, 2005). Therefore, a well monitored medical supplies and medicine is critical for the healthcare industry. According to Frost and Sullivan, the investments by pharmaceutical companies in RFID will reach $ 2.3 billion by 2011 (Barnes, 2006). RFID tags can also be attached to the patient to track their location (Smith and Konsynski, 2003). Tracking the location of patients is particularly important in cases of long-term care, mentally challenged patients, and newborns (ITU, 2005). The ability to determine the location of a patient within a hospital can facilitate and expedite the delivery of healthcare. From a patient convenience and enhanced experience perspective, if hospitals used patient identification RFID tags, a nurse or

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other caregiver would not have to wake the patient up to verify their identity. As tags become more sophisticated, they could be used to monitor and transmit patient data (e.g., temperature, respiration, pulse) through wireless sensors that will interoperate within a broad network of generic readers (Smith and Konsynski, 2003). Other possible applications of RFID in the healthcare industry include tracking physicians within the hospital and cleaning of hospital beds. Table 1 summarizes the RFID applications in hospitals. According a recent study, RFID and its related technologies in the hospital marketplace will reach $8.8 billion by 2010 (Sokol, 2005). The study reported that the market will be segmented into three general categories: RFID hardware and software integration ($1.3 billion), infrastructure support for RFID enablement ($2.7 billion) and hospital connectivity ($4.8 billion). Currently, less than 23 percent of RFID solutions implemented by hospitals are using passive RFID technology (Spyglass, 2006). Passive RFID systems require a reader to be waved near a transponder with an RFID chip and have been used in healthcare to identify patients or drugs in medication administration. The study,

however, found that many hospitals hope to use active RFID systems in the future.

obstacles and challenges For rFId adoptIon In hospItals The tremendous potential of RFID in hospitals is, however, being hindered by several obstacles including high cost, the lack of established standards, and privacy and security issues (ITU, 2005). Among them, the cost of tags is a major barrier to the adoption of RFID in hospitals. A study reported that 57 percent of healthcare professionals indicated that a major hurdle is lack of available funding, and 46 percent cited the cost of RFID tags and readers as a major barrier (BearingPoint, 2005). Although it has been projected that there will be a dramatic reduction in the price of the tags over the next few years, the current cost is still prohibitive for many routine applications (ITU, 2005). Currently, low-end tags sell for 7 to 10 cents each and readers cost between $1,000 and $3,000, depending on the features of the device (RFID, 2006).

Table 1. RFID Applications in hospitals RFID Applications

Examples

Tracking Medical Supplies and Medicine

• Holy Name Hospital in New Jersey is using an RFID asset tracking system which has enabled the staff to locate a piece of tagged equipment by using a PC. • St. Vincent’s Hospital in Alabama is monitoring tagged surgical instruments for location and their maintenance schedule

Tracking Patient

• Bangkok hospital issues a RFID wristband to patients which is carrying the patient’s name, age, gender, and dosage of any needed drug. • Jacobi Medical Center in New York traces the medical history of patients by reading information from the RFID radio wristbands.

Locating Medical Staffs

• Staff and patients at the Beth Israel Hospital in New York can be located using the tagged bracelets that they wear.

Other Applications

• Bielefeld municipal hospitals tested beds with integrated RFID chips in order to improve the deployment and cleaning of hospital beds.

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The lack of established standards is also delaying the adoption of RFID technology (ITU, 2005; Lai et al., 2005). There are currently no globally agreed upon standards, and there are literally dozens of manufacturers of tags and readers utilizing multiple frequencies and specifications (Twist, 2005). The lack of standards means that organizations will be forced to incur high costs to ensure compatibility with multiple readers and tags, and it is difficult for most firms to commit significant resources if they do not know whether their suppliers and customers will be using a compatible technology (Twist, 2005). A study reported that 60 percent of healthcare professionals said they have delayed some RFID activities while they wait for industry or government guidance on standards (BearingPoint, 2005). In addition, the concern of privacy has become a major problem to those who adopt RFID in hospitals. Consumer advocacy groups (e.g., Consumers Against Supermarket Privacy Invasion and Numbering [CASPIAN]) have raised privacy issues about RFID technology (Shim et al., 2006). The concerns revolve around consumer privacy and fears that if RFID technology is adopted, it could be used to allow hospitals to obtain information about patients and to track their movement without their knowledge (Jones et al., 2005). Security has also become a major issue in implementing RFID since identification information on passive RFID tags can be easily stolen (Smith, 2005). Additionally, the extreme popularity of bar coding may be an obstacle in the way of RFID adoption since RFID would require significant financial investment and mind-set changes to those who have become accustomed to bar coding (Smith, 2005). Finally, it has been reported that many hospitals are concerned about the network infrastructure, scalability, integration capability and application availability of current RFID technology (Spyglass, 2006).

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conclusIon Ubiquitous healthcare will provide an increased accessibility to healthcare providers, more efficient tasks and processes, and a higher quality of healthcare services. As a key technology of ubiquitous healthcare, RFID enables a fully automated solution for information delivery in hospitals, thus reducing the potential for human error and increased efficiency. In spite of its tremendous global potential, RFID is still marginally adopted and in an early stage in hospitals. For widespread adoption of RFID in hospitals, it is imperative to find solutions to the current obstacles of RFID adoption including high cost, the lack of established standards, and privacy and security issues.

reFerences AIM. (2001). Shrouds of time: History of RFID. Retrieved January 20, 2006, from http://www. aimglobal.org/technologies/rfid/resources/ shrouds_of_time.pdf Angeles, R. (2005). RFID technologies: Supplychain applications and implementation issues. Information Systems Management, 22(1), 51-65. Asif, Z., & Mandviwalla, M. (2005). Integrating the supply chain with RFID: A technical and business analysis. Communications of the Association for Information Systems, 15, 393-427. Barnes, K. (2006). RFID exploding into pharma industry. Retrieved September 3, 2006, from http://www.in-pharmatechnologist.com/news/ ng.asp?n=64786-gartner-frost-and-sullivan-rfid BearingPoint. (2005). Large healthcare organizations are embracing RFID. Retrieved September 4, 2006, from http://www.nahit.org/cms/index. php?option=com_content&task=view&id=157 &Itemid=148

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Coonan, G. M. (2002). Making the most of mobility. Health Management Technology, 23(10), 32-36.

RFID. (2006). The cost of RFID equipment. Retrieved April 21, 2006, from http://www.rfidjournal.com/faq/20

Finch, C. (1999). Mobile computing in healthcare. Health Management Technology, 20(3), 64-65.

Römer, K., Schoch, T., Mattern, F., & Dübendorfer, T. (2004). Smart identification frameworks for ubiquitous computing applications. Wireless Networks, 10(6), 689-700.

Gazette. (2005). RFID in the pharma supply chain. Retrieved September 2, 2006, from http://www. rfidgazette.org/2005/11/rfid_in_the_pha.html Harrop, P., & Das, R. (2006). RFID in healthcare 2006-2016. Cambridge, UK: IDTechEx. Janz, B. D., Pitts, M. G., & Otondo, R. F. (2005). Information systems and health care II: Back to the future with RFID: Lessons learned - Some old, some new. Communications of the Association for Information Systems, 15. ITU. (2005). Ubiquitous network societies: The case of radio frequency identification. Retrieved January 20, 2006, from http://www.itu.int/osg/spu/ ni/ubiquitous/Papers/RFID%20background%20 paper.pdf Fanberg, H. (2004). The RFID revolution. Marketing Health Services, 24(3), 43-44. Kanellos, M. (2005). RFID tags used to track hurricane Katrina dead. Retrieved September 2, 2006, from http://networks.silicon.com/ lans/0,39024663,39152382,00.htm Lai, F., Hutchinson, J., & Zhang, G. (2005). Radio frequency identification (RFID) in China: Opportunities and challenges. International Journal of Retail & Distribution Management, 33(11/12), 905-916. Lyytinen, K., & Yoo, Y. (2002). Issues and challenges in ubiquitous computing. Communications of the ACM, 45(12), 63-65. Menon, N. M., Lee, B., & Eldenburg, L. (2000). Productivity of information systems in the healthcare industry. Information Systems Research, 11(1), 83-92.

Shim, J. P., Varshney, U., & Dekleva, S. (2006). Wireless evolution 2006: Cellular TV, wearable computing, and RFID. Communications of the Association for Information Systems, 18, 497-518. Smith, A. D. (2005). Exploring radio frequency identification technology and its impact on business systems. Information Management & Computer Security, 13(1), 16-28. Smith, H., & Konsynski, B. (2003). Developments in practice X: Radio frequency identification (RFID) - An internet for physical objects. Communications of the Association for Information Systems, 12, 301-311. Sokol, B. (2005). RFID and emerging technologies market guide to healthcare. Retrieved September 1, 2006, from http://www.rfidjournal.com/article/ articleview/1534/1/1/ Spyglass. (2006). Providers not passive about RFID. Retrieved September 5, 2006, from http:// healthdatamanagement.com/HDMSearchResultsDetails.cfm?articleId=12499 Twist, D. C. (2005). The impact of radio frequency identification on supply chain facilities. Journal of Facilities Management, 3(3), 226-239. Varshney, U. (2005). Pervasive healthcare: Applications, challenges and wireless solutions. Communications of the Association for Information Systems, 16, 52-72. Weiser, M. (1993). Some computer science issue in ubiquitous computing. Communications of the ACM, 36(7), 74-84.

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Zuckerman, A. M. (2000). Creating a version for the twenty-first century healthcare organization. Journal of Healthcare Management, 45(5), 294-305.

KEY TERMS Electronic Product Codes (EPC): A unique serial number that identifies an object or person. Currently, the 96-bit EPC is the most prevailing version and contains the detail information about the specific object or person being monitored. Extensive Markup Language (XML): A general-purpose markup language, whose primary purpose is to facilitate the sharing of data across different information systems, particularly via the Internet.

Object Name Service (ONS): An automated networking service that points computers to sites on the World Wide Web. Radio Frequency Identification (RFID): A technology used to identify, track, and trace a person or an object without using a human to read and record data. Ubiquitous Healthcare: Healthcare to anyone, anytime, and anywhere by removing location, time and other restraints while increasing both the coverage and quality of healthcare. Ultra-Wideband (UWB): A wireless communications technology that can transmit large amounts of digital data over a wide spectrum of frequency bands with very low power for a short distance.

Internet Protocol (IP): The method by which data is sent from one computer to another on the Internet. Each computer on the Internet has at least one IP address that uniquely identifies it from all other computers on the Internet.

This work was previously published inHandbook of Research on Distributed Medical Informatics and E-Health, edited by E. Ferrari and B. Thuraisingham, pp. 273-281, copyright 2009 by Information Science Publishing (an imprint of IGI Global).

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Chapter 4.8

Ubiquitous Risk Analysis of Physiological Data Daniele Apiletti Politecnico di Torino, Italy Elena Baralis Politecnico di Torino, Italy Giulia Bruno Politecnico di Torino, Italy Tania Cerquitelli Politecnico di Torino, Italy

abstract Current advances in sensing devices and wireless technologies are providing a high opportunity for improving care quality and reducing the medical costs. This chapter presents the architecture of a mobile healthcare system and provides an overview of mobile health applications. Furthermore, it proposes a framework for patient monitoring that performs real-time stream analysis of data collected by means of non-invasive body sensors. It evaluates a patient’s health conditions by analyzing different physiological signals to identify anomalies and activate alarms in risk situations. A risk function for identifying the instantaneous

risk of each physiological parameter has been defined. The performance of the proposed system has been evaluated on public physiological data and promising experimental results are presented. By understanding the challenges and the current solutions of informatics appliances described in this chapter, new research areas can be further investigated to improve mobile healthcare services and design innovative medical applications.

IntroductIon Technological advances in sensing devices, miniaturization of low-power microelectron-

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Ubiquitous Risk Analysis of Physiological Data

ics, and wireless networks enable the design and exploitation of wireless devices capable of autonomously monitoring human health conditions to improve mobile healthcare services for patients and health professionals. Mobile health applications may play a key role in saving lives by allowing timely assistance, in collecting data for medical research, and in significantly cutting the cost of medical services. Hence, advances in health information systems and healthcare technologies offer a tremendous opportunity for improving care quality while reducing cost [Lee, 2006]. Non-invasive medical sensors measuring vital signs (e.g., temperature, heart rate, blood pressure, oxygen saturation, serum glucose), integrated into tiny intelligent wearable accessories (e.g., watches [http://www.skyaid.org/ LifeWatch/life_watch.htm]), are currently under development [Jovanov, 2005]. Wearable devices allow an individual to closely monitor changes in her or his vital signs for extended periods of time and provide a comprehensive view of a patient’s condition. These devices can be integrated into a general health system architecture to continuously monitor patient health status and timely recognize life-threatening changes. Hence, an important issue in this context is the real-time analysis of physiological signals to characterize the patient condition and immediately identify dangerous situations. This chapter describes the architecture of a mobile healthcare system and provides an overview of health applications. Furthermore, it proposes a flexible framework called IGUANA (Individuation of Global Unsafe Anomalies and Alarm activation) to perform stream analysis of physiological data to monitor a patient’s health condition, by analyzing physiological measures collected by means of a set of wearable sensors. The real-time analysis exploits data mining techniques for assessing the instantaneous risk of monitored people. To allow ubiquitous analysis, real-time processing is performed on mobile devices (e.g., Pocket PCs and smart phones).

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When a dangerous situation is detected, an immediate intervention may be requested by raising an alarm (e.g., phone call, SMS) to the closest medical centre.

MobIle health systeM archItecture The overall architecture of a mobile health system (see, e.g., [Jones, 2006], [Apiletti, 2006]) is shown in Figure 1. It may be composed by some or all of the following subsystems: • • •

A body sensor network A wireless local area network A GSM network

Each individual (patient) wears a set of sensors that monitor physiological signals. These sensors, which are integrated into non-invasive objects, are connected to the user’s device (also called personal server, e.g., a smart phone or a PDA) through a short range communication link (e.g., Bluetooth), in charge of transmitting recorded signals. The device may locally elaborate the incoming signals to immediately detect life-threatening situations. The set of wearable sensors and the mobile device form the body sensor network. The second subsystem allows the communication between the user’s mobile device and the elaboration centre, possibly by means of an infrastructure node (e.g., an access point). Communication with the elaboration centre may occur when recorded data is transferred to the system for off-line analysis or to backup/gather historical data. Finally, through the GSM network an alert message may be sent to the closest medical centre to request prompt medical intervention when a risk situation is detected. A more detailed description of each subsystem is presented in the following. Body Sensor Network. A body sensor network consists of multiple sensing devices capable

Ubiquitous Risk Analysis of Physiological Data

Figure 1. General architecture of a mobile health system

of sampling and processing one or more vital signs (e.g., heart rate, blood pressure, oxygen saturation, patient activity, body temperature) or environmental parameters (e.g., patient location, environment temperature and light). Furthermore, these devices are able to transfer relevant gathered measurements to a personal server (i.e., a mobile device like a personal digital assistant or a smart phone) through a wireless personal network implemented using ZigBee (based on the IEEE 802.15.4 standard) or Bluetooth (defined in the IEEE 802.15.1 specifications). According to the type and nature of a healthcare application the frequency of relevant events (e.g., sampling, processing, and communicating) is set. Sensors periodically transmit their status and gathered measurements. Power consumption reducing strategies are exploited to extend battery life. Furthermore, since sensing devices are placed strategically on the human body, as tiny wearable accessories they must satisfy requirements for minimal weight, miniature form-factor, and low-power consumption to allow extended health monitoring time. Several efforts have been devoted to the design of wearable medical systems ([Axisa, 2003], [Varady, 2002]) and the reduction of power

consumption of medical body sensors ([Anliker, 2004], [Cheng, 2004], [Branche, 2005]). For example in [Axisa, 2003] the authors describe a system for the measurement of the nervous system activity by means of a smart shirt and a smart glove, which use textiles with sensors and wires for communication. Instead, in [Anliker, 2004], the authors describe several ways to reduce power consumption by choosing passive sensors, activating the display back-light only when the user presses a button, putting special care in the analog processing unit design, reducing clock frequency, and replacing a discrete analog board with an ASIC. In [Branche, 2005] a method to minimize LED driving currents, lowering the overall power requirement of a reflectance mode pulse oximeter, is described. Wireless LAN (WLAN). Once the physiological data have been collected by means of the body sensor network and transferred to the personal server, this device may send them to an elaboration centre through the WLAN. By means of the WLAN the personal server can reach an Internet access point to communicate with medical centre services (e.g., off-line physicians’ analysis). The elaboration centre is generally a computer which locally stores historical physiological signals col-

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lected during everyday monitoring and provides a powerful graphical user interface to show time series of physiological data. The elaboration centre is particularly convenient for in-home monitoring of elderly patients. Many efforts have been devoted to integrate mobile devices (e.g., PDAs) and WLAN technology [Lin, 2004]. Through the WLAN an Internet access point is reached to transfer in real time the patient’s signals to a remote central management unit, where medical staff can access and analyze the physiological data. Also the framework proposed in [Wu, 1999] is focused on a medical mobile system which performs real-time telediagnosis and teleconsultation. Patient measures are collected by a DSP-based hardware, compressed in real time, and sent to the physicians in hospital. GSM Network. If the mobile device is equipped with appropriate intelligence, it can process the physiological data locally and automatically generate alarms when life-threatening events are detected [Manders, 1996]. When the device identifies a dangerous situation, it can send an alarm to the medical centre. In this way, data are transmitted only when needed and data compression can be avoided. In [Varshney, 2006] the authors concentrate on improving transmission of emergency messages, which must be reliably delivered to healthcare professionals with minimal delays and no message corruption. They propose a network solution for emergency signal transmission using ad hoc wireless networks, which can be formed among patient-worn devices. The core of the overall architecture is the mobile device which records physiological values from wearable sensors, transmits vital signs to the elaboration centre, locally elaborates them to detect dangerous situations, and sends alert messages to request prompt medical intervention. Connectivity with other architectural components employs different technologies. While the mobile device interfaces to the body sensor network through a network coordinator that implements ZigBee or Bluetooth protocols, it exploits mobile

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telephone networks (e.g., GSM) or WLANs to reach an Internet access point for communicating with the medical server. Furthermore, the mobile device is also a critical point of the architecture. Since portable appliances work with different constraints (e.g., power consumption, memory, battery), the analysis of physiological signals performed on mobile devices requires optimized power consumption and short processing response times which are important research topics in different computer science areas.

health care applIcatIons Since health care applications of intelligent systems are becoming pervasive, sensor technologies have been exploited for patient monitoring. Furthermore, medical staff and patients can benefit from the monitoring process by applying automatic real-time knowledge extraction algorithms. Hence, an important issue in this context is the analysis of physiological signals performed on mobile devices, which requires optimized resource-aware algorithms. In [Lin, 2004] the authors propose a monitoring system, which integrates PDAs and WLANs. Through the WLAN, the patient’s measures are sent to a remote management unit, where the medical staff can analyze the data. In [Wu, 1999] the architecture of a medical mobile system is proposed. It performs real-time telediagnosis and teleconsultation. Patient data are gathered by a DSP-based device, compressed in real time, and sent to physicians. The main advantage of these approaches is the simplicity of the architecture, which does not require any intelligence to the devices, since the analysis is performed in the elaboration centre. However, since this architecture introduces a delay for data transmission, it may cause a delay in detecting a critical condition. Furthermore, it requires the presence of physicians to monitor patients also in normal conditions. If the mobile device is a smart appliance, it can

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elaborate the measures locally and trigger alarming actions [Manders, 1996]. When dangerous conditions are detected, the medical centre can be automatically warned. Thus, only when unsafe situations occur, data are actually transmitted. One step further towards the elaboration on mobile devices is proposed in [Gupta, 2004] where a PDA is exploited to receive data from medical sensors and to transmit them over bandwidthlimited wireless networks. The authors specifically address the problems of managing different medical data (e.g., vital bio-signals and images), developing an easy interface (user-oriented for physicians) to view or acquire medical data, and supporting simultaneous data transfers over low bandwidth wireless links. Many efforts have been dedicated to improving hardware and connectivity among devices ([Lorincz, 2004], [Jones, 2006]), but less attention has been devoted to investigating analysis techniques to assess the current risk level of a patient. However, the definition of efficient algorithms that automatically detect unsafe situations in real time is a difficult task. In [Varady, 2002] an algorithm to discover physiological problems (e.g., cardiac arrhythmias) based on a-priori medical knowledge is proposed. Physiological time series recorded through sensors may be exploited for learning usual behavioral patterns on a long time scale. Any deviation is considered an unexpected and possibly dangerous situation. More recently, in [Sharshar, 2005] the extraction of temporal patterns from single or multiple physiological signals by means of statistical techniques (e.g., regression) is proposed. Single signal analysis provides trend descriptions such as increasing, decreasing, constant and transient. Instead, multiple signal analysis introduces a signal hierarchy and provides a global view of the clinical situation. Furthermore, a machine learning process discovers pattern templates from sequences of trends related to specific clinical events. The above mentioned solutions either are limited to specific physiological signals, or require some kind of

a-priori information, such as fixed thresholds, or address related but different problems, such as detecting long term trends.

ThE IGUANA FRAMEWoRK The IGUANA (Individuation of Global Unsafe Anomalies and Alarm activation) framework is based on the overall architecture shown in Figure 1. It performs real-time stream analysis of data collected by a body sensor network on a mobile device and evaluates a patient’s health conditions by analyzing different clinical signals to identify anomalies and activate alarms in risk conditions. The building blocks of the framework are shown in Figure 2. Since risk conditions depend on the specific disease or patient profile, we first perform a training phase in which the framework automatically learns the common and uncommon behaviors. Given historical physiological data, IGUANA creates different risk models tailored to specific conditions (e.g., a specific disease) or patient profiles. The training phase is performed off-line in the elaboration centre and different models of patients and diseases can be created. The most suitable model for the current monitored patient is exploited in the risk evaluation phase. Since patient conditions depend on the contribution of several physiological signals (i.e., heart rate, blood pressure, oxygen saturation), we devise a global risk function to evaluate the risk indicator for the patient at each instant. The proposed risk function combines different components. Each component models a different type of deviation from the standard behavior, such as the difference from the moving average value to evaluate a long term trend, or the difference from the previous measure to detect quick changes. Furthermore, standard conditions for patients may be represented by a normality band, whose upper and lower bounds are denoted as normality thresholds. Outside these thresholds, the risk of

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Figure 2. Building blocks of the IGUANA framework Training Historical data

Model Model building

Risk evaluation

Risk labelled data

Real time data

a patient additionally increases. Higher danger levels are denoted by a higher risk value of the risk function. During the on-line classification phase, IGUANA processes real-time streams of measures collected by sensors. For each measure, the risk value is computed by means of the proposed risk function to evaluate the current patient condition. If a dangerous situation is detected, an alarm (e.g., phone call, SMS) can be sent to the closest medical centre to request prompt medical intervention.

Risk Function The idea of quantifying the health status of a patient by means of his/her physiological signals is based on the comparison between current conditions and the common behavior derived by the analysis of previously collected data. We define as dangerous a situation in which the patient exhibits a deviation from a standard conduct described by the model. The model can be tailored to the different clinical conditions of a patient. We base our risk evaluation on a function that combines different components. Each component assesses a kind of deviation from the normal clinical behavior. The same risk function is applied to all physiological signals. However, different weights may be assigned for each signal, according to its

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importance in the global clinical condition evaluation and its specific physiological characteristics. The risk of each measure depends on the following components, which are depicted in Figure 3. 1.

2.

3.

Offset: It is the difference between the current measure and the moving average value. Since the moving average is the mean value of the measures in a given time window, the offset shows long term trends related to the current situation of a given patient. Slope: It is the difference between the current and previous measures. Its purpose is to detect quick changes. Hence, it shows short term trends. Dist: It is the difference between the current value and the closest of two thresholds, named “normality thresholds”. Normality thresholds define a range outside which the patient risk increases because of excessively high (or low) values. They are estimated as the maximum and minimum values of the moving average computed on the data analyzed during the model building phase. However, some patients may have slightly higher (or lower) values, due to particular diseases or special treatments. Hence, such thresholds should be adapted by the doctor to the specific needs of the patient.

Ubiquitous Risk Analysis of Physiological Data

The above risk components are combined to compute a risk value by meansof the risk function in Box 1, where wo, ws, and wd are weight factors for the different risk components. A higher risk is associated with a point with high offset and slope, because it is far from the moving average of the signal and it is on a trend which increases the distance. Points with low offset and slope correspond to a lower risk value, because the signal, even if far from its moving average, shows a stabilizing trend, because its distance is decreasing. This concept is expressed in the first term of the function (i.e., wo ⋅ Offset + ws ⋅ Slope ), but it is not sufficient to properly estimate the risk value. According to this term only, a point which has null slope and null offset (i.e. a measure equal to the previous one) has the same risk as a point with high opposite slope and offset values, which describes a situation where the measure is really far from the average but quickly returning to normal-

ity. The second term in the risk formula (i.e., wr ⋅ Offset 2 + Slope 2 ) takes into account this effect by considering the distance of a point in slope-offset coordinates from the origin. Finally, the dist contribution is added as third term. It increases the risk associated with measures outside the normality thresholds range. The weights ws, wo, and wd are parametric functions of slope, offset, and dist respectively. The coefficient wr is a function of wo and ws. Each weight is in the range [0,1] and can be different for positive and negative component values. This approach allows a wide degree of flexibility in the risk evaluation, thus suiting different characteristics of physiological signals. For example, while increases in peripheral blood oxygen saturation are beneficial, decreases are relevant to risk evaluation. By considering only negative component values the correct interpretation of the physiological behavior is provided (e.g., positive and negative offset values can be easily associated with different weights).

Figure 3. Risk components used in the risk function

Box 1. Frisk = wo ⋅ Offset + ws ⋅ Slope + wr ⋅ Offset 2 + Slope 2 + wd ⋅ Dist

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Ubiquitous Risk Analysis of Physiological Data

in the global risk computation. For example, considering heart rate and body temperature of a heart patient, the weight of the heart rate signal should be higher than the weight of the body temperature signal in the global risk computation. Since we focused our analysis on identifying unsafe anomalies which can lead to vital threat, we consider only vital signals and even a single high risk measure is enough to show an unsafe trend. For this reason, the global risk associated with the clinical situation of the patient at a given instant is defined as the highest risk level among those assigned to every physiological signal at the same instant.

Training Phase In the training phase we analyzed historical clinical data to automatically model normal and unsafe situations. The most infrequent situations are considered as representative of risky situations and are exploited to model dangerous states, while common behaviors yield a model of standard (normal) states. Since situations depend on the specific disease or patient profile, different models can be built. After computing for every measure its corresponding risk components (offset, slope and dist), the model building step is performed in three parts: •

•

•

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Measure clustering: Clustering is separately applied to the offset, slope and dist measures, to partition them in (monodimensional) clusters, characterized by homogeneous values. IGUANA is currently based on a hierarchical clustering technique, but the generality of our framework allows us to exploit any suitable clustering algorithm. Different selection issues are discussed in the Experimental Results section. At the end of this phase, for each considered sensor stream, a collection of classes for offset, slope and dist values is available, each characterized by upper and lower bounds. Measure risk computation: The risk associated with each physiological signal (or measure) is computed by applying the risk function to the offset, slope and dist risk levels. Measure risk is divided into a finite number of values by means of a discretization step. Global risk computation: The clinical situation of a patient depends on every physiological signal at the same instant. Since different physiological signals give different contribution to the global risk according to the specific disease or clinical conditions, we allow different weights for each signal

Real-Time Risk Evaluation Phase Real time streams of measures incoming from different sensors are initially processed separately, but the same operations are applied to all of them and at the end the results are combined into a unique value indicating the risk factor of the current clinical situation. Before starting the analysis, the value of each incoming measure is compared with user-defined absolute thresholds to determine whether it is outside a given range. If so, it is directly assigned the highest risk level. Examples of user defined thresholds for some vital signals are: • • • •

Heart rate: 40-150 beats per minute; Arterial blood pressure (systolic): 80-220 mmHg; Arterial blood pressure (diastolic): 40-120 mmHg; Peripheral blood oxygen saturation: 90100%.

Next, the offset, slope and dist components are computed for the incoming measure. The needed information is: (i) the previous measure, (ii) the previous moving average value, and (iii) the normality threshold values. A risk level is

Ubiquitous Risk Analysis of Physiological Data

assigned to each component, by comparing the current values with the predefined classes stored in the model itself. The risk associated with each physiological signal (or measure) is computed by applying the risk function to the offset, slope and dist risk levels. In this step, the user-defined weight parameters (see Section “Risk function”) are exploited. Next, the maximum risk among all synchronous measures is assigned as global risk level. If the obtained global risk level is above a user-defined threshold, an alarm may be triggered.

experIMental results We validated our approach by means of several experiments addressing both the effect of varying several parameters of the framework (i.e., clustering algorithm selection, sliding window width, sampling frequency, and risk function weights), the performance of its current implementation, the required resources and the battery average lifetime. While experiments for the model building phase have been performed off-line using a typical desktop PC, the classification phase has been extensively tested on mobile platforms.

datasets We validated the IGUANA framework with sensor measures publicly available on the Internet and collected by PhysioBank, the PhysioNet archive of physiological signals (www.physionet.org/ physiobank), maintained by the Harvard-MIT Division of Health Sciences and Technology. We analyzed 64 patient records from the MIMIC-numerics database (Multiparameter Intelligent Monitoring for Intensive Care) [http:// www.physionet.org/physiobank/ database/mimicdb], for whom the medical information we needed had been recorded and who were more than 60 years old. MIMIC-numerics is a section of the larger MIMIC database whose data

is represented in the numeric format displayed in the digital instrumentation used for patient monitoring in hospitals. Since the MIMIC database collects data from bed-side ICU (Intensive Care Unit) instrumentation, the clinical situations of the patients we analyzed were extremely serious, allowing us to the test our approach in such utmost conditions. For our purpose, in the MIMIC-numerics database we chose 4 physiological signals considered significant of a patient’s health conditions: (i) heart rate (HR) [beats per minute], (ii) systolic arterial blood pressure (ABPsys) [mmHg], (iii) diastolic arterial blood pressure (ABP-dias) [mmHg], and (iv) peripheral blood oxygen saturation (SpO2) [percentage]. Original measures from the MIMIC-numerics database are provided every second. NA-threshold values (see Section “Real-time Risk Evaluation Phase”) were determined according to medical literature. PhysioNet also provides PhysioToolkit, a library of software for physiological signal processing and analysis. We used some PhysioToolkit tools (e.g., the rdsamp utility/command) for extracting the desired data from the databases and during the preprocessing phase.

Clustering Algorithm Selection We considered many clustering techniques (e.g., partitioning, hierarchical, and density-based). We focused on partitioning and hierarchical techniques [Han, 2000], which were available in the statistical open-source environment R [http:// www.r-project.org/]. Partitioning algorithms, such as k-means, performed worse than hierarchical algorithms, because they clustered also normality situations in different risk levels. Figure 4(a) shows this wrong behaviour. Hierarchical clustering algorithms may use different methods to compute the inter-cluster distance. In our context, the average linkage method yields better results than single linkage (which forms chains of points), or complete linkage, or ward. Figure 4(b) shows clusters obtained by applying the average linkage

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Ubiquitous Risk Analysis of Physiological Data

method. We finally observe that the clustering algorithm is a single, modular component of our framework. Any suitable algorithm may be easily integrated in place of the current one.

Sliding Window Size The size of the sliding window models the effect of the recent past on the current situation. The longer the sliding window, the less the moving average follows any sharp trend of a measure. Decreasing the sliding window size increases the rapid adaptation of risk evaluation to abrupt changes in a measure. The offset is the risk component affected by variations of the sliding window. It is based on the moving average value, which is strongly affected by the sliding window length. The variation of the sliding window size allows the IGUANA framework to adapt to different patient conditions. When a small sliding window size is considered, sudden changes in physiological values are quickly detected as potential risk conditions. However, due to therapy side effects or a very active life style, some patients may be allowed to have quick changes in physiological values without being in danger. In this case, a longer sliding window may smooth the effect of a short, abrupt change in the context of a normal, steady situation.

Sampling Frequency The sampling frequency value directly affects the alarm activation delay. Every measure is assigned a risk level, which can potentially trigger an alarm. Hence, a dangerous situation can be identified within the next measure, which is in a sampling period time. For example, to identify a heart failure soon enough to have good chances of life-saving intervention by an emergency staff, the longest alarm activation delay should be 15 seconds. Longer delay values may be suitable for different purposes. The IGUANA framework is

862

able to easily adapt to diverse sampling frequencies. Since sensor measures may be provided with different frequencies, in this context the adaptability of the IGUANA framework becomes essential. When the sampling frequency is high, even subtle, short anomalies are detected, while a longer sampling period hides some of the sharpest spikes, but correctly identifies the remaining unsafe situations.

Risk Function Weights Risk function weights are among the most important parameters of the framework, because they directly determine the effect of each risk component (slope, offset, and dist) on the computed risk value. Hence, a physician is allowed to customize this setting to the clinical conditions of the patient and to the kind of anomalies to be detected. We report the results of some experiments performed to show the separate effect of the different risk components. All experiments are performed on the same sample dataset. They have no direct medical value, but demonstrate the adaptability of the framework to a wide range of situations. In Figure 4(c) and Figure 4(d) risk evaluation is only based on the offset component (slope and dist weights are set to zero). Risk rises with the distance between the measured value and the moving average. Such setting allows a physician to reveal deviations from a stationary behaviour dynamically evaluated. In this case, positive or negative spikes in the signal time series are identified as dangerous situations. To separately analyze positive and negative contributions of the offset component, the offset weight wo is set to 1 only for positive offset values in Figure 4(c), and only for negative offset values in Figure 4(d). When it is necessary to identify abrupt increases in a given measure, the slope risk component should be considered. This kind of analysis allows a physician to focus on rapid changes in the physiological behaviour of the patient.

Ubiquitous Risk Analysis of Physiological Data

Figure 4. Experimental results for different parameters of the framework

(a) k-means algorithm

(b) hierarchical algorithm, average linkage method

(c) positive offset only (

d) negative offset only

performance In our experiments, we evaluated the performance of the IGUANA framework both for the off-line model building phase and for the on-line classification phase. Model building experiments have been performed on an AMD Athlon64 3200+ PC with 512 Mb main memory, Windows XP Professional operating system and R version 2.1.1. As expected, the k-means algorithm is about 60 times faster than the hierarchical algorithm and shows a better scalability with increasing data cardinality. However, since model creation is performed off-line, the selection of the clustering algorithm has been

based on the quality of generated clusters, rather than performance. With hierarchical clustering algorithms, different methods for computing inter-cluster distance may be adopted. Different distance computation methods show a negligible effect on performance. Hence, again, the selection of the average linkage distance method was driven by cluster quality issues. Models with thousands of measures, generated by means of hierarchical clustering, are created in tens of minutes. Performance of the classification phase is more critical, since this task is performed online. Furthermore, to be able to deliver real-time classification of incoming sensor data, the time requested by the classification of a single measure has to be less than the sampling period of

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the sensors. Since mobile health applications have specific requirements in terms of power consumption and resources, they need to be evaluated directly on the scene. We performed experiments with a mobile version of IGUANA developed for both the Pocket PC and the Smartphone architectures. In Figure 5 a sample screenshot of the mobile application is presented. The instantaneous risk of each monitored vital sign is denoted as a number ranging from 1 to 5 (ABPsys, systolic blood pressure; ABPdias, diastolic blood pressure; HR, heart beat rate; SpO2, peripheral blood oxygen saturation). To the right, the global risk is shown, together with the remaining battery power. Results are promising, since the Smartphone battery proved to last many hours. Memory resources are estimated to be in the order of the hundreds of bytes for the data structures, while the complete application can be run on a 2 Mbyte equipped Smartphone without restrictions. Since each measure requires around 0.5 ms to be processed by the mobile application, real-time measure classification can be performed even at high sampling frequency. These experiments highlight both the adaptability and the efficiency of the proposed approach.

conclusIon In this chapter we have introduced the architecture of a mobile healthcare system to collect and analyse physiological signals for ubiquitous patient monitoring. We reviewed some of the relevant recent literature on health care applications and we proposed a flexible framework to perform the real-time analysis of clinical data collected by means of a body sensor network. We illustrated the effectiveness of our framework by analysing publicly available clinical data, by investigating different physiological signals, and by estimating its performance. An off-line analysis is performed to build a model tailored

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to a specific patient or disease. On-line analysis classifies each measured value with a risk level according to the previous model. Experiments, performed on both personal PCs and mobile devices (smart phones and Pocket PCs), show the adaptability of the proposed approach and its computational efficiency. There are a number of directions in which the proposed framework can be extended. Since our aim was to detect dangerous situations, we have analyzed vital signs, by considering each signal independently. However, physiological signals can be correlated and contribute in different ways to the global risk computation. Hence, a further improvement of the framework will be the analysis of the correlation among different physiological signals which contribute to a global clinical situation.

reFerences Anliker U., et al. (2004). AMON: a wearable multiparameter medical monitoring and alert system. IEEE Transactions on Information Technology in Biomedicine, 9(1), 415–427. Apiletti, D., Baralis, E., Bruno, G., Cerquitelli, T. (2006). IGUANA: Individuation of Global Unsafe ANomalies and Alarm activation. Presented at IEEE IS ’06 - Special Session on Intelligent System for Patient Management. London, (pp 267-272). Axisa, F., Dittimar, A., Delhomme, G. (2003). Smart clothes for the monitoring in real time and conditions of physiological, emotional and sensorial reaction of human. In Proceedings of the 2 Annual International Conference of the IEEE EMBS, (pp. 3744-3747). Branche, P., Mendelson, Y., (2005). Signal Quality and Power Consumption of a New Prototype Reflectance Pulse Oximeter Sensor. Presented at IEEE Northeast Bioengineering Conference,.

Ubiquitous Risk Analysis of Physiological Data

Hoboken, NJ, (pp 42- 43).

Biology Society, Amsterdam, pp 1987-1988.

Cheng, P.-T., Tsai, L.-M., Lu, L.-W., Yang, D.-L., (2004). The design of PDA-based biomedical data processing and analysis for intelligent wearable health monitoring systems. Presented at IEEE International Conference on Computer and Information Technology, (pp 879 - 884).

Sharshar, S., Allart, L., Chambrin, M. C., (2005). A new approach to the abstraction of monitoring data in intensive care. Lecture Notes in Computer Science, 3581, 13–22.

Gupta, S., Ganz, A. (2004). Design considerations and implementation of a cost-effective, portable remote monitoring unit using 3G wireless data networks, presented at IEEE EMBS, San Francisco, CA, USA, pp. 3286-3289. Han, J., Kamber, M., (2000). Data Mining: Concepts and Techniques, Series Editor Morgan Kaufmann Publishers. The Morgan Kaufmann Series in Data Management Systems, Jim Gray. Jones V., et al. (2006). Mobihealth: mobile health services based on body area networks. In Technical Report TR-CTIT-06-37 Centre for Telematics and Information Technology. University of Twente, Enschede. Jovanov, E., Milenkovic A., Otto C., de Groen P. C. (2005). A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2,(6). Lee I., et al. (2006). High-Confidence Medical Device Software and Systems. Computer 39(4), 33-38.

Varady P., Benyo Z., Benyo B. (2002), An open architecture patient monitoring system using standard technologies. IEEE Transactions on Information Technology in Biomedicine, 6(1), .95–98. Varshney, U., (2006). Transmission of emergency messages in wireless patient monitoring: routing and performance evaluation. Presented at IEEE Hawaii International Conference on System Sciences, vol. 5, (pp 91- 100). Wu, H.-C., et al., (1999). A mobile system for real-time patient-monitoring with integrated physiological signal processing. Presented at IEEE BMES/EMBS Joint Conference, vol.2, (pp.712).

KEY TERMS ABPdias: Diastolic Arterial Blood Pressure. Diastole is the period of time when the heart relaxes after contraction. The diastolic pressure is the lowest pressure in the cardiac cycle (i.e., in the relaxing). A typical value for a healthy adult human is approximately 80 mmHg.

Lorincz, K., et al. (2004). Sensor networks for emergency response: Challenges and opportunities. IEEE Pervasive Computing, 3(4), 16-23.

ABPsys: Systolic Arterial Blood Pressure. Systole is the contraction of the chambers of the heart, driving blood out of the chambers. The systolic arterial blood pressure is defined as the peak pressure in the arteries, which occurs near the beginning of the cardiac cycle (i.e., in the contraction). A typical value for a healthy adult human is approximately 120 mmHg.

Manders, E., Dawant, B. (1996). Data acquisition for an intelligent bedside monitoring system. Presented at IEEE Engineering in Medicine and

Bluetooth: Radio standard and communications protocol (i.e., IEEE 802.15.1 standard) for wireless personal area networks characterized

Lin, Y.-H., et al. (2004), A Wireless PDA-Based Physiological Monitoring System for Patient Transport. IEEE Transactions on Information Technology in Biomedicine, 8(4), 439-447.

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by short transmission ranges (1-100 m). It has been designed for low power consumption and it is based on low-cost transceiver microchips in each device. Global System for Mobile Communications Gsm (Originally from Groupe Spécial Mobile):. It is the most popular standard for mobile phones in the world. GSM is a cellular network, which means that mobile phones connect to it by searching for cells in the immediate vicinity. GSM networks operate in four different frequency ranges and use a variety of voice codecs. Heart Rate (HR): The heart rate describes the frequency of the cardiac cycle. It is a vital sign and usually it is calculated as the number of contractions (heart beats) of the heart in one minute and expressed as “beats per minute” (bpm). When resting, the adult human heart beats at about 70 bpm (males) and 75 bpm (females), but this rate varies among people. Personal Digital Assistant (PDA): It is a handheld computer, also known as pocket or palmtop computer. A typical PDA has a touch screen for data entry, a memory card slot for data storage, IrDA and USB ports for connectivity. Wi-Fi and Bluetooth are often integrated in newer PDAs. Smartphone: It is a full-featured mobile phone

with personal computer like functionalities. Applications for enhanced data processing and connectivity can be installed on the device, and may be developed by the user. Smart functionalities may include miniature keyboard, touch screen, operating system, and modem capabilities. Saturation of Peripheral Oxygen (SpO2):. It measures the percentage of haemoglobin binding sites in the bloodstream occupied by oxygen. It is usually evaluated by a non-invasive pulse oximeter, which relies on the light absorption characteristics of saturated haemoglobin to give an indication of oxygen saturation. Wireless Local Area Network (WLAN): It is a network which uses a modulation technology based on radio waves to enable communication among devices in a limited area, without using wires. Among benefits of WLAN there are: convenience, cost efficiency, mobility, expandability, and ease of integration with other networks and network components. ZigBee: Name of a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 802.15.4 standard for wireless personal area networks. The ZigBee 1.0 specification was ratified on December 14, 2004 and is available to

This work was previously published in Handbook of Research on Distributed Medical Informatics and E-Health , edited by J. Wang, pp. 478-492, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 4.9

RFID in Healthcare: A Framework of Uses and Opportunities Nebil Buyurgan University of Arkansas, USA Bill C. Hardgrave University of Arkansas, USA Janice Lo Baylor University, USA Ronald T. Walker University of Arkansas, USA

abstract With its potential and unique uses, healthcare is one of the major sectors where radio frequency identification (RFID) is being considered and adopted. Improving the healthcare supply chain, patient safety, and monitoring of critical processes are some of the key drivers that motivate healthcare industry participants to invest in this technology. Many forward-looking healthcare organizations have put the potential of RFID into practice and are realizing the benefits of it. This study examines these empirical applications and provides a framework of current RFID deployment in the healthcare industry and opportunities

for continued deployment. This framework also presents a categorical analysis of the benefits that have been observed by the healthcare industry. In addition, major implementation challenges are discussed. The framework suggests asset management, inventory management, authenticity management, identity management, and process management are the broad areas in which RFID adoptions can be categorized.

IntroductIon Although RFID has been around for more than 50 years, it has only recently received much atten-

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

RFID in Healthcare

tion due to the very well publicized and promoted mandates by Wal-Mart and the United States Department of Defense for its use in their supply chains (Jervis, 2005). This increased awareness of the technology has resulted in various uses in a variety of industries, well beyond the niche applications it has enjoyed for the past 50 years. As one extension of its use, RFID has started to emerge as a major technology in the healthcare industry. RFID has some compelling advantages that make it particularly attractive for healthcare including robustness, unobtrusiveness, ease of use, and value proposition (Garfinkel and Rosenberg, 2006). The Food and Drug Administration (FDA) of the Department of Health and Human Services (HHS) recommended using RFID on all drugs at the unit level by 2007 to prevent drug counterfeiting (Wicks et al., 2007). In addition, a number of pilot projects have proven to improve the quality of care and reduce costs. Furthermore, these pilot programs have shown that RFID applications have unquantifiable benefits that include saving lives, preventing injuries, and reducing medical errors. Since the healthcare market’s consumption of RFID services is expected to increase more than 23 times, from $90 million in 2006 to $2.1 billion in 2016, it makes sense to take a closer look at the current status to see how RFID is being used in the industry (Harrop, 2006). As RFID technology becomes cheaper and more reliable, the next topic that needs to be discussed is how to strategically implement RFID into healthcare operations. This study provides an overview of current RFID technology deployments in the healthcare industry and the potential opportunities for expanding them. In addition to its short-term benefits and long-term payoffs, this study also discusses main implementation challenges faced. A categorization framework of RFID uses and opportunities are introduced that suggest five empirical application areas: asset management, inventory management, authenticity management, identity management, and process management. Representative applications in these areas are

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given to provide good insight into the uses and potential uses of RFID in healthcare. The paper is structured as follows. First, a brief background on the technology and an overview of the healthcare participants are provided, followed by an analysis of the current uses of RFID in healthcare. Then, the opportunities for RFID in healthcare are discussed, followed by the major challenges RFID currently faces in the healthcare industry.

rFId technology BACKGRoUND RFID is a data collection, acquisition, and storage technology, which uses radio waves to automatically identify individual items and provide realtime information. The goal of any RFID system is to carry data in suitable transponders, generally known as tags, and to retrieve data at a suitable time and place to satisfy particular application needs (Finkenzeller, 2003). RFID was originally developed for and used in military applications, such as in World War II where it was used to identify friendly aircraft (Jervis, 2006). In the late 1960s, it found its way into the retail industry with the intent of creating electronic article surveillance products to fight shoplifters. It was in the 1970s that the first commercial application of the technology was introduced in vehicle tracking (Shepard, 2005). Typically, RFID systems consist of three components (Keskilammi et al., 2003): (1) a small electronic data-carrying device called a transponder (also called a tag); (2) a reader that communicates with the transponder; and (3) a data processing system that contains information about the tag-carrying item. There are two types of RFID systems: active systems and passive systems. The tags in active systems are powered by an internal battery while the ones in passive systems derive the power to operate exclusively from the field generated by the radiation emitted

RFID in Healthcare

by the reader. Active tags are generally bigger than passive tags. In addition, they also have larger storage capacity and transmit continuously or at a reader’s request (Jervis, 2006). According to new research by IDTechEx, the healthcare industry represents only 3% of the active RFID market worldwide (Monegain, 2007). Passive tags, on the other hand, generally do not transmit unless they are interrogated by a reader. They are smaller and less expensive and do not depend on a power source. However, passive tags have a shorter read range than active tags. In a basic passive RFID system, the following general operating procedures take place: the reader generates an electromagnetic field to supply a voltage that is rectified inside the tag; the reader transmits information to the tag by modulating the carrier wave; finally, the tag back-scatters the carrier wave by modifying its own impedance to transmit information back to the reader. Read ranges depend on several factors, such as frequency, the power of readers, environmental factors, and material interventions (Jervis, 2006). The recent increase in RFID utilization is due in part to the rapidly declining cost of passive RFID tags, which are cheaper than active RFID tags (Jervis, 2006). Although today’s tags contain a

silicon chip for data storage, there is a slow trend towards chipless RFID tags which will drive down costs even more (Harrop, 2006). Table 1 summarizes the main differences between active and passive RFID systems. Currently, RFID tags may operate at several different frequency bands, including low, high, ultra-high, and microwave. Low frequency ranges between 125 kHz and 134.2 kHz; high frequency is set at 13.56 MHz; ultra-high frequency ranges between 860 MHz and 960 MHz; and microwave frequency is set at both 2.45 GHz (same frequency used by Bluetooth and WiFi) and 5.8 GHz (Jervis, 2006; Das, 2006). Frequency directly affects the read distances and response times (or transfer rates) of RFID tags. Generally, lower frequencies have shorter read ranges and slower response times. Higher frequencies have longer read ranges and faster response times. Typically, for passive RFID, low frequency (LF) tags can be read within 30 centimeters; high frequency (HF) tags can be read from 1 to 2 meters; and ultra-high frequency (UHF) tags can be read up to 10 meters (Philips Semiconductors et al., 2004). Although higher frequencies have the advantage of longer read ranges and faster response times, they have less advantage when it comes to inter-

Table 1. Differences between active and passive RFID systems Active RFID

Passive RFID

Internal to tag

Energy transferred from reader via RF

Tag Battery

Yes

No

Availability of Tag Power

Continuous

Only within field of reader

Communication

Long Range (100+ meters), networking of tags & readers

Short Range (0.4. Therefore, the variables are well explained by the two extracted factors. Each of the two attitude factors consists of a balanced ratio of items, measuring both the

Figure 2. Factor analysis for attitude toward RFID technology: Results (extracted factors, factor loadings, and item variance) Item

Var.

In my opinion, RFID technology is a good idea.

v14

Factor 1

2

Expl. Variance

.735

I am enthusiastic about RFID.

v13

.720

RFID technology appalls me.

v11

-.672

RFID technology has many advantages for consumers.

v15

.662

RFID technology depresses me.

v16

-.638

RFID technology is fun.

v10

.576

I cannot imagine that this technology works reliably.

v12

.672

I think the use of RFID technology is not simple.

v17

.596

RFID technology increases the costs and consumers have to pay them.

v09

*

30.30%

*

42.48% *

*Excluded variables from the analysis (factor loadings < 0.4)

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Consumer Attitudes toward RFID Usage

affective and the cognitive component of attitude. Hence, the proposed attitude definition can be used to describe attitude toward RFID tagged products as well. To examine the extent to which the items used can represent the constructs that needed to be operationalized, the measuring instrument is evaluated based on its convergence validity. Convergence validity measures the degree of conformance between the constructs and their respective measurements. With this objective, the coefficient alpha is computed. This coefficient provides information on how well the sum of single answers can be condensed to obtain a total tendency, whereby the maximum alpha value is one. The larger Cronbach’s Alpha is, the better the validity of the entire scale. For this analysis, alpha values of 0.05 for ARFID and of 0.51 for AP were computed. For a compound scale to be regarded as sufficiently reliable, a minimum Alpha value of 0.7 is often required in the literature (Brosius, 2002). Hence, the Alpha values computed for both attitude factors are not satisfactory. The scales used therefore have only limited reliability. This suggests that the used items apparently do not sufficiently measure the attitude constructs. The limited construct validity should be examined

again in a second study by means of an improved item scale.

suMMary oF results As can be seen from the results that are included in Figures 1 and 2, two factors with eigenvalues greater than one could be extracted from the original variables. Contrary to the first definition, both attitude constructs are represented by two factors. Altogether, the factors obtained explain the variables used to a good degree, since satisfactory factor variances of 42.47% and 56.74%, were achieved respectively. We can therefore conclude that both consumer attitudes toward RFID technology (ARFID) in general, and toward products labeled with RFID tags (AP) are determined by affective and cognitive components concerning RFID. The results obtained here should, however, be observed critically due to the rather small convergence validity. However, this was the first survey attempting to determine and quantify how attitudes toward RFID are formed. The items pooled from related literature were not adequate to yield good results. Hence, further items need to be developed and tested in the future.

Figure 3. Factor analysis for attitude toward products with RFID tags: Results (extracted factors, factor loadings, and item variance) Item

1102

Var.

Factor 1

I am enthusiastic about products with RFID tags.

v26

.929

Products with RFID tags are fun.

v21

.768

RFID tagged products have many advantages for consumers.

v27

.674

I think the usefulness of products with RFID tags is greater than the usefulness of products with bar codes.

v20

.547

2

Expl. Variance

29.65%

I think using RFID tags instead of bar codes is a good idea.

v25

.775

Products with RFID tags disgust me.

v24

-.691

I think products with RFID tags make shopping easier.

v23

.644

Products with RFID tags depress me.

v22

-.598

56.74%

Consumer Attitudes toward RFID Usage

Figure 4. Overall evaluation of RFID All in all…

Variable

N

Mean value

Variance

…RFID technology appeals to me.

v45

346

4.26

2.716

…products with RFID tags appeal to me.

v46

346

3.87

2.453

Despite these constraints, the survey provided new insights into the dimensionality of RFID. Firstly, the attitude toward RFID technology is characterized by two factors that measured the emotions and the cognitions separately. From this it can be concluded that the attitude toward RFID technology of the consumers surveyed cannot be described by a single factor consisting of both affective and cognitive components, but two individual factors exist each of them describing one component. Secondly, as the study context is general, critics may argue that there could be differences in attitudes depending on the nature of the product. In order to find out which product or product group respondents had in mind during the survey, an open-ended question was asked at the end of the survey. Most of the consumers (55%) thought of food products. A future study should investigate the attitude toward RFID for specific products or product categories. Thirdly, at the end of the survey, consumers were asked for an overall evaluation of the RFID technology in general, and of RFID tagged products (Figure 4). The evaluation of products with RFID tags scored lowest with a mean value of 3.87 (again measured on a seven point scale: 1= strongly disagree, 7 = strongly agree). It is possible that consumers did not know the real difference between today’s products with bar codes and future RFID tagged products. Furthermore, the answers spread widely around the mean value, as indicated by the high variance (2.453), also an indicator of the uncertainty of consumers about this topic.

resuMe and Future challenges What implications do these findings have for the management and marketing of RFID technology and RFID tagged products? Companies who plan to use some means of RFID technology in direct contact with customers still have the possibility to emphasize consumer benefits, explain critical aspects, and invalidate typical criticisms. Consumer attitudes are mixed and mostly formed based on emotions; this finding possibly indicates that most consumers do not yet have a clear opinion about RFID due to a lack of knowledge. This implies a need to educate consumers about RFID. Companies could benefit, as consumers may favor buying RFID-enhanced products if they were to know more about the advantages of this technology. For RFID to evoke positive attitudes in consumers, information and advertising should involve positive emotions, as results suggest that attitudes toward RFID are based separately on emotions and cognitions. Companies need to address the cognitive attitude component as well, since little is known by the average consumer today. Nevertheless, consumers are starting to learn more about RFID. The information, however, seems to be rather diffused and not necessarily clear both in terms of context and content. Therefore, objective information is required. As print media are most commonly used by consumers to obtain information on this topic (30.5% of all survey respondents having heard of RFID obtained this information from print media), ef-

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Consumer Attitudes toward RFID Usage

forts to educate consumers should concentrate on objective press coverage. In summary, the influences of this developing technology on consumer behavior have rarely been considered as a field of research. Thus, few studies are available currently. This short survey summary has provided some interesting insights into consumer attitudes as well as input on how and where to continue research in the future. RFID technology ought to be explored in more detail as it is advancing rapidly and becoming more visible in every day life. Analyzing the further developments of consumer attitudes and behavior toward this new technology should be an interesting research topic.

reFerences Balderjahn, I. (1995). Einstellungen und einstellungsmessung. In B. Tietz, R. Köhler, & J. Zentes (Eds.), Handwörterbuch des marketing (pp. 542554). Stuttgart: Schäffer-Poeschel Verlag. Böhmer, R., Brück, M., & Rees, J. (2005). Funkchips. Wirtschaftswoche, 3, 38-44. Brosius, F. (2002). SPSS 11. Bonn: mitp-Verlag. Capgemini. (2004). RFID and consumers: Understanding their mindset. Retrieved June 29, 2004, from http://www.nrf.com/download/ NewRFID_NRF.pdf Capgemini. (2005). RFID and consumers: What European consumers think about radio frequency identification and the implications for business, Retrieved May 21, 2005, from http://www. de.capgemini.com/servlet/PB/show/1567889/ Capgemini_European_RFID_report.pdf Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1002.

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Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading: Addison-Wesley. Günther, O., & Spiekermann, S. (2005). RFID and the perception of control: The consumer’s view. Communication of the ACM, 48(9), 73-76. Inaba, T., & Schuster, E. W. (2005). Meeting the FDA’s initiative for protecting the U.S. drug supply. American Pharmaceutical Outsourcing Journal, forthcoming. Juban, R. L., & Wyld, D. C. (2004). Would you like chips with that? Consumer perspective of RFID. Management Research News, 27(11/12), 29-44. Kotler, P. (2003). Marketing management (11th ed.). Upper Saddle River, NJ: Prentice Hall. Kroeber-Riel, W., & Weinberg, P. (2003). Konsumentenverhalten (8th ed.). München: Vahlen. Metro Group. (2004). RFID: Uncovering the value. Applying RFID within the retail and consumer package goods value chain. Düsseldorf: Author. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. Scharfeld, T. A. (2001). An analysis of the fundamental constraints on low cost passive radio-frequency identification system design. Cambridge, MA: MIT. Solomon, M., Marshall, G., & Stuart, E. (2004). Marketing. Real people, real choices (4th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Spiekermann, S., & Ziekow, H. (2006). RFID: A systematic analysis of privacy threats & a 7-point plan to address them. Journal of Information System Security, 1(3), forthcoming.

Consumer Attitudes toward RFID Usage

Strassner, M., Plenge, C., & Stroh, S. (2005). Potenziale der RFID-technologie für das supply chain management in der automobilindustrie. In E. Fleisch & F. Mattern (Eds.), Das Internet der dinge—Ubiquitous computing und RFID in der praxis (pp. 177-196). Berlin: Springer. Thompson, R., Higgins, C., & Howell, J. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143. Trommsdorff, V. (2004). Konsumentenverhalten (6th ed.). Stuttgart: Kohlhammer. Wilkie, W. (1994). Consumer behavior (3rd ed.). New York: John Wiley and Sons.

KEY TERMS Affect: Reflects feelings regarding the attitude object and refers to the overall emotional response of a person toward the stimulus. Attitude: Relatively permanent and long-term willingness to react in a consistently cognitive, affective, and conative way.

Behavior: Actions or reactions of an organism in relation to the environment. Cognition: Knowledge or beliefs the person has about the attitude object and its important characteristics. Optical Character Recognition (OCR): Involves computer software designed to translate images of typewritten text into machine-editable text, or to translate pictures of characters into a standard encoding scheme. Radio Frequency Identification (RFID): A radio-supported identification technology typically operating by saving a serial number on a radio transponder that contains a microchip for data storage. RFID Tag: Transponder carrying information usually attached to products that will generate a reply signal upon proper electronic interrogation sending the relevant information. Stimulus: In psychology, anything effectively impinging upon any sense, including internal and external physical phenomena, the result of which is a response.

This work was previously published in Encyclopedia of Multimedia Technology and Networking, Second Editon, edited by M. Pagani, pp. 247-253, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 5.10

Determinants of User Acceptance for RFID Ticketing Systems Dimitrios C. Karaiskos Athens University of Business and Economics, Greece Panayiotis E. Kourouthanassis Athens University of Business and Economics, Greece

abstract

IntroductIon

RFID ticketing systems constitute a particular type of pervasive information systems providing spectators of sports events with a transparent mechanism to validate and renew tickets. This study seeks to investigate the factors that influence user acceptance of RFID ticketing systems. The theoretical background of the study was drawn from the technology acceptance model (TAM) and the innovation diffusion theory (IDT), and enhanced with factors related to privacy and switching cost features. The research model was tested with data gathered through a lab experiment (N=71). The participants perceived the system as useful and easy to use, and expressed the willingness to adopt it should it become commercially available. Moreover, the results of ANOVA tests suggest that the age and education of users influence their perception towards the usefulness of the system and its subsequent use.

The advent of mobile and wireless technologies such as Wi-Fi, ZigBee (Geer, 2006), and RFID (Smith & Konsynski, 2003) have inspired new research fields that challenge our existing view of Information Systems (IS) and their use by envisioning new ways of interacting with them away from the boundaries imposed by the desktop computer. The gradual miniaturisation of electronic components, the massive reduction of their production and operation costs, and their ability to communicate wirelessly, contributed to the design and development of systems that are capable of being embedded in objects, places, and even people (Roussos, 2006). Information Systems scholars have named this new phenomenon using such terms as nomadic computing (Lyytinen & Yoo, 2002), ubiquitous computing (Weiser, 1993), and pervasive computing (Saha & Mukherjee, 2003).

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Determinants of User Acceptance for RFID Ticketing Systems

These terms share the common denominator that Information Technology pervades the physical space, operates in the periphery of humans’ world, and supports a variety of applications and services in a context-aware and passive manner. Birnbaum (1997) identified these novel characteristics in the IS discipline by defining a new IS class entitled pervasive information systems (pervasive IS). Pervasive IS may support both personal and business activities. Kourouthanassis and Giaglis (2006) provide a taxonomy of pervasive IS and their features by identifying four pertinent application types--personal, domestic, corporate, and public. Personal pervasive IS rely on wearable hardware elements to provide a fully functional computing experience on the direct periphery of the user. Typical examples include biomedical monitoring systems (Jafari, Dabiri, Brisk, & Sarrafzadeh, 2005), human detection systems (Smith et al., 2005), and remote plant operation systems (Najjar, Thompson, & Ockerman, 1997). Domestic pervasive IS primarily automate tasks that otherwise require human supervision in the household (e.g., heating and lightning control, monitoring the home inventory, etc.). Typical examples include MIT’s Home of the Future initiative (Intille, 2002) and the Aware Home (Kidd et al., 1999). Corporate pervasive IS may support enterprise-wide activities, such as supply chain management (e.g., warehouse management (Prater, Frazier, & Reyes, 2005)), workforce management (e.g., sales force automation (Walters, 2001)) and office support (Churchill, Nelson, & Denoue, 2003; Greenberg & Rounding, 2001), and customer relationship management (Kourouthanassis, 2004). Finally, public pervasive IS may provide interactive environments in public places. Examples include wireless museum guides (Hsin & Liu, 2006) and mobile information devices in hospitals (Xiao, Lasome, Moss, Mackenzie, & Faraj, 2001) to name a few popular applications. RFID ticketing systems fall under the umbrella of public pervasive IS by providing spectators

of sports events with a technology-augmented method for renewing and validating their tickets. The underlying technology is radio-frequency identification (RFID) which is a generic term for technologies that use radio waves to automatically identify people or objects. The identification process involves the storage of a unique serial number to an RFID tag comprised of a microchip and an antenna. The antenna enables the chip to transmit the identification information to an RFID Reader. The reader converts the radio waves reflected back from the RFID tag into digital information that can then be passed on to computers that can make use of it. RFID technology already has been incorporated in sports tickets over the past few years with the most notable deployment being during the 2006 FIFA World Cup (Schmidt & Hanloser, 2006). The successful paradigm also has been followed by numerous football clubs in the U.K. such as Fulham, Coventry City, Manchester City, Reading, and Wigan. The new ticketing scheme promises to bring the advantages of RFID technology to the sports events arena. Tickets incorporating the RFID technology have added value credited to the technical capabilities of RFID which are wireless connectivity, persistent memory, and computing power. In particular, a RFID-enabled ticket contains a microprocessor which allows encryption methods to be applied in order to be uniquely authenticated, thus, discouraging incidents of forgery, counterfeiting, and replication. Furthermore, the RFID-enabled ticket has the ability to store data regarding service details and owner’s personal data, making possible the unique identification of the owner and the provision of added value services to him. Also, the wireless connectivity of the ticket allows its owner to pass control gates faster and to be more easily located for security reasons. Although RFID tickets represent an excellent balance between cost, security, and access control, issues of reliability and durability have been received with scepticism due to their feature of

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Determinants of User Acceptance for RFID Ticketing Systems

Table 1. Technology-based ticketing systems Technology Magnetic Stripe Ticketing

Functionality

Application Area

Advantages

Disadvantages

access privileges and fare remaining are included and magnetically recoded after each use

commonly used to sports events and public transportation

technologically simple and inexpensive to produce

relatively small amount of information and may be easily read and copied

commonly used to airline flights and entertainment venues

cheap and efficient method, 24/7 accessibility

requires customers who know how to use computers and how to access the internet

commonly used to parking lots, toll collection, and public transportation

cheap and efficient method

not a familiar method to the public

commonly used to sports events, public transportation and entertainment venues

programmable medium capable to offer advanced real time ticketing services (such as discounts, offers etc.)

expensive, requires sophisticated infrastructure and hinders security implications

commonly used to sports events, public transportation and entertainment venues

programmable medium capable to offer advanced real time ticketing services (such as discounts, offers etc.), time saving transactions

expensive, requires sophisticated infrastructure and hinders security implications

tickets are reserved or bought through a website (internet) or by telephone Electronic Ticketing

once a reservation is made an e-ticket exists only as a digital record in the computers customers usually print out a copy of their receipt which contains the record locator or reservation number and the e-ticket number mobile ticketing consider the mobile phone as the payment device

Mobile Ticketing

the reservation and actual payment may occur through exchanging an SMS message or submitting a request through the mobile network

Contact Smart Cards

an embedded microprocessor and memory allows to save and manage access privileges and fare remaining

Contactless Smart Cards

an embedded microprocessor and memory allows to save and manage access privileges and fare remaining, an embedded antenna gives the ability to communicate wirelessly with the infrastructure

uniquely identifying individuals and, indirectly, extrapolating their location; this raises privacy concerns for spectators. Drawing from the aforementioned challenges, this chapter aims at identifying the factors that affect spectators’ acceptance of RFID ticketing systems. To meet this objective, we have developed an integrated user acceptance framework that draws measurement variables from the disciplines of psychology and information systems. Although IS evaluation has been one of the most popular research topics with several models or frameworks attempting to explain the adoption and use of IT services (e.g., (Goodhue & Thompson,

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1995; Taylor & Todd, 1995; Venkatesh, Morris, Davis, & Davis, 2003)), the properties of RFID ticketing systems in terms of wireless interaction modalities for users and prospective security and/ or privacy issues led us to enhance the existing user acceptance frameworks with factors addressing the aforementioned matters. The framework has been empirically validated through the design and implementation of a prototype RFID ticketing system and the execution of a lab experiment. The chapter initially presents the architecture and design rationale of the RFID ticketing system in Section 2 (RFID Ticketing System). Section 3 (Research Model and Method) presents the

Determinants of User Acceptance for RFID Ticketing Systems

Figure 1. RFID ticketing system architecture

Figure 2. Registration kiosk: customer data form and registration’s confirmation screen

Figure 3. Ticket kiosk: welcome and PIN code authentication screen

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Determinants of User Acceptance for RFID Ticketing Systems

development of an appropriate measurement model to assess the acceptance of the system and the method used to acquire empirical data. Sections 4 (Results) and 5 (Discussion) address the results of the study, while section 6 provides a discussion and concluding remarks that identify the pertinent theoretical and practical implications of the proposed solution.

RFID TICKETING SYSTEM overview of Technologies and assessment To select the appropriate underlying technology for the RFID ticketing system, we scrutinized the established practices followed by similar systems. Electronic ticketing is today a commodity in areas like entertainment (cinemas, theaters, and amusement parks to name a few popular application areas), sport events, public transportation, parking lots, automotive toll collections, and airline control. The technologies most commonly used are magnetic stripe tickets/cards, electronic tickets via internet, mobile ticketing systems, and smart cards. The advantages of these systems are cashless and queue-less transactions, flexible payments (e.g., by sending an SMS), reduction in operating and maintenance costs due to little or no manual intervention, accurate access control

resulting in a reduced number of fare dodgers and in anti-counterfeiting, and efficient cost accounting with the potential of offering variable ticket pricing. Table 1, provides a summary of these approaches. A comparative evaluation on the available technological solutions suggests that smart cards, and in particular contactless smart cards, are the prime candidates to support the interaction modalities of the proposed solution. Contactless smart cards can be used to automate the process of purchasing and validating tickets because of their inherent capabilities. They are typically credit card sized with an embedded microprocessor, memory, and an antenna providing them with the ability of wireless communication; thus, physical contact with a card reader is not required. Instead, the reader gains access to the card’s data by emitting radio signals that are received by the card’s built in antenna, thus facilitating two-way information exchange with the card’s memory. This can include validation procedures, access controls, writing, and reading of the memory. Furthermore, such cards can be powered by induction using the reader, which extends the useful battery life of the card or does not require a battery at all (passive contactless smart cards). The most common technology that drives the ticketing solutions that are based in contactless smart cards is Radio Frequency Identification Technology (RFID).

Figure 4. Gate kiosk: welcome and PIN code authentication screen

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Determinants of User Acceptance for RFID Ticketing Systems

RFID Ticketing System: Rationale and Architecture In our case, we propose a RFID-enabled ticketing system, henceforth referred to as SEAT that can be employed to manage ticketing procedures in any type of athletic event. The main innovation of the proposed approach is the incorporation of RFID technology to a traditional smart card. A prototype version of the system has been implemented and tested through a lab experiment. The SEAT ticketing system manages all operations related to issuing and selling tickets, and also controlling access for athletic events hosted in stadiums. The system functionality is based on a RFID-enabled personal debit card (contactless smart card). The RFID card enables fans to buy tickets from properly equipped automated ticket kiosks and to enter the stadium through gates that provide automated access control. SEAT automates these processes resulting in decreased time and effort in buying tickets and passing through stadium gates while, at the same time, providing secure and effective access control. Furthermore, the system offers value-added services in the form of cashless transactions in the stadium, real time traffic information for the sport club, and personalized services to fans. SEAT follows a 3-tier architecture based on distributed systems principles (Tanenbaum & Van Steen, 2003), supporting wide geographic dispersion of the system resources, independent modules providing the services, and central data storage and data access. The system consists of four distinct components: 1.

2.

A RFID contactless smart card that stores important information regarding remaining price units and stadium access rights (operating at 13.56 MHz and supporting ICODE 1, 48 bytes read/write memory) A RFID reader (connected to the workstation) that reads/writes the RFID card and communicates all the information from and

3.

4.

to the workstation (13.56 MHz frequency, supports ICODE 1, read/write capability, RS232 connectivity) Workstations (personal computers or laptops) that host a software module relative to the service provided (registration, kiosk, access control) A server that manages the database read/ write access and the reader’s (identification)

Figure 1 illustrates the system architecture. In a typical usage scenario, a spectator of the athletic event issues a RFID contactless smart card from the registration kiosk (a one time registration) and charges it with a specific amount (see Figure 2). The spectator can then go to a ticket kiosk and purchase a ticket for the event of his preference by swiping or placing the RFID card near the reader that is attached to the ticket kiosk. For security and authentication purposes a PIN number is requested (see Figure 3). Finally, the spectator can enter the stadium by swiping or placing the RFID card near the reader that is attached to the gate’s turnstile. One more PIN number is requested for security and authentication purposes (see Figure 4). All system components have been developed following the .NET framework. Interfaces with the RFID reader have been developed using the corresponding SDK, and have been programmed in VB.NET. To measure the perceived acceptance of the proposed solution, we performed a lab experiment in which a representative set of spectators of athletic events where invited to use and assess the implemented prototype. Drawing from established theories that measure IS acceptance we developed an integrated research model and a set of corresponding hypotheses. The following section discusses the rationale and development of our research model.

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Determinants of User Acceptance for RFID Ticketing Systems

research Model and Method Predicting an individual’s intention to use an information system has been widely studied under the technology acceptance research stream in IS literature (e.g., Davis, 1989; Hartwick & Barki, 1994; Venkatesh, Morris, Davis, & Davis, 2003). Researchers in this area have adopted theories that predict human behavior and have tailored them to the IS domain to predict the usage of a particular Information Technology (IT) or System. Within the IS literature, these ideas have taken shape in the form of various models following the conceptual framework that individuals’ reaction to using a particular technology may influence their intention of actually using that technology, as illustrated in Figure 5. The dominant model in the IT domain is the technology acceptance model (TAM) (Davis, 1989), which contends that behavioral intention to use an IS is contingent on two salient beliefs, namely perceived usefulness and ease of use. Moreover, TAM posits that perceived usefulness and perceived ease of use determine an individual’s intention to use a system, with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use. Thus, TAM uses questions in order to collect an individual’s perceptions about the usefulness and the ease of use for a specific system, factors that affect the individual’s intention to use the system, therefore, his acceptance to actually use it. TAM has been widely used in technology acceptance studies and has provided rich empirical

evidence of individuals’ acceptance of technology. However, there is an ongoing debate as to whether the parsimonious TAM is explanatory enough, or whether additional factors should be included in the model to obtain a richer explanation of technology adoption and use (e.g., Mathieson, 1991; Plouffe, Hulland, & Vanderbosch, 2001; Taylor & Todd, 1995; Venkatesh, Morris, Davis, & Davis, 2003). Many researchers suggested that TAM needs to be enhanced with additional variables to provide an even stronger model (Kenneth, Kozar, & Larsen, 2003; Legris, Inghamb, & Collerette, 2003), especially when applied in contexts that are beyond the traditional workplace (Bruner & Kumar, 2005; Heijden, 2004; Weiser & Brown, 1996) as in the case of RFID ticketing systems. For example, TAM has been modified to accommodate the new properties of wireless business environments, incorporating perceived playfulness and security as antecedents of intended system use, and task type as moderator to the aforementioned relationships (Fang, Chan, Brzezinski, & Xu, 2006). Similarly, Hung et al. (2003) extended TAM with factors from the Theory of Planned Behaviour (Ajzen, 1991) and Innovation Diffusion Theory (Rogers, 2003) to predict WAP services adoption. Finally, Cheong and Park (2005) suggested that perceived system quality, contents quality, and Internet experience may be used as predictors of the original salient beliefs of TAM, namely perceived ease of use and usefulness. They applied and validated the proposed model to assess mobile Internet acceptance in Korea.

Figure 5. Basic concept underlying technology acceptance research stream (Venkatesh, Morris, Davis, & Davis, 2003)

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Determinants of User Acceptance for RFID Ticketing Systems

In our case, we chose to augment the technology acceptance model with issues related to innovation adoption, privacy and switching cost features, as shown in Figure 6, in order to predict behavioural intention to use the system. The proposed system provides an alternative approach of purchasing tickets of sports events. Current practices suggest that a ticket to an athletic event may be purchased either physically or through electronic means (regardless of whether it is through the Internet or another medium--e.g., via phone reservation). Therefore, we considered it important to include in our model the compatibility factor from the diffusion of innovations theory (Rogers, 1995). Moreover, since the proposed system employs automated monetary transactions via the RFID smart card, we needed to examine risk issues that might affect an individual’s perception of the system’s use. RFID technology has already raised many privacy fears as it is perceived that it would be used to track, identify, and acquire personal information in an intrusive way (Garfinkel, Juels, & Pappu, 2005; Ohkubo, Suzuki, & Kinoshita,

2005). To this end, people fear that commercial companies will use RFID to profile individuals and perform direct marketing activities based on that information. To predict the adoption of the RFID ticketing system, we need to take into consideration the perceived risk that might negatively affect an individual’s decision to adopt and use the system. Finally, because there are several competing approaches to RFID ticketing schemes ranging from season tickets, to phone reservations, and e-ticketing purchases, we can expect that their attractiveness may have a negative impact on a spectator’s intention to accept the proposed service. We included the attractiveness of alternatives factor in our integrated research model. A detailed discussion on the formulation of the proposed research model and the resulting research hypotheses is available in Karaiskos et al. (2007). To test and validate the research model, we conducted a lab experiment. The lab experiment lasted for one week and subjects were invited via e-mail. The experiment participants were given a

Figure 6. Proposed research model

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Determinants of User Acceptance for RFID Ticketing Systems

Table 2. Sample’s descriptive statistics (N=71) Gender

Age

Men

70.4%

Elementary School

0.%

Women

29.6%

Junior High/Middle School

19.7%

50

0. %

Education

Table 3. First stage linear regression analysis (pu dependent variable) Model

R2

Adjusted R2

Change in R2

F

Sig. of F

Change in F

Sig. of change in F

,614

,619

112,195

,000

112,195

,000

Beta

t

Sig.

2,661

,010

10,592

,000

Model Summary 1

,619

Predictors: (Constant), PEOU Dependent Variable: PU Coefficients 1

Variable

B

S.E. B

(Constant)

,795

,299

PEOU

,779

,074

,787

Dependent Variable: PU

demonstration of the system’s functionality and were then prompted to use it following specific usage scenarios. These scenarios involved the issuing of the smart card, purchasing tickets for specific games, and entering the stadium (virtually) by using the RFID card. Each session lasted 30 minutes. After each session, participants were asked to complete a questionnaire, which included a set of items concerning the constructs of the proposed research model. The items were drawn from relevant studies and were measured following a Likert scale from 1(totally disagree) to 5(totally agree). The questionnaire was divided into two parts. The first part measured the perceived acceptance of the system while the second part collected the demographic details of the participants. The sequence of measurement items in the questionnaire

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was randomized to ensure internal consistency (Cook & Campbell, 1979) and construct validity (Karahanna, Straub, & Chervany, 1999). Seventy eight (78) individuals participated in the lab experiment voluntarily, out of which 71 questionnaires where found to be consistent. The sample’s descriptive statistics are presented in Table 2.

results The research model was found to satisfy content and construct validity as indicated by Straub et al. (1999) and appears in Karaiskos et al. (2007). Furthermore, in order to assess the measurement model, we performed linear regression analysis in two stages. The first stage considered perceived

Determinants of User Acceptance for RFID Ticketing Systems

Table 4. Second Stage Linear Regression Analysis (BI dependent variable) Model

R2

Adjusted R2

Change in R2

1

,636

,630

,636

F

Sig. of F

Change in F

Sig. of change in F

,000

120,411

,000

Models Summary 120,411

Predictors: (Constant), PU, PR, AA, PEOU Dependent Variable: BI Coefficients 1

Variable

B

S.E. B

(Constant)

,414

,330

PU

,909

,083

Beta

t

Sig.

1,257

,213

,797

10,973

,000

PEOU

,207

1,789

,078

AA

,101

1,188

,239

PR

,016

,142

,887

Excluded Variables

Dependent Variable: BI

usefulness (PU) as the dependent variable with perceived ease of use (PEOU) being its predictor. The second stage measured behavioral intention of use (BI) as the dependent variable, with perceived usefulness (PU), perceived ease of use (PEOU), perceived risk (PR), and attractiveness of alternatives (AA) being its predictors. Table 3 summarizes the results of the first stage that provide empirical evidence that Perceived Ease of Use significantly affects Perceived Usefulness. Table 4 summarizes the results of the second stage that provide empirical evidence, which supports the prediction of behavioral intention of use from perceived usefulness, while the other independent variables where excluded because they were found to affect insignificantly the behavioral intention of use. The model explains 66.1% of the variance regarding spectators’ intentions to use the system.

proFIlIng the users oF rFId TICKETING SYSTEMS In order to identify the pertinent target groups for a potential commercial exploitation of the RFID

ticketing system, we examined the effects of the demographics of the sample (age and education) to the dependent variable of the model (behavioral intention of use). Furthermore, we examined the effect of the demographic information of the sample to perceived usefulness because of the variable’s strong predictive power to behavioral intention of use. We used One-Way ANOVA to measure the aforementioned effects.

Differences Based Upon Age The results illustrated in Table 5 indicate that age plays an important role concerning Behavioral Intention of Use as differences between the age groups are significant at the level of 99,92% (sig=,008). This finding is graphically illustrated in Figure 7. Likewise, the usefulness of the systems is perceived differently across age groups, at significance level of 99,84% (sig=,014) especially between the age group of 26-34 and ?y2 ?y) (= ?f ?floor) --> (modify currentLoc:floor ?f) (modify currentLoc:room ?name) )

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//Room Purpose (FACT03 :desc room-purpose :role dinning :purpose take_meal …) //Position (FACT04 :desc position :purpose take_meal :min_high 1000 …) //Rule User Situation (rule Rule02 (currentLoc :room ?name) (FACT02 :name ?name) = ?room_c (?room_c :role ?role) = ?room_p (?room_p :purpose ?purpose) (FACT04 :purpose ?purpose) = ?selected_p (FACT01 :tag-id 0003 :z ?z) ~ (< ?selected_p:min_high ?z ) --> (modify situation:status abnormal) )

Concept of Symbiotic Computing and its Agent-Based Application

Figure 14. Hardware configuration Ultr asonic R ec eiver (ZP S)

Data c ommunication card (P HS ) User Terminal

USB C amera

Ultr asonic Tag (ZP S)

V ideo Receiver Application (JMF-rec Agent) Acti ve type R FID R ec eiver

(a) Ultrasonic Tag and User Terminal

G UI of Us er Requirement (Us er Agent)

(b) US B C amera and R FID R eceiver

(c) User Terminal Interface

(d) Ultrasonic Receiver

Figure 15. Experimental environment settings Partition

PC Display Pc11 802.11g

Desk

Pc8

Living room

Partition

PC2

802.11g

D

C

Pc10

Pc7

B

Table

Media converter

Pc5 100 Mbps Ethernet

(a) Observation site (1) : Office room

Table

USB Camera

PC1

USB Camera Table

Television

Plasma Television

PC Display Pc6

802.11b

100 Mbps Ethernet

PC Display

Pc9

DV Camera

A

PC3 802.11b Wireless access

PHS (128 kbps)

USB Camera

PC4

100 Mbps Ethernet

small Pc12

(b) Observation site (2) : Living room in home

ence. In this example, we use a handheld PC (Microsoft Windows XP, Celeron M 900 MHz, 256 MB memory, VAIO type-U; Sony Corp.) for this device. It is connected to the network with a CF type data communication card (PHS), which provides a link of 128 kbps bandwidth. We use sensors of two kinds to obtain location information of uses in the room. We use Furukawa Sanki’s Zone Positioning System (ZPS) [ZPS, 2008] for ultra sonic sensors as shown in Figure 14(a) and (d). Figure 14(a) shows a ZPS tag in the style of a name plate. We also use an RFID system (Fu-

(c) Watched site: Living room

jitsu Software Technologies Ltd.) [LPS, 2007], as shown in Figure 14(b). This is an active-type RFID system using 315 MHz radio frequency. It can recognize the tag location within 2 m from receiver, in a minimum setting.

experimental environment and scenarios Figure 15 shows the room settings of the watching site. Figure 15(a) depicts the room installation of the office room and Figure 15(b) as that of the

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living room of his son. Several display devices are used, including PC displays, built-in displays on laptop PCs, and TV sets. They are connected to PCs that are linked by wireless or wired networks. Here, “Small PC12” represents the user terminal described in section 7.1. In terms of the location sensor, a ZPS ultrasonic sensor is used in both (a) and (b). Here, the elderly father is in his living room. His son moves around his living room and office room. In this situation, our system will select the most appropriate camera, a PC with reasonable network connection, and display devices, considering the RS situation. Then suitable quality of live video is displayed on one display according to the son’s requirements for the supervision and status of devices.

experiment Exp.(1): Experiments with NF agents and PF agents Method: In the experiment, the watching person first specifies a user requirement from “best resolution” and “best smoothness” options, using a user interface on the user’s terminal provided by the User agent. This selection is based on the background of the following: a.

b.

The son wants to observe the father’s facial color or expression in high resolution of the video because he is worried about status of his father’s illness (Exp.(1)-1). The son wants to see his father’s full posture as smooth as possible because he is concerned about his father’s lower back pain condition (Exp.(1)-2).

Subsequently, the son moves through the rooms. In these experiments, the father’s location is fixed for simplification. He is at point “A” in his living room shown in Figure 15(c). Based on the requirements and location of the son, agents work cooperatively to select the most adequate sets of

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entities based on multiple context information. We observe the behavior of the agents. Result of Exp.(1)-1: The son specifies the best resolution of the video to observe his father’s facial color. Then he moves to the location at point “B” in Figure 15(a). Point “B” is the service area of PC display of PC6 and a television set connected to PC5. Here, in the case of the location-based scheme, the video service moved to PC display of PC6 from the user terminal because it is judged to be nearest to the PC display. However, the video quality is too low to observe the father’s facial color vividly because it is moved with the same video quality parameters as it is in the user terminal. The user terminal was connected to the network with 128 kbps. For that reason, the quality was reduced to save bandwidth resource consumption. This is caused by the lack of device awareness. In contrast, in the proposed system, an agent organization was constructed. It selected the TV display and DV camera with high resolution to fulfill the user’s requirement, as portrayed in Figure 16. In terms of software, DVTS is selected because it can provide high-quality video. Moreover, agents recognized that, at the sender site, PC3 with a DV camera is connected by 11 Mbps wireless link. Therefore, the full frame rate of DVTS transmitting will not be available. A DVTS-send agent tunes its frame rate to meet the network bandwidth. In this case, multiple situations of entities, involving the network, user, software, and hardware are effectively coordinated and user requirements are satisfied. This flexible configuration will reduce the gaps attributable to the inconvenience and quality of services. Result of Exp.(1)-2: The son specifies the high smoothness of movement of the video to observe his father’s health condition in this case. Then he moves to the location at point “D” in Figure 15(b). Point “D” is the service area of portable PC PC10 and PC display of PC11. Here, in the case of a location-based scheme, the video service moved to display of PC10 from

Concept of Symbiotic Computing and its Agent-Based Application

Figure 16. Result of Exp.(1)-1: Effect of NF and PF agents in case of high quality requirement

Difficult to see color of the face vividly due to low res olution.

I want to observe my father s facial color because he is s ick…

P lasma T V c onnected to P C5

P C Dis play c onnected to P C6 P C6

mov

He s pecified high res olution requirement us ing P C12

e S mall PC P C12

(a) Initial s tatus

mi

gra

t ed

gr mi (b) Location-bas ed service configuration (previous s cheme)

the user terminal because it is judged to be the nearest PC display. However, the frame rate of the video is too low to view the movement of his father’s body smoothly. On the other hand, the proposed system selected the PC display of PC11 and the USB camera connected to PC1, with high frame rate to fulfill the user’s requirement. In terms of the network context, PC11 is the best because it is connected by a wired link with 100 Mbps. Moreover, agents recognize that PC11 cannot play DVTS video because DVTS software was not installed in PC11. For this reason, the USB camera with JMF-send agent is selected. The JMF-send agent also controls frame rates as high as possible. In this case, multiple situations were deeply considered; user requirements for high smoothness of the video were satisfied by the constructed agent organization.

d at e

(c) uE yes-based s ervice configuration (proposed s cheme)

Exp.(2): Experiments with SF Agents Next, we performed an experiment to verify the effect when SF agents are introduced. It is expected that it achieves the goals of observation by a community of two or more people because understanding of the interpersonal relationships and recognition of the situation can be done by the SF and MCF agents. Here, we show an example of observation by several persons: (a) watched person’s son, (b) his relative, (c) his neighbor with a good relation, and (d) a person of the same town as him. In a situation where the watched person’s privacy is considered, such as when he is in his bed room, agents are organized to deliver appropriate video images. In this case, the JMF-send agent, which can operate image filtering, is incorporated into the organization. Then the JMF-send agent controls

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Figure 17. Result of Exp.(2): Effect of SF Agents in case that it needs to keep watched person’s privacy

(a) His son’s view

(b) His relative’s view

the image to provide each display to (a)–(d), as shown in Figure 17. A raw image with high quality is delivered to the son. A lower quality image that portrays only movement is delivered to the relative. The others do not receive an image. Another experiment is performed to show an example of the display in the emergency situation when a watched person falls, as with a seizure. The Situation Recognizer judges it to be an emergency of a fall, etc., using the elderly person’s location information provided by an ultrasonic wave tag, and the background knowledge related to the structure of the house. The display is changed so that many people can know the situation by lowering the privacy level. Concretely, an unretouched image is delivered to the son and the relative, a neighbor with a good relation to the watched person receives a low-quality image that shows only the appearance; a person of the same town is notified by an emergency message without any video image.

discussion From experiments described in previous sections, we confirmed the feasibility of proposed system based on Symbiotic Computing. We evaluated that our system can effectively construct a service configuration that matches the requirement, coping not only with the location information, but also with device and network status around users

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(c) His neighbor's view

(d) View of his friend living nearby

in a ubiquitous environment. In this application, heterogeneous entities like display devices, capture devices, PCs, networks, different kinds of sensors, software components, etc., are wrapped by the wrapping-based model of the ADIPS framework; they are integrated efficiently. This architecture would accelerate the reuse of the components used in this care-support system in the form of agent, by other types of applications. Basically, the modularity, the autonomy and the loose coupling characteristics of the agents would conform to the construction of symbiotic applications. It can adapt to diverse types of entities and scalability of system size. Using this architecture, development and extension of the system can be accomplished easily. On the other hand, we confirmed the effect of SF agents and MCF agents. Using these agents, uEyes can recognize relationships between people in the real world and detailed situations of the watched person. Based on that information, uEyes can handle QoS parameters and set privacy levels to an adequate level. These social functions contribute to enhancement of security and safety in service provision for non-expert users to bridge the gaps. In terms of the privacy concerns, this system provides a very simple function to switch between different views of the watched person, based on the pre-defined human relationships and system/ network resource status. This application example

Concept of Symbiotic Computing and its Agent-Based Application

is shown mainly to describe the overall system behavior by simplifying each individual function. The privacy is one of the most important issues in this kind of system. Also we have to consider more gentle human interface provided to the watched person, including privacy level instruction and confirmation of the list of watching persons. We are investigating these additional functions; however, further discussion is out side the scope of this paper. This work is assuming the situation where the ubicomp environment matures at a certain level. Thus, as for the deployment of this system in practical situation, we have some limitations in current ubiquitous computing environment in terms of cost and usability. To address these problems, some kinds of automatic configuration mechanism to reduce users’ burden is required in installation and operation. The autonomous property of agent can help to realize this mechanism; however we will consider this in our future work.

reLAted WorKs The proposed model and architecture based on the concept of Symbiotic Computing aims at realizing the post-ubiquitous computing environment. Therefore, our work is related to many other studies in the field of ubiquitous computing. For instance, in EU, a Framework Program for Information Society Technology (IST) has been progressing, showing the direction of the road ahead to realizing the “Ambient Intelligence” towards 2010. Many projects are on going to achieve the goal in the Framework Program FP7. Our work provides a generic model to integrate the existing technologies for ubiquitous computing and to implement them in a systematic manner, according to the basic model of co-existence between real space and digital space. Moreover, the Social Function in our model is a remarkable function that would be a key component to overcome the traditional ubiquitous computing environment. This func-

tion can add another important aspect of human and community, such as human relationships, social rules, common sense, social behavior, etc, in order to give the users a pleasant feeling of safety when they have to directly deal with the IT environment. In addition to the concept, model and architecture of Symbiotic Computing, we give a software infrastructure to implement symbiotic applications based on multiagent technologies. There has been considerable research on frameworks to provide ubiquitous services [Itao, 2004; Minar, 1999; Minar, 2000; Roman, 2002; Minami, 2003; Gribble, 2001]. Ja-Net [Itao, 2004] aims to construct emergent services based on user preferences. Hive [Minar, 1999; Minar, 2000] is a multi-agent platform for dynamically creating services through interaction among agents. Roman et al. proposed a middleware for active spaces [Roman, 2002]. STONE [Minami, 2003] and Ninja [Gribble, 2001] can provide the services based on the service template that is requested by the user. These superior frameworks and service construction schemes are investigated for dynamic cooperation among many kinds of system components to provide user-oriented services. These previous works are based only on user contexts and functional components while concentrating on providing guarantees of coordination and operation, or standardization of the specifications. Therefore, these works are expected to satisfy a particular requirement and limitation including the network and computer resources. We believe that it is much more important to consider QoS for provisioning services, particularly in the ubicomp environment. Compare to the existing works, our system gives the dynamic system construction mechanism, considering not only a user’s location, but also multiple contexts of diverse system elements such as device status, network congestion, software availability, etc., as well as users’ requirements in a systematic manner. Moreover, a robust and resilient software development infrastructure can be provided to

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handle multiple reconfiguration and extension of system components in ubicomp environments in which elements are eminently changeable. In terms of the supervisory application systems, some research groups have investigated the application for health care in ubicomp environments. GerAmi [Corchado, 2008] is an advanced support system with intelligent agent that dynamically schedules nurses’ tasks, reports on their activities, and monitors geriatric patient care. In a similar study, researchers have studied the contextual information to recognize user activities to provide opportunistic services to hospital staff, by using a hidden Markov model [Sanchez, 2008]. In one of these attempts, the live video supervisory systems have been studied. They offer detailed information of various target persons with the sense of safety on the watching side, which cannot be obtained by traditional monitoring systems [Rowan, 2005; Boudy, 2006; Kang, 2006], which transmit only the location information and vital information of the watched person. Some research groups are working on how to apply this kind of system to ubicomp environments [Takemoto, 2005; Silva, 2005; Cui, 2004; Lohse, 2005] as described in section 5. Existing systems have merely switched the cameras and the displays because they consider only a user’s location information. Therefore, even if the video quality is inadequate, the camera or display that is nearest to the user is selected. Our system can consider a balanced relationship between QoS and privacy. It can handle the contexts of diverse system elements such as device status, network congestion, software availability, etc., as well as all users’ locations, to realize suitable QoS. The users’ requirements and social relations are also considered systematically to ensure privacy. Both aspects could be handled sufficiently and simultaneously. This successful implementation of the prototype system was lead by the concept of Symbiotic Computing and its system construction based on multiagent technologies.

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concLusIon We defined symbiotic computing based on ubiquitous computing, Web computing, and relevant models of AI and Cognitive Informatics. A model of the symbiotic computing in this paper is newly formalized based on an agent-oriented model to incorporate social heuristics and cognitive functions into DS to bridge the gaps. To realize the model, we used an agent-based framework in which agents construct and reconstruct organizations dynamically according to situations of RS. The agent organization, which is customized to diverse situations in RS, can integrate necessary and sufficient functional elements and information in DS. It can provide situation-aware services to users in RS. This flexibility is a core function of symbiotic computing: it plays an important role in facilitating the symbiosis of RS and DS by bridging the gaps. We applied this concept and model to a support system for supervision of elderly people and children. The experimental results show that the gaps in that domain can be bridged successfully because its dynamically constructed agent organization for supervision considers the situation of users and devices in RS. The system can provide services that specifically address quality and privacy issues. Future studies will apply this model to application domains other than multimedia supervision such as health-care support, child commuting support, cooperative intellectual work, and so on.

AcKnoWLedGMent This work was partially supported by Sendai Intelligent Knowledge Cluster and the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, 19200005.

Concept of Symbiotic Computing and its Agent-Based Application

references Boudy J., Baldinger J.-L., Delavault F., Muller M., Farin I., Andreao R. V., Torres-Muller S., Serra A., Gaiti D., Rocaries F., Dietrich C., Lacombe A., Steenkeste F., Schaff M., Baer M., Ozguler A., & Vaysse S. (2006) Telemedecine for elderly patient at home: the TelePat project. Proc. of the 4th International Conference on Smart Homes and Health Telematics (ICOST2006)}, 19, pp. 74-81. Corchado J. M., Bajo J. & Abraham A. (2008) GerAmi: Improving Healthcare Delivery in Geriatric Residences. IEEE Intelligent Systems, 23(2), pp.19-25. Cui, Y., Nahrstedt, K., & Xu, D. (2004). Seamless User-level Handoff in Ubiquitous Multimedia Service Delivery. Multimedia Tools and Applications Journal, Special Issue on Mobile Multimedia and Communications and m-Commerce, 22, pp. 137-170. Fujita, S., Hara, H., Sugawara, K., Kinoshita, T., & Shiratori, N. (1998). Agent-based design model of adaptive distributed systems. Applied Intelligence, 9, pp. 57-70. Gribble S., Welsh M., Behren R., Brewer E., Culler D., Borisov N., Czerwinski S., Gummadi R., Hill J., Joseph A., Katz R., Mao Z., Ross S., & Zhao B. (2001) The Ninja architecture for robust Internet-scale systems and services. Special Issue of Computer Networks on Pervasive Computing, 35(4), pp. 473-497. Hattori, H., Ohguro, T., Yokoo, M., Matsubara, S., & Yoshida, S. (1999). Socialware: multiagent systems for supporting network communities. CACM, 42(3), pp. 55-61. Itao T., Tanaka S., Suda T., & Aoyama T. (2004). A Framework for Adaptive UbiComp Applica-

tions Based on the Jack-in-the-Net Architecture. Wireless Networks, 10(3), pp. 287-299. Kang J. M., Yoo T., & Kim H. C. (2006) A WristWorn Integrated Health Monitoring Instrument with a Tele-Reporting Device for Telemedicine and Telecare. IEEE Trans. Instrumentation and Measurement, 55(5), pp. 1655-1661. Kinoshita, T., & Sugawara, K. (1998). ADIPS Framework for Flexible Distributed Systems. Lecture Notes in Artificial Intelligence 1599, Multiagent Platform, pp. 18-32. Lohse, M., Repplinger, M., & Slusallek, P. (2005). Dynamic Media Routing in Multi-User Home Entertainment Systems. The Eleventh International Conference on Distributed Multimedia Systems (DMS’2005). LPS: Local Positioning System. (2007) http:// jp.fujitsu.com/group/fst/services/ubiquitous/rfid/ index.html (in Japanese) Lyytinen, K., & Yoo, Y. (2002). Issue and Challenges in Ubiquitous Computing. CACM, 45(12), pp. 63-65. Minami M., Morikawa H., & Aoyama T. (2003) The Design and Evaluation of an Interface-based Naming System for Supporting Servic Synthesis in Ubiquitous Computing Environment. Journal of IEICEJ, J86-B(5), pp. 777-789. Minar N., Gray M., Roup O., Krikorian R., & Maes P. (1999) Hive: Distributed Agents for Networking Things. Proc. of the 1st International Symposium on Agent Systems and Applications / 3rd International Symposium on Mobile Agents (ASA/MA ‘99), pp. 118-129. Minar N., Gray M., Roup O., Krikorian R., & Maes P. (2000) Hive: Distributed Agents for Networking Things. IEEE Concurrency Magazine, 8(2), pp. 24-33.

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Ogawa, A., Kobayashi, K., Sugiura, K., Nakamura, O., & Murai, J. (2000). Design and Implementation of DV based video over RTP. Packet Video Workshop 2000. Oxford University Press. (2000). Oxford Advanced Learners dictionary. Roman M., Hess C., Cerqueira R., Ranganathan A., Campbell R. H., & Nahrstedt K. (2002) A Middleware Infrastructure for Active Spaces. IEEE Pervasive Computing, pp. 74-83. Rowan J. & Mynatt E. D. (2005) Digital Family Portrait Field Trial: Support for Aging in Place,’’ {\it Proc. of ACM SIGCHI Conference on Human Factors in Computing Systems 2005 (CHI2005), pp. 521-530. Sanchez D, Tentori M. & Favela J. (2008) Activity Recognition for the Smart Hospital. IEEE Intelligent Systems, 23(2), pp.50-57. Shiratori, N., Suganuma, T., Sugiura, S., Chakraborty, G., Sugawara, K., Kinoshita, T., & Lee, E. S. (1996). Framework of a flexible computer communication network. Computer Communications, 19, pp. 1268-1275. Shiratori, N. Symbiotic Computing Project. (2007) http://symbiotic.agent-town.com/ Silva, G.C. D., Oh, B., Yamasaki, T., & Aizawa, K. (2005). Experience Retrieval in a Ubiquitous Home. Proc. of the 2nd ACM Workshop on Capture, Archival and Retrieval of Personal Experiences 2005 (CARPE2005), pp. 35-44. Smith, R. (1980). The Contract Net Protocol, High Level Communication and Control in a Distributed Problem Solver. IEEE Trans. Comp., 29(12), pp. 1104-1113. Suganuma, T., Imai, S., Kinoshita, T., Sugawara, K., & Shiratori, N. (2003). A Flexible Videocon-

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ference System based on Multi-agent Framework. IEEE Trans. on Systems, Man, and Cybernetics part A, 33(5), pp. 633-641. Sugawara, K. (2005). Agent-based Support System for Project Teaming for Teleworkers. Lecture Notes in Artificial Intelligence 3371, Intelligent Agents and Multi-Agent Systems, pp. 279-290. Takahashi H., Tokairin, Y., Suganuma, T., & Shiratori, N. (2005). Design and Implementation of An Agent-based middleware for Context-aware Ubiquitous Services. Frontiers in Artificial Intelligence and Applications, New Trends in Software Methodologies, Tools and Techniques, 129, pp. 330-350. Takemoto, M., Oh-Ishi, T., Iwata, T., Yamato, Y., Tanaka, Y., Tokumoto, S., Shimamoto, N., Kurokawa, A., Sunaga, H., & Koyanagi, K. (2005). Service-composition Method and Its Implementation in Service-provision Architecture for Ubiquitous Computing Environments. IPSJ Journal, 46(2), pp. 418-433. Uchiya, T., Maemura, T., Hara, H., & Kinoshita, T. (2007). Interactive Design Model of Agent System for Symbiotic Computing. Proc. of 7th IEEE International Conference on Cognitive Informatics. Wang, Y. (2005). On Cognitive Properties of Human Factors in Engineering. Proc. of ICCI2005, pp. 174-182. Wang, Y. (2007a) The Theoretical Framework of Cognitive Informatics. The International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 1(1), pp.1-27. Wang, Y. (2007b) On Laws of Work Organization in Human Cooperation, The International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 1(2), pp. 1-15.

Concept of Symbiotic Computing and its Agent-Based Application

Weiser, M. (1996) Ubiquitous Computing. http:// www.ubiq.com/hypertext/weiser/UbiHome. html

ZPS: Zone Positioning System. (2008) http://www. furukawakk.jp/products/ (in Japanese)

This work was previously published in the International Journal of Cognitive Informatics and Natural Intelligence, Vol. 3, Issue 1, edited by Y. Wang, pp. 34-56, copyright 2009 by IGI Publishing (an imprint of IGI Global).

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Chapter 8.12

Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays Jesus Favela CICESE, Mexico Mónica Tentori CICESE and Universidad Autónoma de Baja California, Mexico Daniela Segura CICESE, Mexico Gustavo Berzunza CICESE, Mexico

AbstrAct Sentient computing can provide ambient intelligence environments with devices capable of inferring and interpreting context, while ambient displays allow for natural and subtle interactions with such environment. In this paper we propose to combine sentient devices and ambient displays to augment everyday objects. These sentient displays are aware of their surroundings while providing continuous information in a peripheral, subtle, and expressive manner. To seamlessly

convey information to multiple sentient displays in the environment, we also propose an approach based on abstract interfaces which use contextual information to decide which display to use and how the information in the display changes in response to the environment. Our approach is illustrated through a hospital monitoring application. We present the design of two sentient displays that provide awareness of patient’s urine outputs to hospital workers, and how contextual information is used to integrate the functionality of both displays.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

IntroductIon Sentient computing is an approach that allows users to naturally interact with the physical environment by becoming aware of their surroundings and by reacting upon them (López de Ipiña & Lai Lo, 2001). Awareness is achieved by means of a sensor infrastructure that helps to maintain a model of the world which is shared between users and applications – referred as Sentient artifacts (Hopper, 1999). Indeed, sentient artifacts have the ability to perceive the state of the surrounding environment, through the fusion and interpretation of information from possibly diverse sensors (Addlesee et al., 2001). However, it is not sufficient to make Ambient Intelligence (AmI) environments aware of the user’s context, they must be able to find a way to communicate this information to users while becoming a natural interface to the environment (Shadbolt, 2003). Ambient displays could be embedded in everyday objects already known and used, thus becoming the user interface of the AmI environment. This vision assumes that physical interaction between humans and computational devices will be less like the current keyboard, mouse, and display paradigm and more like the way humans interact with the physical world. For instance, a mirror augmented with infrared sensors and an acrylic panel could detect human presence and act as a message board to display relevant information when a user faces the mirror. Hence, AmI environments could be augmented with such displays that unobtrusively convey information to users without requiring their full attention, while at the same time, allowing an implicit and natural interaction. Indeed, the notion of what constitutes a computer display is changing. No longer is a display confined to the typical CRT monitor with a single user paying focused attention while interacting with virtual objects on the screen (Lund & Wilberg, 2007). Rather, computer displays are found in such diverse forms as small

screens in mobile phones or handheld computers, to ambient displays that provide peripheral awareness to the presence and status of people, objects or information. In this article, by binding the ideas of sentient computing and ambient displays we propose the concept of sentient displays to define a new and appropriate physical interaction experience with an AmI environment. Such sentient displays will be capable of monitoring users’ context, promptly notify relevant events and provide users with continuous information in a subtle, peripheral and expressive manner without intruding on our focal activity. Moreover, multiple such displays could be integrated in an AmI environment, with a decision of which one to use dependent on contextual circumstances, such as the user’s location, the presence of other people or the activity being performed by the user. Thus, we also discuss an approach to develop contextual interfaces for a variety of sentient displays located throughout the intelligent environment. Our approach is based on the use of abstract interfaces that are specialized to specific devices once a decision is made as to which sentient display(s) should be used. This approach facilitates the progressive integration of new sentient displays. To illustrate the concept of sentient displays we draw upon scenarios related to hospital work. Mobility and frequent task switching cause hospital workers to occasionally miss important events, such as a catheter being disconnected due to the patient movement or the need to change a urine bag that has been filled-up (Moran et al., 2006). Consequently, hospital workers have been held liable for their failure to monitor and promptly respond to patients needs (Smith & Ziel, 1997). Sentient displays located throughout hospital premises could be used for a diverse number of hospital applications, such as notifying hospital workers of a crisis or just provide continuous awareness of the health status of patients.

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Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

sentIent dIsPLAys: AuGMentInG nAturAL obJects WItH AMbIent dIsPLAys And sentIent tecHnoLoGIes Research in pervasive computing has included the development of ambient devices that can become part of the background while acting as a digital interface to ambient information. As stated by Mankoff: “Ambient displays are aesthetically pleasing displays of information which sit on the periphery of a user’s attention. They generally support the monitor of information and have the ambitious goal of presenting information without distracting or burdening the user” (Mankoff et al., 2003). For instance, the artist Natalie Jermijenko at Xerox Parc augmented a string with a motor and spin to convey the traffic’s status to a user –the Dangling String (Weiser & Brown, 1995). The device rotates at a speed that depends on the amount of traffic in the highway captured through analog sensors. During periods of intense traffic, the string’s movements are slightly audible as well. Thus, ambient displays are, unlike ordinary computer displays, designed not to distract people from their tasks at hand, but to be subtle reminders that can be occasionally noticed. In addition to presenting information, the displays also frequently contribute to the aesthetics of the locale where they are deployed (Lund & Wilberg, 2007). For instance, as part of the AmbientRoom project, several displays using light, sound or motion have been developed to augment a user’s office (Ishii et al., 1998). Undeniably, ambient displays need computing devices capable of perceiving our surroundings by seeing or hearing the entities in the environment, what these entities are doing and where they are. To this aim, research in sentient computing has focused on the development of sensors that attached to today’s computing and communication technology are capable of perceiving a range of contextual information such as location, traffic status, user’s presence and so on (Abowd & Mynatt, 2002). The most popular

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sentient devices are indoor location systems (Addlesee et al., 2001; Werb & Lanzl, 1998). While ambient displays provide a different notion of what constitutes an interface to the AmI environment, sentient technologies allow such displays to be reactive and perceptive to dynamic changes in the environment. In this article we propose the concept of sentient displays combining the ideas of ambient displays and sentient computing. Sentient displays are our everyday artifacts augmented with digital services capable of perceiving information from the environment and then using this information to extend the capabilities of such artifact. We envision a sentient artifact as an object that encapsulates the information perceived and then provides users with a new form of interaction with the environment. This interaction could be either by offering continuous, subtle and peripheral awareness or by allowing users to change the status of such object in order to affect an AmI environment.

oPPortunItIes for tHe dePLoyMent of sentIent dIsPLAys In HosPItALs: A cAse study For nine months, we conducted a field study in a public hospital’s internal-medicine unit, observing the practices of the hospital staff, who attended patients with chronic or terminal diseases (Moran et al., 2006). Such patients are often immobile and incapable of performing the activities of daily living (ADL) by themselves. The study was conducted to understand: (1) the type of patients’ information being monitored by hospital workers and (2) the problems faced by hospital workers when monitoring patients. Hospital workers are responsible for providing integral and specialized care for patients. As part of this, nurses monitor patient’s activities of daily living (ADL), such as, if a patient has taken his medicine, if he has walked, eaten, felt from the

Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

bed, evacuated, etcetera. As a part of specialized care, nurses need to monitor the behavioral patterns in the activities that put at risk the patients’ health or that indicate an internal failure which might evolve into a more serious disease (e.g., pneumonia, an apoplexy or a stroke), such as, if a patient is agitated, if a patient is bleeding or if the patient has respiratory insufficiency. These behavioral patterns associated to risk activities (RA) are monitored through the vital signs. To illustrate the problems faced by hospital workers when monitoring patients we present a realscenario that was observed in the hospital: Nurse Rita is informed, at the beginning of her working shift, that the attending physician has changed Pedro’s medication to include cyclosporine. Pedro is a 56 years old man, who has a chronic renal failure and just had a renal transplant. So, to monitor Pedro’s reaction to the new kidney and to the medicine being administered to him, Rita needs to supervise the frequency and quantity of Pedro’s urine. Nurse Rita starts her shift by taking care of Juan –the patient in bed 226. While she is inserting a catheter to Juan, Pedro’s urine bag fills up. Unaware of Pedro’s status, Rita continues taking care of Juan. After several minutes another nurse informs Rita that Pedro’s urine bag spilled up. Rita moves to Pedro’s room to clean him. The problem illustrated in the scenario could be avoided if Rita knows when Pedro’s urine bag is almost full. This, and similar examples, helped us identify major issues faced by hospital workers when monitoring patients. In particular, issues related to hospital workers being on the move include maintaining awareness of their patients’ status, being easily accessible when an emergency occurs, and prioritizing patient care on the basis of the patient’s health condition. In addition, nurses must manage the tradeoff between having expressive versus silent awareness. While nurses want to be aware of the status of all the

patients they are taking care off, they don’t want this awareness to intrude in their focal activity. Finally, nurses need to interpret ambient information over time and at different levels of detail. This poses interesting challenges related to how contextual information influences the importance of the information presented. For instance, while to monitor Pedro’s health it is significant for Rita to know the frequency and quantity of his urine outputs, for those patients with other diseases this information might not be relevant for her. This need to monitor the status of patients in an environment already saturated with information and with hospital workers constantly on the move and switching from one task to another, inspired us to design sentient displays for the hospital.

tHe AdL MonItor: A MobILe sentIent dIsPLAy The ADL Monitor is a sentient display aimed at creating a wearable ambient connection between patients and nurses (Tentori & Favela, 2008). The ADL Monitor is composed of one sentient artifact and two ambient displays. The first ambient display is a two-layered vinyl bracelet containing five buttons with embedded lights (see figure 1a). Each button represents a patient under the nurse’s care. The lights turn on when a patient is executing an activity, when particular actions occur, or after a series of events take place. Nurses can press the button to consult information associated to the activity a particular patient is executing. This information is shown by the ADL assistant that runs on the nurse’s smartphone (see figure 1b) –the second ambient display. Nurses can also use the ADL assistant to assign priorities by selecting colors (figure 1c) or to set contextual information to act as a trigger for the activities being monitored (figure 1d). To notify patient’s urine habits the mobile ADL Monitor uses the WeightScale. The WeightScale is a sentient artifact attached to the urine bag and measures its weight.

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Figure 1. The mobile ADL Monitor. (a) A nurse uses the bracelet; (b) the mobile ADL assistant shows information related to an activity being executed by a patient; (c) a nurse uses her smartphone to configure the bracelet; and (d) a nurse associates contextual information with an activity.

Going back to our scenario: Rita uses ADL assistant in her smart phone to specify that the light representing Pedro in her bracelet should turn yellow when Pedro evacuates, and red if he evacuates more than five times in six hours (Figure 1d). Later, while Rita is preparing medicines, Pedro’s light turns yellow. Rita presses the button, and her smart phone indicates what Pedro is doing (Figure 1b). Rita learns that Pedro has urinated approximately 10 milliliters (this information is calculated though the weight sensor attached to Pedro’s urine bag). Rita goes to Pedro’s room to update his liquid balance. Throughout the night, Pedro’s light in Rita’s bracelet constantly turns yellow. A couple of hours later, while Rita is talking to Dr. Perez, her bracelet turns red. Rita consults her smart phone and realizes that Pedro has urinated seven times in six hours. She discusses this with Dr. Perez, who then decides to change Pedro’s medication to avoid damaging the new kidney. The system uses a client-server architecture as a basis for its implementation. When a nurse presses a button on the bracelet, a message is sent back to the server, specifying a patient and bracelet ID. This ID is used by the server to determine which patients’ status should be displayed on which smart phone. We developed our own

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components to communicate the bracelet with the server. A transmitter is responsible for sending and receiving messages from the bracelet at frequencies under 27 Mhz. This transmitter is internally connected to the CPU and embeds a receptor circuit that manages the radioelectrical signals from the bracelet and translates them into pulses. In contrast, the bracelet has embedded a receptor circuit that converts radioelectrical signals into electrical pulses. In the following lines we described how the components work to support the services just described.

Providing Perceptible and silent Awareness The idea of this service is to adequately manage how the information will be presented to the user. An ambient display that changes too fast can distract the user whiles a display that changes too slowly can pass unnoticed (Johan et al., 2000). To balance this tradeoff, the mobile ADL Monitor modulates the information shown to the user. While the bracelet only displays the status of a patient and his identity, the smartphone shows more information, such as the quantity and frequency of urine outputs. This will allow a nurse to explicitly extend the information shown by the bracelet with the one shown in the smartphone

Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

binding both displays while unrestricting their usefulness by presenting just enough information –how much a patient has urinated.

enabling simple, effortless and seamless Interaction Users should be able to interact with the display implicitly and naturally. Users do not have previous experience interacting with ambient displays; hence they should be intuitive. The mobile ADL Monitor uses colors analogous to a traffic light adapted from the medical model used in the emergency unit. This allows nurses to naturally discover the emergency state of a patient. In addition, nurses can press the button that represents each patient to consult more information of the state of a patient. Therefore, by embedding a light in each button of the bracelet we are extending the capabilities of an artifact without altering the traditional means of interaction with it. This will result in a reduction of the cognitive load by learning how displays work and increasing the amount of attention on content (Gross, 2003).

of this information wirelessly. When the bag is replaced the button goes back to its normal position. We use motes to avoid saturating the rooms with wires that could be obtrusive to nurses and patients. To evaluate the ADL Monitor we interviewed seven nurses, each for 30 to 60 minutes, to evaluate the bracelet’s design, the system’s core characteristics, the nurses’ intention to use the system, and their perception of system utility. All seven nurses indicated that the bracelet will help them save time, avoid errors, and increase the quality of attention given to patients. One nurse commented, this bracelet will improve the quality of attention. The work will be the same, but I will do [it] faster. … For instance, if a patient has evacuated … I would promptly know the patient needs and I [could] take with me the things that I would need. In addition, nurses noticed that the system will help them prioritize events and patients:

enabling unobtrusive Information sensing

Something that we currently cannot do is identify which patient has to be attended first; a system like this one [would] help me identify the urgency with which each of my patients needs to be attended.

The mobile ADL Monitor requires to monitor the weight of a urine bag wore by a patient. For this, we developed a sentient artifact that measures the amount of urine in a bag and communicates this information wirelessly through a mote –the weightScale. This weightScale is attached to a urine bag wore by a patient allowing thus an unnoticeable sensing. The weightScale is made of two acrylic pieces which are separated through a spring and a push button. We calibrated the required separation between both pieces. When the urine reaches a threshold (i.e., when the urine has filled 80% of the urine bag) the button is pressed. Once the button gets pressed, the sensor generates an electronic pulse. This pulse is read by the mote that is responsible for the transmission

Overall, the staff viewed the application as useful, efficient, and generally appealing. Nurses repeatedly expressed that this system would solve many of the problems they face and improve their work, saying that it directly assists with “patient care” rather than merely supporting “secondary tasks,” as they say current systems do. Nurses validated both scenarios and provided us with additional insights and opportunities for applying our technology. For instance, nurses explained that they are used to have the technology directly attached to the patient to avoid problems and errors. Nurses explained that having the information in the smart phone might cause problems, because they might confuse the patient they are

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attending with another one. For instance a nurse explained: I prefer for the bracelet to only function as an indicator, rather than consulting the information of the patient in my cell phone I would prefer to consult this information in the room of the patient. In this case I would be sure that the problem I am handling corresponds to such patient. Indeed, fixed monitoring systems that allow patients to place a call to a nurse at the nurse pavilion have been successfully adopted in hospitals. What we need is to allow our sentient display to be seamlessly integrated with fixed monitoring systems.

tHe fLoWerbLInK: A fIXed sentIent dIsPLAy In this section we describe a fixed sentient display that notifies nurses the patients’ urine outputs and the status of their urine bag to be integrated with the ADL Monitor–the FlowerBlink (Segura et al., 2008). The FlowerBlink is a wooden box containing twenty four artificial flowers: twelve emergency flowers with stems and twelve situation stemless flowers (Figure 2). The flowers are

composed of a two-layered felt that enclose pistils covered with insulating tape. In each pistil a red or yellow led is embedded. The emergency flowers have stems with an embedded yellow light in their pistils (Figure 2a). All emergency flowers blink whenever an event or an emergency occur with a urine bag wore by a patient –if a urine bag is full. In contrast, situation flowers are flowers without stems that have a red light embedded in their pistils. These situation flowers are arranged in a matrix to represent patients’ location in the unit. Each column in the matrix represents a room while a row represents a patient’s bed (Figure 2b) –each room has three beds for patients. This arrangement allows nurses to quickly discover which patients’ bag is about to spill. Situation flowers turn on whenever a nurse approaches the FlowerBlink or if the emergency flowers are blinking. While emergency flowers are blinking a situation flower turns on, indicating to a nurse the location of the patient related to that event. The FlowerBlink includes two sentient artifacts and one ambient display. The first sentient artifact is the WeightScale, described in section 4. The other sentient artifact is the PrescenceDetector that is a card carried out by a nurse that detects her presence when she is in front of the FlowerBlink. The ambient display is the flower vase with a set of flowers that display the status

Figure 2. The FlowerBlink placed in the nurse pavilion. (a) The flowers that notify of emergency events (b) The flowers that personalized their color based on the nurse’s presence

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of the urine bag of the patient. We embedded in the box of the ambient display a communication interface which directly controls the flowers light. When the base station receives the information, it identifies the sensor that sent it, thereby identifying the location of the patient, and then turning on the red light of the corresponding flower. We use the phidgets toolkit (Greenberg & Fitchett, 2001) to implement the FlowerBlink. One of the main challenges in integrating the FlowerBlink and the ADL Monitor is content adaptation. Binding both sentient displays will involve choosing on which device to display the information. This decision must take into account the device capabilities and its user’s context. For instance, while the FlowerBlink can show patients’ status, their location, their urine bag status and their frequency of urine outputs; the bracelet is only capable of displaying patients’ identity and their status. Moreover, the smartphone can show a more complex representation of content because a complex interface can be displayed in such device. Adding more information in the FlowerBlink or in the bracelet could cramp both displays confusing the user on how to use them. This mismatch between rich content and constrained devices capabilities presents a main challenge in integrating multiple sentient displays (Lum & Lau, 2002).

suPPortInG MuLtIPLe sentIent dIsPLAys to MonItor PAtIent stAtus In this section we describe our approach to seamlessly convey patient status information to multiple sentient displays. Based on contextual information our approach: (1) selects the sentient display to be responsible for showing ambient information, (2); defines a concrete interface for the selected display; and, (3) finally, adapts the information in the target display. The concrete interface is derived from an abstract, generic user

interface (Braun & Mühlhäuser, 2004; Souchon & Vanderdonckt, 2003). This is illustrated through the following version of our scenario: Rita uses the FlowerBlink and the ADL Monitor to supervise the frequency and quantity of Pedro’s urine outputs. A sensor in the urine bag detects the presence of new liquid. At this time, Rita, the nurse responsible for monitoring the patient, approaches the nurse pavilion. Thus, the system decides to notify her through the FlowerBlink sentient display located in this area. The description of the abstract notification interface is sent to the agent that acts as a proxy for the sentient display. The agent calls its interpreter to transform the abstract interface to a concrete interface that it then sends to the FlowerBlink. Rita notices that the flower base is blinking and realizes that the information relates to one of her patients. She consults her smartphone to learn that Pedro has urinated for the third time this morning and his urine bag might soon need to be replaced. The interface in the FlowerBlink will turn all lights off as its proxy agent becomes aware that Rita has consulted the information in her smartphone. As the scenario shows, contextual information is used by the system in two stages. First, location information is used to decide which sentient display will be the most suitable to use. Since Rita is in the nurse pavilion the FlowerBlink located there would be the best display to notify the status of her patients. Once the concrete interface is executing on the sentient display it will be adapted when it is informed that the users notices the information. In the example when the sentient display realizes that Rita has become aware of the patient status it adapts its interface by turning off the lights. The presence of another person for which the information is relevant, for instance, the head nurse, might turn the lights on again. Figure 3 and Figure 4 show the sequence diagram of the scenario. These show the main com-

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Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

Figure 3. Sequence diagram of the scenario, showing how the notification is sent through the FlowerBlink :Sensor Solicitor

:Server

FlowerBlink

:AgentFlower

readPort( ) newUpdate(data)

create()

:Composer

getInterface(service) compose()

XForms document chooseDevice()

setInterface(XForms document)

create()

:InterpreterF

traslateInterface() set state

Figure 4. Sequence diagram of the scenario, showing how the FlowerBlink is turned off when its Agent becomes aware that the nurse has consulted the information in her smartphone :AgentCell

Mobile Phone

:Server

:AgentFlower

:InterpreterF

FlowerBlink

request setInterface(XForms document) create()

:InterpreterC

traslateInterface() set interface notification

updateState(data)

traslateEvent() set state

ponents of the architecture, which are described next. In the architecture a Server application communicates to the different sentient displays, through their Proxy Agents, the changes notified by the Sensor Solicitor. The Sensor Solicitor is connected to a set of sensors which update the information about the state of patients’ urine bags. When a notification is about to be sent the Server requests the Composer for the generic

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user interface to be used to inform the patient’s status (patient, disease, time, urine bag level, and so on). This interface will be interpreted for each sentient display depending of its own resources. The Server decides to notify the nurse through the FlowerBlink because she is at the nurse pavilion, thus the generic user interface is sent to the FlowerBlink Proxy Agent. This agent updates the FlowerBlink state in response to the

Adaptive Awareness of Hospital Patient Information through Multiple Sentient Displays

interpretation of the generic interface given by the Proxy Agent’s Interpreter. In this case, the lights are turned on, to inform that the urine bag is almost full. When the nurse notices that the FlowerBlink is blinking, she consults her smartphone for detailed information about her patient. The smartphone’s Proxy Agent requests the information to the Server. This information is delivered using the same generic interface sent to the FlowerBlink. Then, the Proxy Agent invoques its interpreter to translate it to the capabilities of the smartphone, so the Agent can render the information in the smartphone using text and images. This acts as a trigger to notify the FlowerBlink that the nurse has become aware of the patient’s status, so it must turn off the lights. The Server is responsible for sending to the FlowerBlink Agent the event, which in turn is passed to the Interpreter to adapt the information in the user interface. The interpreter indicates to the FlowerBlink Proxy Agent the adaptation to be performed in the display, in this case, to turn the lights off. The generic user interface is described using a User Interface Declarative Language because it does not assume any specific interaction modality or presentation. It captures the purpose of the interface, defining what it must do and not how. Such an approach is followed by XForms (Boyer, 2007), which can separately declare the data model, the presentation, the way of binding the data model to the display elements, as well as the actions and events. User interface elements in XForms are abstract, so that different platforms can choose to implement them in different ways, however the purpose of the tags remains the same (Rivera & Len, 2002). Inputs and outputs, events and actions that modify data element must be translated to appropriate representations for the platform. For example, an input for FlowerBlink could be given by a sensor sensitive to the light, another example is when the data model is changed and in response the output related to this data could be represented

by turning off or turning on the lights through an electric pulse. In the smartphone the output could be rendered using text and images. The XForms document is the same for each device, in consequence an XForms interpreter is required for each platform, which must implement the XForms specification, but for the purpose of the application it only needs to implement the elements of XForms that are used. In addition, the interpreter for the FlowerBlink needs to consider two cases, first it must select just the output related to the data element needed to display, in this case the amount of urine in the bag, and in the second case the amount of urine in the bag at three state alerts depending on a predefined threshold. This kind of consideration goes beyond the XForms specification, since this is specific to the application and platform. For the cell phone it is possible to display all the data in the model. The XForms composer defines the abstract user interface in an XForms document, it contains the information related to the patient defined in the data model, an XML structure, the generic outputs bind to the data model, and the actions to perform adaptations in the data model and the user interface. All possible adaptations that data model or interface may suffer in function of the context must be considered, so the events and actions related to these adaptations must be defined in the abstract user interface. The Server must notify updates on relevant contextual information so the adaptation defined for these updates could be realized in the concrete user interface.

concLusIon And future WorK In this article, we discuss the concept of sentient displays in support of hospital work and a contextaware approach for content adaptation. We show that sentient displays are capable of becoming aware of users’ context and then present continuous and expressive information in a subtle and

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unobtrusive way. We show that a combination of mobile and fixed displays enables a hospital smart environment to provide the type of awareness hospital workers need to promptly identify patient’ needs, save time and avoid errors. We discuss that contextual information allows an AmI environment to automatically choose the adequate sentient artifact to display information according to users’ needs. We plan to conduct an in situ evaluation of the displays developed to assess their impact within the hospital. In addition, we plan to explore a new setting where this type of technology could be useful –in particular, in nursing homes. Workers at nursing homes specialized in the care of elders with cognitive disabilities face working conditions that are similar to those in hospitals. Such workers also use common strategies to monitor patients’ status. This monitoring is done manually, making it time consuming and error prone. This is another healthcare scenario in which sentient displays can prove useful.

AcKnoWLedGMent This work was partially funded by CONACYT through scholarships provided to Monica Tentori, Daniela Segura and Gustavo Berzunza.

references Abowd, G., & Mynatt, E. (2002). The humen experience. IEEE Pervasive, 1(1), 48-57. Addlesee, M., Curwen, R., Hodges, S., Newman, J., Steggles, P., Ward, A., et al. (2001). Implementing a sentient computing system. IEE Computer, 34(8), 50 - 56. Boyer, J. (2007). Xforms 1.0 (third edition). Http:// www.W3.Org/tr/2007/rec-xforms-20071029/.

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Braun, E., & Mühlhäuser, M. (2004, May 25-28). Extending xml uidls for multidevice scenarios. Paper presented at the Workshop on Developing User Interfaces with XML of the Advanced Visual Interfaces Conference, Gallipoli, Italy. Greenberg, S., & Fitchett, C. (2001, November 11-14). Phidgets: Easy development of physical interfaces through physical widgets. Paper presented at the 14th annual ACM symposium on User interface software and technology, Orlando, Florida. Gross, T. (2003, June 22-27). Ambient interfaces: Design challenges and recommendation. Paper presented at the International Conference on Human-Computer Interaction, Crete, Greece. Hopper, A. (1999). The cliord paterson lecture: Sentient computing. Philosophical Transactions of the Royal Society of London, 358(1773), 23492358. Ishii, H., Wisneski, C., Brave, S., Dahley, A., Gorbet, M., Ullmer, B., et al. (1998). Ambientroom: Integrating ambient media with architectural space. Paper presented at the Conference on Human Factors in Computing Systems (CHI), Los Angeles, US. Johan, R., Skog, T., & Hallnäs, L. (2000, January 22-24). Informative art: Using amplified artworks as information displays. Paper presented at the Designing augmented reality environments, Elsinore, Denmark. López de Ipiña, D., & Lai Lo, S. (2001, September 17-19, 2001). Sentient computing for everyone. Paper presented at the The Third International Working Conference on New Developments in Distributed Applications and Interoperable Systems, Krakóow, Poland. Lum, W., & Lau, F. (2002). A context-aware decision engine for content adaptation. IEEE Pervasive Computing, 1(3), 41-49.

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Lund, A., & Wilberg, M. (2007). Ambient displays beyond conventions. Paper presented at the British HCI Group Annual Conference. Mankoff, J., Dey, A. K., Hsieh, G., Kientz, J., Lederer, S., & Ames, M. (2003). Heuristic evaluation of ambient displays. Paper presented at the Conference on Human Factors in Computing Systems, Lauderdale, Florida, USA. Moran, E. B., Tentori, M., González, V. M., Martinez-Garcia, A. I., & Favela, J. (2006). Mobility in hospital work: Towards a pervasive computing hospital environment. International Journal of Electronic Healthcare, 3(1), 72-89. Rivera, J., & Len, T. (2002). Get ready for xforms. Http://www.Ibm.Com/developerworks/xml/ library/x-xforms/. Segura, D., Favela, J., & Tentori, M. (2008). Sentient displays in support of hospital work. Paper presented at the UCAMI, Salamanca, Spain.

Shadbolt, N. (2003). Ambient intelligence. IEEE Intelligent Systems, 18(2), 2-3. Smith, K. S., & Ziel, S. E. (1997). Nurses’ duty to monitor patients and inform physicians. AORN Journal, 1(2), 235-238. Souchon, N., & Vanderdonckt, J. (2003, June 4-6). A review of xml-compliant user interface description languages. Paper presented at the International Conference on Design, Specification, and Verification of Interactive Systems, Funchal, Portugal. Tentori, M., & Favela, J. (2008). Activity-aware computing for healthcare. IEEE Pervasive Computing, 7(2), 51-57. Weiser, M., & Brown, J. S. (1995). Designing calm technology. PowerGrid, 1(1), 10. Werb, J., & Lanzl, C. (1998). Designing a positioning system for finding things and people indoors. IEEE Spectrum, 35(9), 71-78.

This work was previously published in the International Journal of Ambient Computing and Intelligence, Vol. 1, Issue 1, edited by K. Curran, pp. 27-38, copyright 2009 by IGI Publishing (an imprint of IGI Global).

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1

Index

A abstraction dimension 428, 430, 433 access control 1429, 1431, 1434, 1436, 1438, 1440, 1446, 1481–1497 active jamming approach 108 activities of daily living (ADL) 1788, 1789, 1790, 1791, 1792, 1793 activities, environments, interactions, objects, users (ABOU) 1506 activity sphere 351 activity-based costing (ABC) 623, 625, 626, 627, 628, 629, 630, 636, 640, 645, 646 actor (actant) 34 actor-network theory (ANT) 29, 34 actuator networks 1521, 1523, 1526 ad hoc classroom 552 ad hoc networks 1217 adaptable context management 280–293 adaptable context management framework (ACMF) 277, 278, 280, 281, 284, 290, 291, 292, 293, 294, 295 adaptive hypermedia 311 adaptive resource and service management (ARSM) 1529, 1535, 1536, 1537, 1538, 1540, 1541, 1543 adaptivity 1527, 1528, 1529, 1530–1534, 1534, 1537, 1538, 1539, 1540, 1543, 1544 addressing scheme 472 adoption strategy 96 advanced encryption standard (AES) 106 advanced health process management (AHPM) 629, 640, 641 affect 1100, 1105 affective computing 552 affordances 312

agent communication language (ACL) 1769 agent virtual machines (AVM) 1769, 1770 AirCROSS 947 Alzheimer’s Community Care (ACC) 874 ALZ-MAS 833, 834, 836, 837, 838, 839, 840, 841, 842 ambient assisted living (AAL) 132 ambient display 680, 696, 698, 701 ambient emotional responsive character, case study 1637 ambient information systems (AIS) 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155 ambient intelligence (AmI) 1, 121, 129, 237, 251, 331, 351, 8331396, 1399, 1402, 1403, 1405, 1643, 1644, 1647, 1655, 1658, 1659, 1661, 1663, 1665, 1720, 1787, 1788, 1796 ambient intelligence (AmI) environment 137–144 ambient intelligence (AmI) space 351 ambient intelligence (AmI), in elderly healthcare 140 ambient intelligence (AmI), in tourism 140 ambient intelligence (AmI), vehicles and transports and 140 ambient interaction 147, 151, 152 ambient learning 1562 ambient media 1626, 1627, 1628, 1629, 1630, 1632, 1633, 1634, 1635, 1636, 1637, 1640, 1641 ambient media principles 1634 ambient multimedia 718 AmbientRoom project 1788 America’s Digital Schools (ADS) 511 AmigoTV 687, 691, 694, 704 analytic hierarchy process (AHP) 1056, 1057, 1060, 1227 analytic hierarchy process (AHP) methodology 1219

Volume I, pp. 1-580 / Volume II, pp. 581-1197 / Volume III, 1199-1797

Index

annotation, a primitive operator 224 ANOVA tests 1106, 1115, 1116, 1117 antenna systems, scanned beam 653 antennas 102, 1374 antennas, smart 648–677 antennas, smart, automatic RFID and 648–677 Anycall 947 Anycall Land 947 anywhere, anytime, by anything and anyone (4As) networking 157 application scenario 1442 ARPANET 1316 ARPU 943 arterial blood pressure, diastolic (ABPdias) 865 arterial blood pressure, systolic (ABPsys) 865 artifact 343, 346, 351 artificial intelligence (AI) 2, 4, 6, 7, 8, 121, 129, 138, 1436, 1446, 1576, 1720, 1723 aspect-oriented programming (AOP) 1584 aspect-scale-context (ASC) 1648 asymmetric intelligence 1574 asynchronous JavaScript and XML (AJAX) 220 attention levels, primary 147, 148 attention levels, secondary 147, 148 attention levels, tertiary 147, 148 attitude 1100, 1105 attractiveness of alternatives (AA) 1115 audio-based memory aid 370–388 augmented reality (AR) 552 authentic learning 552 authentication 1209, 1216,1429, 1430, 1434, 1435, 1436, 1437, 1438 authentication processing framework (APF) 1374 authentication processing framework (APF) database 1376, 1377, 1381, 1384, authentication processing framework (APF) key 1376 authentication processing framework (APF) system 1376, 1378, 1382, 1383, 1384, 1385 Auto-ID Centre 1305, 1309, 1311 automated identification and data capture (AIDC) 83, 109 automated inventory control systems (AICS) 83 automatic identification (auto-ID) 54, 596–597, 1299 automatic identification and data capture (AIDC) 65, 78, 81 automation 997, 1002, 1003, 1004 autonomic computing 276, 294 awareness 1583, 1585

2

B B3GSOAP 471, 473, 480, 483 backend internal database 92 barcode technology 51, 53, 83, 597–622,1099, 1291 behavior 1100, 1105 behavioral intention of use (BI) 1115, 1116, 1117 behaviorism 1565 beyond-third-generation (B3G) 728 bio-media 718 bio-MEMS 965, 971 biometrics 612 biosensors 971 bioterrorism 957, 958, 969, 970, 971 bioterrorism attacks 959, 960 black box 29, 34 Bluetooth 193, 236, 536, 552, 854, 855, 856, 865, 866, 963, 1200, 1206, 1207, 1211, 1212, 1214, 1218 Boeing 599, 607 bridging the gap 1331 broadband telecommunication 942 broadcast and mobile networks 353 browsing adaptive network for changing user operativity (BANCO) 220 buddy list 680 business models 1053, 1054, 1055, 1056, 1057, 1059, 1060, 1061, 1062, 1064, 1065 business models, customer oriented 1054 business process model notation (BPMN) 256 business, process, management 1693 business-to-business (B2B) 1285 bystander apathy 1417

C calm technology 146, 511, 519 calm technology environments 517 calm technology era 516 calm technology, age of 511 camera 710 carbon monoxide (CO) 1182 care tracking 782 case-based reasoning 1014 Centers for Disease Control (CDC) 961 CheckSite 828 choice, forced 1418 city healthcare 802, 803, 807, 808, 809, 812 classes dimension 428, 433 client/server paradigm (CSP) 183

Index

client-server network 182 clinical context sensitivity 769 CLIP framework 941, 942, 944, 949, 952 Coca-Cola 947 code division multiple access (CDMA) 942 cognition 1100, 1105 cognitive agents 123 cognitive processing 313, 318 cognitive style 314, 317, 320, 321, 329 collaborative business items (CoBIS) 1573 collaborative learning 535, 552 collaborative technologies, e-mail 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329 collective laissez faire 1337 communication systems 702 communication tools 512 communication, ambient 1316, 1325 communication, closed-form 681–682 communication, computer-mediated 1050 communication, near field (NFC) 1068 community-oriented workflows 253 complex behavior 1572 composite capabilities / preferences profile (CC/PP) 1655 computer aided design (CAD) 807 computer supported collaborative care (CSCC) 1504 computer supported collaborative learning (CSCL) 1742 computer supported collaborative work (CSCW) 214, 1742 computer-aided design (CAD) 214 computer-based training (CBT) 1565 conceptual model 462, 466, 468, 469, 470, 479 concierge type business model 160 concur task trees (CTT) 257 connectivity 1003 consistency 312 constraints 312 constructivism 899, 1158, 1565 consumer electronics (CE) 719 consumer value 1052, 1053, 1055, 1056, 1057, 1058, 1059, 1060, 1062, 1063, 1064 consumer value preferences 1052, 1053, 1055, 1056, 1057 contactless card 115 contactless smart card 552 context awareness 277, 278, 294, 464, 467, 468, 472, 473, 474, 476, 485, 715, 1080, 1604, 1612 context broker architecture (CoBrA) 1648

context information 1482, 1483, 1485, 1486, 1488, 1494, 1495 context-aware access control 1483–1489 context-aware ecosystems 1079, 1081, 1084, 1085, 1087, 1088, 1089, 1091, 1093 contextual usage 715 contextualization of content 715 continuous trainings 1566 control of reinforcement, locus of 1323 control-flow patterns 257 convergence of technology 715 core competencies (CC) 96, 98 core logic 1033 coupling unit 53 creative funding efforts 514 CRM 948 CRT monitor 1787 cryptographic algorithms 1218 cryptographic protocols 108 cryptography 1481, 1489, 1490, 1495 CT-based education 1582 cultural approach 991 cultural factors 991 cultural shaping of technology 991 customization 584

D data agents (DA) 1241 data aggregation 1525 data management 92, 586 data processing 1435, 1442, 1443, 1445, 1447 data protection directive 1402, 1403 data storage 586 data tampering 104 Daum 953 decision trees 1015 decryption key 1376, 1377, 1378, 1381, 1382 defamation 1463 dehumanisation 1417 denial of service (DoS) attacks 1213 design aid for intelligent support system (DAISY) 216 design ethnography 992 development 539, 543, 545, 546, 549, 967, 971, 1201 development dimensions 428 development environment 463 development framework 433 device layer 1569 diffusion 30

3

Index

digital economy 156 digital imaging 886 digital libraries 931, 938 digital multimedia broadcasting (DMB) 715 digital pen 743 digital video broadcast-handheld (DVB-H) 715 DigitalDesk 743 discovery learning 553 displays, ambient 146 displays, glanceable 146 displays, peripheral 146, 152, 154 displays, visual 146 disruptive technology 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1311, 1313 distributed hash table (DHT) 183 distributed systems 14 distribution centers (DCs) 94 Documents-to-Go 890 domain oriented design environments (DODEs) 215 Dongtan ubiquitous networking 162 DVD player 951 dynamic interaction generation for building environments (DIGBE) 216

E e-commerce 238, 249, 251, 1286, 1292, 1681, 1687 economic development 1614 edgeware 53 educational activities 889 EF Sonata 950 e-insurance 1483 e-learning 1175, 1177 electromagnetic waves 55 electronic article surveillance (EAS) 55, 1294, 1299 electronic product codes (EPC) 46, 53, 57, 67, 81, 602–603, 817, 852, 1227, 1232, 1294, 1299, 1308, 1357 electronic product codes (EPC) data 92 electronic product codes (EPC) specifications 107 electronic program guide (EPG) 724 electronic workplace monitoring 1331 embedded agents 124 embedded intelligence 129 embedded ubiquitous environment 512 emotion capture 1632 emotional ambient media 1626–1642 emotional ambient media design 1637 emotional binding 1629, 1634, 1642 emotional computation 1626, 1627, 1628, 1629, 1630, 1632, 1633, 1636, 1637 emotional computation, techniques 1631

4

emotional models in psychology 1629 emotional processing 313, 315–316, 318, 322, 326 emotional state 1632, 1635 emotions, categorization of 1631 emotions, synthesis of 1633 employee monitoring 1331 employee privacy 1336 employee surveillance 1332 end-user development (EUD), excellence on 213 enhanced message service (EMS) 948, 949 enterprise resource planning (ERP) 45, 83, 94, 1148, 1285, 1292 EPCglobal 67, 73, 76, 79, 81 ePerSpace project 172 e-portfolio 553 e-schoolbag 553 ethnography 985, 990, 992 EU directive 1360 European article numbering (EAN) system 46, 67, 81, 1230 European Space Agency (ESA) 390 EV-DO 942 EV-DO devices 947 EV-DO services 945 everyware 1687 exchangeable routing language (XRL) 256 experience clip 1503 experiential learning 1565 extensive infrastructure 1081 extensive markup language (XML) 852

F fair practices 1344 Faraday cage 108 Federal Trade Commission (FTC) 1360 feedback 312 fibre technologies 158 field service management (FSM) 463, 465, 485 Fimm 947 finite state machine (FSM) 1571 FlowerBlink 1792, 1793, 1794, 1795 FlowiXML 254 Food and Drug Administration (FDA) 587, 808, 811, 826 forecasting 582 forensics 957, 959, 971, 972 formal learning 1565 forward link only (FLO) 715 Foundation for Intelligent Physical Agents (FIPA) 279, 296 friend or foe (IFF) 1351

Index

functional dimension 428, 433 fuzzy logic 1013

G GAIA 1651 galvanic skin response (GSR) 1725, 1726 Gartner hype cycle 532, 553 gateways 28 generic remote usage monitoring production system(GRUMPS) 1322, 1323, 1327 genetic algorithms 1015 genomics 971 geographic information system (GIS) 390, 553, 933, 1618 global basis 960 global healthcare exchange (GHX) 1149 global positioning systems (GPS) 160, 521, 537, 553, 950, 1083, 1229 global system for mobile communication (GSM) 70 global village concepts 1317 GPS location based system 1511 Grandeur XG 950 graphical models 279 graphical user interfaces (GUI) 730 Greenstone 938 grid computing 236, 251 grid computing 35, 36, 37, 38, 39, 40, 42 group purchasing organizations (GPOs) 870 GS1 67, 76, 78, 80, 81 GuideCell 945

H handheld devices 889 handheld readers 90 hash-based access control protocol 106 health management 160 healthcare 623, 624, 625, 629, 630, 633, 635, 636, 644, 645, 1353 healthcare ARSM (H-ARSM) 1536, 1538, 1543 healthcare monitoring, remote 1482–1483 healthcare provision composition 769 healthcare provision, self-care as the primary 771 healthcare supply chains, characteristics of 1145 healthcare toilet 160 Healthcare, RFID in 803 healthcare, societal aspects of 765 healthcare, ubiquitous 845, 852 heart rate (HR) 866 heterogeneity 349 high frequency (HF) 46, 85, 87, 89, 91, 1232

high-speed Internet access 512 home entertainment (HE) 717 Hospitals, RFID implementation in 815–822 human factors study 1005 human learning 1582 human-computer interaction (HCI) 191, 212, 310, 326, 354, 939, 1584 human-machine interface 1005 human-societal perspective 768 hypermedia 1582 Hyundai Kia 950

I icon recognition test 364 identification (ID) 1387 identifies privacy 1367 identity management systems, requirements of 1070 identity management systems, security infrastructure 1074 identity management, for wireless service access 1067–1078 identity management, solutions & controversies 1070 identity theft 613 image virtual object (IVO) 1740, 1744 implicit learning 1565 IMT-2000 942 IMT-2000 network 951 inbuilt volatile memory 91 individual differences 310, 311, 326 inductive coupling 1292 industrial, scientific, and medical (ISM) applications 88 industrialization 1608 informal learning 553, 1565 information integration 145, 146, 148 information overload 145, 146, 149 information retrieval 1549 information sharing 585 information, embeded 148 informative art 146 infotainment 712 infrastructures 1601, 1602, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1624 injunctions 1463 in-network processing 1518, 1524, 1526 innovation diffusion theory 30, 34, 1106, 1116 innovation translation 34 innovations 29 innovative convergence services 353 input/output (I/O) controller 90

5

Index

inquiry-based learning 553 instant messaging (IM) systems 1511 INSTINCT project 353 institutionalized funding 514 integrated circuits (ICs) 1386 integrated offering 1053, 1064 integration management 92 integration of technology 513 intelligent music system 1721, 1725, 1732, 1733, 1735 intelligent network nodes (INNs) 164 intelligent network servers (INS) 164 intelligent product (IP) 1240 intelligent product agents (IPA) 1241 intelligent transportation system (ITS) 490, 1619 intelligent user interfaces (IUIs) 131, 309–329 interactive digital entertainment (IDE) 1742 interactive drama architecture (IDA) 1747 interactive table 741 interactive whiteboards 553 International Telecommunications Union (ITU) 162 Internet protocol (IP) 972, 1200, 1687 Internet service provider (ISP) 30 interoperable systems, building 1620 interpersonal environment 512 interrogation zone 1299 interrogators (readers) 83, 84, 89, 91, 92, 95, 102, 107 interruptibility 1325 invention 29, 34 inventory reduction 584 invisible information gathering 1357 IS literature 1079, 1080, 1081 iTransIT framework 488 iTransIT management system 492

J Java context awareness framework (JCAF) 278, 295 Jeil Advertising 948 job manager agent (JMA) 1241 June 953 jurisdiction 1450, 1452, 1462, 1463

K K’merce 946 K’merce service 946 K’merce-banking 946 K’merce-lottery 946 K’merce-stock 946 K’merce-ticketing 946

6

Kelly, Kevin 4, 5, 7 Keystone Architecture Required for European Networks (KAREN) project 490 key-value models 279 KGP agents 125 KGP model 124 Kill Function 1357 Kill Operation 1394 kill tag approach 107 killer application 948 kilobyte 1386 knowledge asset management type business model 160 knowledge hoarding 1051 knowledge management 1050, 1051 knowledge representation 1051 knowledge sharing 1039, 1051 knowledge transfer 1051 Kodak PalmPix® 891 Korea Information Security Agency 953 Korea Internet Information Center 951 Korea Securities Computer Co. 954 Korea Wireless Internet Standardization Forum 954 Korea, IT infrastructure of 1606 Korean households 941 Korean mobile communication 943 Korean mobile market 942, 943 KTF 947

L L’Oreal 947 Law, choice of 1452, 1463 LCD devices, color 947 lead user 1022, 1027, 1028, 1029, 1034, 1036, 1037 learning environment design models 515 learning management systems (LMS) 554 learning motivation 513 learning needs 516 learning object 554 learning space 1177 learning styles 516 learning theories 1564 left-to-right (LTR) 227 LG 942 LG Telecom 947 lifelong learning 554 limited access 512 line-of-sight fashion 1285 literacy skills 888 living cookbook 200 location-based services (LBS) 35, 36, 950, 1618

Index

locations 1528, 1530, 1532, 1535, 1536, 1537, 1538, 1541, 1543, 1544 locations, alphabetical, time, category hierarchy (LATCH) 1506 logic-based models 279 long running processes 1572 Lotte 947 low frequency (LF) 46, 85, 87, 89, 91, 1232 low-fi prototypes 1512 low-tech prototyping 1182

M mAdnet 947 m-advertising 946 Magic Land 908, 909, 910, 911, 912 magnetic resonance images (MRIs) 226 manufacturing execution system (MES) 45 manufacturing resource planning (MRP) 94 mapping 312, 710 markup scheme models 279 mash-ups 554 Massachusetts Institute of Technology (MIT) 650, 1288 massively multiplayer online role-playing games (MMORPGs) 1746, 1752 m-banking 944 memo pad 890 mentoring 535, 546, 554 messaging service 1553–1561 Mfreei 949 m-government 952 microbial forensics 957, 959, 972 microchip 1374 micro-content 554 micro-learning 554 Microsoft Windows Workflow Foundation (WWF) 256 middleware 53, 85, 90, 91, 92, 93, 102, 166, 351, 462, 463, 466, 468, 471, 473, 475, 477, 480, 481, 484, 485, 1299, 1674 middleware agent (MWA) 1241 middleware suite 462, 466, 471, 485 MIMOSA 1398, 1399, 1401, 1402, 1404, 1405, 1406 Ministry of Information and Communication 952 Ministry of Post and Telecommunications 952 mixed reality 1738, 1739, 1740, 1742, 1746, 1747, 1751, 1759 mobile advertising 946 mobile applications, designing for 385 mobile classroom 555 mobile communications 167

mobile computing 35, 36, 37, 42 mobile computing 939 mobile devices 35, 37, 38, 40, 41, 167 mobile devices, processing power limitations of 167 mobile education 519 mobile Internet service 943 mobile leader 942 mobile learning 515, 1160, 1178 mobile networks 464, 484 mobile phone 942, 943, 945, 946, 947, 949, 950, 953, 955, 956 mobile public certification service 954 mobile spam 953 mobile technologies 519, 941, 942, 952, 1179 mobile technologies, use of 512 mobile technology, use of 512 mobile wallet service 945 MobiLife 1400, 1404, 1407 Mobitel 949 model-driven architecture (MDA) 414, 415, 416, 437, 1584 model-driven development (MDD) 409, 413, 414, 417, 418, 419, 427, 428, 434, 435 model-driven engineering (MDE) 254, 1584 MOGATU 1650 Moneta 944 monitoring 1532, 1533, 1542 motivation, two factor theory of 1413 motivation/ability framework 1301, 1302, 1308, 1309, 1311, 1312 Moving Picture Experts Group (MPEG), video coding standard 7, 41, 43 m-payment 944 multi-frequency operation readers 90 multi-frequency tags 90 multimedia 39, 43 multimedia annotation of digital content over the Web (MADCOW) 216 multimedia message service (MMS) 948, 949 multimodal interaction 1653, 1656, 1668 multi-play offering 1055, 1057, 1058, 1059, 1062, 1064 multiplayer online games (MMOG) 1738, 1744, 1746 multiplayer ubiquitous games (MUG) 1738, 1739, 1745, 1746, 1747, 1751, 1753, 1754, 1755, 1757, 1758, 1759 multi-radio devices 471 MundoCore 15, 16, 17 mutual cognition function (MCF) 1767, 1768, 1771, 1772, 1775, 1779, 1780

7

Index

mutual cognition function agent (MCFA) 1767

N n.Top 945 nano-learning 555 nanotechnology 886 Nate 945 Nate service 949 natural media 718 navigational aid for blind pedestrians 389–407 navigational aid for blind pedestrians: activity-centered approach 396 navigational aid for blind pedestrians: aids 390 navigational aid for blind pedestrians: user-centered approach 393 near field communication (NFC) 1068 nearest-neighbor algorithm 1015 Neptune Orient Lines (NOL) 1311 network business model 1021, 1022, 1023, 1033, 1034, 1037 network effect 1292 network money (NeMo) service 944, 945 network selection and billing 166 networking 156 networking technology 157 neural networks 1016, 1019 new economy 1615 new media 551 New Zealand, ubiquitous networking in 163 next G 529, 548, 555 next generation networks (NGN) 330, 331, 349 NFC mobile phone 117 none of your business (NOYB) 1409, 1423 Norman, Donald 4, 5, 7, 8 n-Zone service 952

O OB beer 947 object name services (ONS) 47, 848, 852, 1227, 1232 object name services (ONS) design 1225 object-oriented models 279 one-to-one (1:1) computing 511, 518 one-to-one (1:1) computing, goal-state 512 one-to-one (1:1) computing, origins 512 one-to-one (1:1) ubiquitous computing 512 online adaptivity 1530–1534 online learning 543 ontology 335, 336, 350, 351 ontology-based models 279

8

optical character recognition (OCR) 1098, 1105 orchestration 1576 Organization for Economic Cooperation and Development (OECD) 941, 1144 organizational model 259 Ovum 948 OxyContin 587 Oxygen project 505

P packet routing 157 participatory design (PD) 1184 patient profiling 785 p-business infrastructure 1200, 1218 peer-to-peer (P2P) 721 peer-to-peer (P2P) support 514 perceived ease of use (PEOU) 1112, 1114, 1115, 1117 perceived risk (PR) 1115 perceived usefulness (PU) 1106, 1112, 1114, 1115, 1116, 1117 peripheral oxygen, saturation of (SpO2) 866 permission-based marketing 953 person, objects, situations, time, and activity (POSTA) framework 1506 personal area network (PAN) 555 personal audio loop (PAL) 371 personal audio loop (PAL), final prototype of 377 personal audio loop (PAL), formative evaluation of 373 personal audio loop (PAL), social and legal acceptance of 384 personal audio loop (PAL), ubiquity of 383 personal audio loop (PAL), usefulness of 375, 382 personal computer (PC) 192 personal computing 171 personal computing device 511 personal computing era 511 personal device 1436, 1438, 1444 personal digital assistants (PDA) 866, 939, 1300, 1584 personalization 310, 311, 318, 319, 322, 324, 325, 326, 1012 pervasive business 240, 252 pervasive commerce 238, 252 pervasive computing 19, 35, 36, 37, 40, 41, 43 pervasive computing (PC) 234, 186, 276, 248, 249, 252, 277, 278, 279, 280, 281, 294, 295, 296, 409, 410, 411, 413, 436, 437, 438, 503, 765, 1106, 1788, 1797 pervasive computing environment 1450

Index

pervasive healthcare (PH) 765 pervasive information systems (PISs) 408, 411, 413, 427, 428, 430, 433, 434, 435, 1106, 1107, 1117 pervasive iTV 707, 715 pervasive loop 780 pervasive m-learning 555 pervasive services 505 Petri nets 256 physical markup language (PML) 47, 1227, 1232 physiological signal interface 1725 Platform for Privacy Preferences (P3P) 1467 platform variation 271 plug and play (PnP) 174 podcasting 549, 555 point of sale (POS) 83, 94 portables with wireless access 512 portals 28 powerful devices 471 presence 680 primary-context ontology (PCOnt). 495 printers 102 privacy 586, 949, 1465–1480 privacy-enhancing technologies (PETs) 1357, 1430, 1431, 1432 problem-based learning 555 process management 623, 625, 629 process model 258 product codes 57 profile matching 786, 787 profit-and-loss pattern 894 progression model 256 project-based learning environments 516 proof of delivery (PoD) 583 propitient multi-agent system 187 protocol 848, 852 prototyping 683, 702 psychology, emotional models of 1629 public domain 1332 purely virtual object (PVO) 1740, 1744, 1746, 1756 putaway accuracy 94

Q quality function deployment (QFD) 1056, 1057, 1060, 1061, 1062, 1065 quality of experience (QoE) 1499, 1500 quality of experience, perceived (PQoE) 1500 quality of service (QoS) 38, 1068, 1500, 1681 quality of transmission (QoT) 1500 questionnaires 710 QWERTY keyboard 453

R radio frequency (RF) 55, 65, 79, 80, 81 radio frequency identification (RFID) 44, 53, 54–64, 65, 79, 80, 81, 112, 521, 581, 594–622, 623–647, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 1098, 1106, 1098, 1105, 1106, 1107, 1108, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1109, 1227, 1229, 1250, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1350, 1358, 1359, 1360, 1374, 1385, 1386, 1396, 1397, 1398, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1618 radio telephones (porto-phones) 199 rapid ethnography 199 reactive composition 1575 reactive composition approaches 1576 really simple syndication (RSS) 536, 555 real-time location systems (RTLS) 872 real-time tracking 1224 real-world connections 512 reference services 939 reflection 1529, 1530, 1534, 1535, 1536, 1537, 1538, 1539, 1541, 1542, 1543, 1547 rehabilitation medicine 624, 628, 644 reminding service 1552–1561 Renault-Samsung 950 research and development (R&D) 1617 resource agents (RA) 1241 resource allocation 158 resource description framework (RDF) 216 resource-based view 1054, 1065, 1066 resource-limited devices 471 restructuring 1614, 1622 retail 1354 returnable transport items (RTI) 1235 reverse mentoring 514, 519 RFID applications 1145 RFID chips 783, 945, 1286 RFID enabled systems 809 RFID frequencies 604–605 RFID implementation decisions 1254, 1263, 1269, 1273, 1274, 1277, 1279, 1280 RFID initiatives in the healthcare space 818 RFID protocol 1391 RFID readers 53, 1299, 1394 RFID readers 600, 601, 603, 652, 1227 RFID readers, API 92 RFID readers, fixed 90 RFID readers, portable 90

9

Index

RFID readers, single frequency operation 90 RFID software 1227 RFID specifications 817 RFID systems 1375, 1376, 1379, 1380, 1381, 1384 RFID tags identifier 1286 RFID tags, 77, 81, 1099, 1105, 1227, 1294, 1295, 1296, 1298, 1299, 1358, 1361, 1362, 1363, 1364, 1369, 1370, 1372, 1374, 1375, 1379, 1380, 1381, 1382, 1383, 1384 RFID tags, combination 602 RFID tags, Gen-2 105 RFID tags, passive 85, 86, 600–602, 1357, 1388, 1394 RFID tags, price of 1256, 1257, 1259, 1262, 1264, 1268, 1269, 1274, 1275, 1280 RFID tags, read/write 602 RFID tags, read-many 85 RFID tags, read-only 85, 86, 88, 602, 1357 RFID tags, read-write 85, 86, 88, 1357 RFID tags, semi-active (semi-passive) 85 RFID tags, semi-passive 601 RFID tags, single frequency 90 RFID tags, write-once 85 RFID tags, write-once read-many (WORM) 86, 88 RFID technology 1103, 1144, 1284, 1286, 1288, 1289, 1290, 1301, 1302, 1311, 1312, 1313, 1331, 1355, 1358, 1360, 1369, 1371, 1372, 1394 RFID technology, retained scenario integration 1151 RFID, active tags 85, 89, 600–602, 1357, 1388, 1394 RFID, history of 54 RFID, technical fundamentals of 816 RFID/SC 1301, 1302, 1303, 1304, 1305, 1309, 1310, 1311, 1312, 1313 RFID-related privacy protection 1358 Right-to-Left (RTL) 227 RoamAD 164 RoamAD’s metro Wi-Fi networks 164 ROI 1253, 1254, 1261, 1262, 1263, 1264, 1273, 1274, 1275, 1279, 1280, 1281 ROI estimation 95 routing 1199, 1207, 1215, 1218 routing scheme 472

S Samsung 942 satellite systems 158 savant 53 scenario focus 466 SCORM specification 559 SCORM-compliant textbooks 557

10

security 92, 167, 583 security mechanisms 1389, 1394 security services 1389,. 1394 semantic context management 1647–1654 semantic technologies 1643, 1644, 1647, 1654, 1656, 1657, 1658, 1661, 1662, 1663, 1665 sensor analysis system 1725, 1730 sensor databases 1521, 1522, 1523, 1524, 1526 sensor networks 158 sensor query processing 1526 sentient artifacts 1787, 1792 sentient computing 555, 1786, 1787, 1788, 1796 sentient displays 1786, 1787, 1788, 1789, 1793, 1794, 1795, 1796 Seoul 947 serialized shipping container code (SSCC) 46 serialized shipping container code (SSCC) 46 server/client paradigm (SCP) 183 service composition 1575 service level agreements (SLAs) 468, 484 service migration 1770 service platform for innovative communication environment (SPICE) 1652, 1653, 1655, 1657, 1660, 1668 service validation 467, 476 service, interaction patterns 1705, 1707, 1708 service-oriented architecture (SOA) 1526, 1550– 1561, 1688, 1689, 1690, 1704, 1709, 1711, 1714, 1715 service-oriented context-aware middleware (SOCAM) 1651, 1653 services, real-time 1602 session initiation protocol for instant messaging and presence leveraging extensions (SIMPLE) 1660, 1666, 1667 set-top box (STB) 725 sharable content object reference model (SCORM) 557 shelf life 583 short message service (SMS) 949 simple authentication and security layer (SASL) 106 simple behaviors 1570 simple, object access protocol (SOAP) 1689, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1714 Singapore 20 singulation 1394 situated learning 556, 1158 situation awareness 1004, 1005 SK Telecom (SKT) 944, 947 Skimming 1357 SKT Moneta 944

Index

slow technology 146 smart antennas 648–677 smart cards 83, 552, 556 smart classrooms 556 smart dust 556 smart environments 252 smart labels 599, 601 smart objects 36, 37, 40, 41 smart products 1562, 1567 smart products, conceptual architecture of 1569 smart tag approach 108 smart technology 519, 1562 smart technology era 516 smartphones 939 sociability 715 social ambient intelligence 1643, 1644, 1647, 1658, 1659, 1661, 1662, 1663, 1665 social awareness 715 social interactions, mediation of 1081 social knowledge 1051 social learning 1565 social networking 1659, 1660, 1661 social networks 30 social usage 715 socio-technical elements 1079, 1081, 1082, 1085, 1091, 1093 SocioXensor 200 Software Shaping Workshop (SSW) methodology 212, 217 SOUPA 1648, 1649 South Korea and Japan, networking infrastructure in 162 South Korea, ubiquitous networking in 162 speech recognition 453 speech synthesis 937 spontaneous service emergence paradigm 187 SSW, system’s architecture 219 statechart diagrams 256 stationary technologies 1079, 1080, 1081, 1082, 1084, 1091, 1093 stimulus, organism, and response (SOR) 1099 stock verification 583 Stockman, H. 649 strategic planning 1613, 1615 stress, work-related 1316, 1317, 1318, 1319, 1320, 1321, 1323, 1324, 1326, 1327, 1328, 1329 STV 679–683 STV1 684 STV2 684 STV3 685 substantive law 1463

supply chain coordination 1284, 1285 supply chain management (SCM) systems 45, 80, 81, 83, 95, 1293, 1302, 1306, 1307, 1308, 1310, 1311, 1313 supply chain visibility 1284–1292, 1292 Sure.com 949 sustainable development 1613 switched beam array antenna 655 symbiotic application (SYMA) 1767, 1768, 1771, 1772 symbiotic computing 1762, 1763, 1764, 1766, 1782 symmetric intelligence 1573 symmetric intelligence, limitations of 1573 synthesis 1576

T TADEUS project 215 tag authentication 106 tag identity (ID) 1388, 1395 tag placement and tag orientation (TPTO) 104 tags 80, 81, 91, 1386, 1394, 1395 tangible objects 1325 task & concepts (T&C) 261 task advertiser 482–484 task assigner 477–480 task model 258 task planner 480–482 task variation 271 TCP/IP 346, 348 technological innovation 34 technology acceptance model (TAM) 30, 34, 1106, 1112, 1117 technology, adoption of 514 technology, user acceptance of 1106, 1112, 1113, 1117, 1120, 1121 Telecom New Zealand 163 Telecommunication Technology Association 954 telematic systems 950 telematics 1619 TeleTables 731, 732 television, anti-social 697–699 television, high definition (HDTV) 727 television, interactive (iTV) 715, 723, 1500 television, interactive digital (IDTV) 719 text chat 682 TGIF 947 third wave of computing 511 third-party logistics (3PLs) 1099 three-dimensional cognitive model 313 3D Live 906, 907, 908, 910, 911, 912 3D live technology 907

11

Index

Thumb Talkers 949 ticketing systems 1108, 1109, 1110, 1111, 1113, 1115, 1116 transponders (tags) 53, 83, 84, 85, 88, 89, 91, 101, 108, 788, 1300, 1374 TV, mobile 715 TV, pervasive 715 TXT-a-Park button 164 TXT-a-Park machine 164 TXT-a-Park service 164

U ubicomp ,learning applications of 1563 Ubicomp Conference Series 21 ubicomp environments 1079 ubicomp paradigm 23 ubicomp technologies 521 ubicomp, experience design and 193 ubicomp, features of 192 ubicomp, kitchen applications of 200 ubicomp, research and development of 20 ubiquitous access 513 ubiquitous automobile network services 160 ubiquitous cities 1615 ubiquitous cities in practice 1615 ubiquitous cities, building challenges of 1619 ubiquitous cities, technologic opportunities 1617 ubiquitous communication systems 1052, 1053 ubiquitous computing (ubicomp) 20, 130, 138, 183, 191, 350, 351, 352, 409, 410, 411, 436, 437, 438, 503, 511, 516, 519, 520, 547, 550, 939, 957, 989, 990, 1005, 1022, 1023, 1024, 1025, 1026, 1027, 1031, 1033, 1035, 1036, 1106, 1425, 1445, 1447, 1442, 1444, 1445, 1426, 1427, 1428, 1429, 1430, 1432, 1433, 1435, 1436, 1437, 1439, 1440, 1441, 1445, 1425, 1426, 1425, 1430, 1482, 1491, 1463, 1562, 1601, 1602, 1603, 1605, 1609, 1610, 1612, 1644, 1645, 1658, 1667, 1670, 1671, 1676, 1677, 1682, 1684, 1685, 1687, 1689, 1709, 1712, 1714, 1715 ubiquitous computing (UC) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18 ubiquitous education 513 ubiquitous functions (UF) 1766, 1768, 1771, 1776 ubiquitous geographic information (UBGI) 1618 ubiquitous learning 513 ubiquitous learning design guide 514 ubiquitous learning environment (ULE) design guide 515

12

ubiquitous learning environment (ULE) model 513, 516 ubiquitous learning environment design models 515 ubiquitous mobile input 439–461 ubiquitous mobile input, continuous interactions, direct 443 ubiquitous mobile input, continuous interactions, indirect 441 ubiquitous mobile input, design space, spatial layout of 454 ubiquitous mobile input, design space, textual layout of 451 ubiquitous mobile input, discrete interactions, direct 444 ubiquitous mobile input, discrete interactions, indirect 444 ubiquitous mobile input, input devices, design space of 440 ubiquitous mobile input, input devices, orientation of 448 ubiquitous mobile input, input devices, positioning tasks of 441 ubiquitous mobile input, positioning techniques of 445 ubiquitous networking 156, 157, 159, 941, 1026, 1034, 1037 ubiquitous networking business models 160 ubiquitous networking environment 159, 161, 166 ubiquitous networking, applications of 160 ubiquitous networking, basics of 156 ubiquitous networking, concept of 157 ubiquitous networking, global evolution of 156, 161 ubiquitous networking, infrastructure of 157 ubiquitous networking, issues in 165 ubiquitous networking, selection and billing of 166 ubiquitous robots 516 ubiquitous services 1021, 1022, 1023, 1025, 1026, 1027, 1029, 1030, 1031, 1032, 1033, 1034, 1037 ubiquitous solutions for pain monitoring and control in post-surgery patients (uPAIN) 419, 420, 421, 422, 423, 425, 426, 427, 429, 432 ubiquitous technologies 193, 511, 515 ubiquitous technologies, administrative guidelines for support of 513 ubiquitous technologies, embedding 511 ubiquitous technology environment 514 ubiquitous technology research frameworks 516 ubiquitous technology waves 513 ubiquity 1026, 1030, 1601, 1602, 1603, 1605, 1607, 1608, 1609, 1610, 1612

Index

u-City 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611 uEyes 1771, 1772, 1773, 1774, 1776, 1780 UI, abstract (AUI) 261 UI, concrete (CUI) 261 UI, final (FUI) 262 u-Korea 952 ultra high frequency (UHF) 46, 85, 88, 89, 91 ultra high frequency tags (UHF) 1232 ultra-wideband (UWB) 852 Uniform Code Council (UCC) 67, 81 uniform resource accessibility 514 unique item identifier (UID) 106 unit profit to tag price (UPTP) ratio 1279 universal product code (UPC) 46, 1230, 1293, 1300 universality 1026, 1030 unremarkable computing 146 user agent profile (UAProf) 1655 user agents (UA) 3, 4, 6, 7, 1471 user authentication 1491–1493 user driven innovation 1022, 1027, 1037 user experience (UX) 716 user experience modeling 992 user friendliness 40, 42 user interface 1437 user interface continuum 356 user interface description language (UIDL) 260 user interfaces (UIs) 254 user profiles 41, 310, 321 user variation 271 user-centered design (UCD) 354 user-centered design cycle 353 uses and gratifications (U&G) 1502

V validation framework 463 value co-creation 1031, 1032, 1034, 1035, 1037 value creating networks 1021, 1022, 1023, 1026, 1030, 1031, 1032, 1034, 1035, 1037, 1038 value networks 1053, 1054, 1055, 1056, 1059, 1061, 1062, 1063, 1306 value networks, multi-play 1052, 1053, 1055, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065 verbal guidance systems for blind pedestrians 393 very nervous system (VNS) 1723 video conferencing 692 videotaped activity scenario (VASc) 1411, 1412 views 1528, 1529, 1535, 1536, 1537, 1538, 1540, 1541, 1543, 1544 vigilance 1005

virtual patients 772 virtual reality (VR) 2, 4, 6, 7 virtual worlds 1156, 1159, 1160 visibility 312 visual barcode 556 vodcasting 556 voice and facial recognition 83 voice chat 682

W wallpaper 943 Wal-Mart Corporation 59, 582, 587 WANs, wireless 158 warehouse management systems (WMSs) 83, 92, 93 wearable computing 249, 556 Web 2.0 145, 514, 1647, 1662, 1663 Web ontology language (OWL) 279, 296 Web pages, interactive 889 Web portals 28, 32 Web services 278, 280, 290, 291, 1688, 1689, 1690, 1691, 1692, 1693, 1695, 1697, 1698, 1705, 1706, 1707, 1709, 1710, 1712, 1714, 1715, 1716 Web services distributed management (WSDM) 277, 278, 280, 290, 291, 292, 293, 294, 295, 302 Web technologies 630 Web, services, business process execution language (WS-BPEL) 1689, 1697, 1698, 1699, 1700, 1704, 1707, 1715, 1719 Web, services, choreography 1689, 1692, 1697, 1715 Web, services, choreography definition language (WS-CDL) 1689, 1706, 1707, 1708, 1716 Web, services, interface definition (WSDL) 1689, 1691, 1693, 1695, 1696, 1697, 1699, 1707, 1711, 1714 Web, services, orchestration 1692, 1693, 1697, 1698 Web3D 912, 928 Web-based training technologies (WBT) 1565 Weiser, Mark 1, 2, 3, 4, 6, 7, 8, 17, 409, 410, 411, 438 what you see is what you get (WYSIWYG) 561 Wibree 1206, 1218 WiBro 942 wide area measurements business model 160 wide area network (WAN) 174 wide-bandwidth range 157 Window Seat 731, 734 wired networking technologies 157 wired networks 157 wireless access 511, 512 wireless channel technology 158

13

Index

wireless communication technologies, short-distance 158 wireless computing devices, Internet-connected 512 wireless connectivity 513 wireless fidelity (Wi-Fi) 1203, 1218 wireless hotspot service 163 wireless Internet platform interoperability 954 wireless local area networks (WLAN) 158, 866 wireless MANs 158 wireless media access 157 wireless networks 157, 250, 251, 940 wireless security 1387, 1395 wireless sensor data 1489–1491 wireless sensor networks (WSNs) 815, 817, 819 wireless service access, and identity management 1067–1078 wireless technologies 886, 1106 Wizard of Oz testing 1502 WordSmith 890 workflow data patterns 257 workflow editor, Petri nets-based 253 workflow information system (WIS) 254 workflow patterns 1689, 1700, 1701, 1703, 1715, 1717

14

workflow resource patterns 257 workflow systems, UI development of 260 workflows 1573 workflows, conceptual modelling of 258 workplace monitoring 1336 workplace variations 271 worldwide interoperability for microwave access (WiMAX) 1607 write-once/read-multiple (WORM) 46

X Xforms 1218 XML documents 93 Xpointe 216

Y Yahoo! 953 YA-TRAP protocol 1391 yet another workflow language (YAWL) 256

Z ZigBee 855, 856, 866 zoonotic pathogens 961