Healthcare Paradigms in the Internet of Things Ecosystem 9780128196649

Table of contents :
HEALTHCARE PARADIGMS IN THE INTERNET OF THINGS ECOSYSTEM
Copyright
Contributors
Preface
About the editors
About the book
1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications
1. Introduction
2. Internet of Things
3. Architecture of IoT
4. Enabling technologies of IoT
4.1 Radio frequency identification
4.2 Electronic product code
4.3 Wireless sensor network
4.4 Near field communication
4.5 Actuator
4.6 ZigBee
4.7 Z-Wave
4.8 Bluetooth LE
5. Applications of IoT
5.1 National
5.2 Transportation
5.3 Healthcare
5.4 Home and personal use
5.5 Community and social utilities
5.6 Smart environment
5.7 Industrial
5.8 Agriculture
5.9 Futuristic
6. Research challenges and issues in IoT
6.1 Massive scaling or addressing
6.2 Creating knowledge, Big Data, and data mining
6.3 Interoperability
6.4 Cloud computing
6.5 Fog computing
6.6 Security
6.7 Privacy
6.8 Standardization
7. Summary
References
2. IoT for healthcare industries: a tale of revolution
1. Introduction
2. Component and mechanism of IoT
2.1 Sensor
2.2 Sensor classification
2.3 Uses of sensors in internet of things
2.4 Gateway
2.5 How does an IoT gateway device work?
2.6 Need for gateway devices in IoT
2.6.1 Reducing the crack between operational technology and information technology
2.6.2 Additional security level
2.6.3 Real-time updates in the field
2.7 Communication network
2.7.1 Satellite
2.7.2 Satellite communication—advantages and disadvantages
2.7.3 Wi-Fi
2.7.4 Advantages and disadvantages
2.7.5 Radio frequency
2.7.6 Advantages and disadvantages
2.7.7 Radio frequency identification
2.7.8 Advantages and disadvantages
2.7.9 Bluetooth
2.7.10 Near-field communication
2.7.11 Modes of NFC
2.7.12 Advantages and disadvantages
2.8 Data analytics
2.8.1 Data analytics and IoT combined—business impact
3. IoT in healthcare
3.1 IoT—the new resident doctor
3.2 History of IoT in healthcare
3.3 Challenges in current healthcare system
3.4 IoT healthcare services and applications
3.5 IoMT industrial market
3.6 IoT healthcare market—target audience
4. Reducing healthcare costs with IoT
5. Challenges of using IoT in the healthcare segment
6. Conclusion
References
3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data in healthcare systems
1. Introduction to Big Data and IoT
1.1 Types of Big Data
1.2 Advantages of Big Data processing
1.3 Architecture of IoT
1.4 Standards of IoT applications
2. Relationship between Internet of Things and Big Data
3. Role of Big Data and IoT in Healthcare Systems
4. Architecture of Apache Flume and Spark
4.1 Components of Apache Flume
4.2 Components of Apache Spark
5. Data analytics for IoT using Big Data analytics
6. Big Data pipeline for IoT data storage and processing
6.1 Proposed Big Data pipeline for processing IoT medical data
6.2 Hybrid prediction model for diabetes detection
6.2.1 Step procedure of DBSCAN algorithm
7. Conclusion and future directions
References
Further reading
4. The role of Internet of Things for adaptive traffic prioritization in wireless body area networks
1. Introduction to wireless body area network
2. Hardware architecture of biomedical sensor
2.1 Methods for deployment of biomedical sensors
2.2 Data dissemination of sensory information
2.3 Requirements of BMSs in WBAN
3. Applications of medical sensors
4. Data dissemination protocols in WBAN
4.1 Routing protocols
4.2 Medium access control protocols
5. Introduction to Internet of Things
5.1 Applications of IoT
5.2 Data dissemination architecture using healthcare-IoT
6. Open issues of WBAN
7. Open issues of IoT
8. Conclusion
References
Further reading
5. Spatiotemporal pattern and hotspot detection of malaria using spatial analysis and GIS in West Bengal: an approach to medic ...
1. Introduction
2. Data and methods
2.1 Study area
2.2 Spatial data collection, integration, and management
2.3 Measures of disease about its occurrences
2.4 Spatial filtering and smoothing
2.5 Inverse difference weighted method
2.6 Hotspot detection and analysis
3. Results and analysis
3.1 Spatial analysis
3.2 Spatial distribution of malaria diseases
3.3 High-low clustering of malaria distribution
3.4 Hotspot analysis of malaria distribution
4. Discussion
4.1 Challenges to controlling the malaria of West Bengal
5. Recommendations
6. Conclusions
References
Further reading
6. Integration of Cloud and IoT for smart e-healthcare
1. Introduction
2. Related work and application areas
3. Background terms
3.1 Internet of Things driven healthcare
3.1.1 Hierarchical architecture
3.2 Cloud computing for healthcare
4. Cloud IoT integration for healthcare systems
5. Cloud IoT complimentary aspects and drivers for integration
6. Architecture framework for healthcare system
6.1 Data acquisition/sensing layer
6.2 Data transmission/sending layer
6.3 Cloud processing/storing layer
7. A Conceptual Healthcare Scenario
8. Design considerations for healthcare architecture
9. Cloud IoT security threats and issues
9.1 Security features and goals
9.2 Security vulnerabilities
9.2.1 IoT issues and limitations
9.2.1.1 Issues in perception layer
9.2.1.2 Issues at network layer
9.2.1.3 Issues at application layer
9.2.2 Cloud service issues
9.3 Potential defense strategies
10. Platforms and services
10.1 Platforms
10.1.1 Available platforms
10.1.2 Available services
11. Challenges and open issues
11.1 Security and privacy
11.2 Protocol support and need for standards
11.3 Efficient power usage
11.4 Delay and limited bandwidth
11.5 Quality of service
12. Discussion and conclusion
References
Further reading
7. IoT-based location-aware smart healthcare framework with user mobility support in normal and emergency scenario: a comprehe ...
1. Introduction to smart and remote healthcare system
2. Introduction of location-aware healthcare system with mobility support
2.1 Functional framework
2.2 Application in healthcare
2.3 Location-aware protocols
3. Learning techniques for healthcare system
3.1 IoT-based healthcare applications
3.2 Learning techniques used for IoT-based healthcare applications
3.2.1 Supervised learning
3.2.1.1 Instance-based learning
3.2.1.2 Decision tree (J48)
3.2.1.3 Multilayer Perceptron
3.2.1.4 Ensemble learners
3.2.2 Unsupervised learning
3.2.2.1 k-means clustering
3.2.2.2 Density-based spatial clustering of applications with noise
3.2.3 Deep learning techniques
3.2.3.1 Convolutional neural network
3.2.3.2 Recurrent neural network
3.2.3.3 Open research issues
References
Further reading
8. Remote health monitoring protocols for IoT-enabled healthcare infrastructure
1. Introduction
2. System architecture for IoT-based healthcare
2.1 Wearable sensing devices
2.2 Wireless body area network
3. Potential applications
3.1 Activity monitoring
3.2 Healthcare monitoring
3.3 User-centric applications
4. Research challenges
4.1 Energy efficiency
4.2 User mobility
4.3 High degree of diversity
5. Overview of protocol standards
5.1 IEEE 802.15.4 standard
5.2 IEEE 802.15.6 standard
5.3 IEEE 802.11 standard
5.4 Bluetooth technology
6. Energy-aware protocols for IoT-based healthcare applications
6.1 MAC and routing protocols for Tier-1 architecture
6.2 Routing protocols for tier-2 architecture
7. Overview of protocols for proactive health monitoring systems
8. Open research issues
9. Conclusions
References
9. Wearable sensor networks for patient health monitoring: challenges, applications, future directions, and acoustic sensor ch ...
1. Introduction
2. Key enabling technique of a wearable body network: sensing
2.1 Sensing technology
3. Human activity monitoring and e-health sensors
3.1 E-health sensors
4. Computable indications in healthcare monitoring
4.1 Vital signs
4.2 Body motions
4.3 Metabolism sensors
5. Transportable devices
5.1 Wrist devices
5.2 Head-mounted devices
5.3 E-textiles
6. Attachable devices
6.1 Wearable skin patches
6.2 Contact lens
6.3 Implantable devices
6.4 Ingestible pills
7. Textile-based wearable devices and appliances
7.1 Textile-based electronic devices and systems
7.2 Textile-based energy devices
8. Measurement mechanism
8.1 Response time and physical phenomenon
8.2 Robustness
8.3 Sensitivity and linearity
8.4 Wireless communication
8.5 Self-power
8.6 Multifunctional sensing
8.7 Intellectualization
9. Wearable sensor applications
9.1 Safety monitoring
9.2 Assessment of treatment efficacy
9.3 Early detection of disorders
9.4 Home rehabilitation
10. Underwater wireless sensor network
10.1 Underwater sensor network architecture
10.2 Routing protocols and challenges
10.3 Underwater sensor network applications
10.4 Challenges and requirements
11. Short note on next-generation sensor networks
12. Conclusions and future work
References
Further reading
10. RFID technology in health-IoT
1. History
2. Introduction
3. IoT and RFID technology
3.1 Internet of Things
3.2 Common challenges of IoT
3.3 RFID technology
3.4 RFID tags
3.5 RFID reader
4. Functions of RFID technology
5. Applications of RFID technology
6. Challenges of RFID technology
7. IoT healthcare networks
7.1 The IoThNet topology
7.2 The IoThNet architecture
7.3 The IoThNet platform
7.3.1 IoT healthcare challenges and open issues
8. IoT healthcare services
8.1 The internet of m-health things
8.2 Ambient assisted living
8.3 Community healthcare
8.4 Adverse drug reaction
8.5 Children health information
8.6 Semantic medical access
8.7 Wearable device access
8.8 Indirect emergency healthcare
8.9 Embedded context prediction
8.10 Embedded gateway configuration
9. IoT healthcare applications
9.1 Electroccardiogram monitoring
9.2 Glucose level sensing
9.3 Body temperature monitoring
9.4 Blood pressure monitoring
9.5 Rehabilitation system
9.6 Oxygen saturation monitoring
9.7 Imminent healthcare solutions
9.8 Medication management
9.9 Wheelchair management
9.10 Healthcare solutions using smartphones
10. IoT healthcare industry developments and status
11. IoT system for in-home healthcare
11.1 Health-IoT
11.2 The Food-IoT
12. Conclusion
References
Further reading
11. Principles and paradigms in IoT-based healthcare using RFID
1. Introduction
2. IoT-based healthcare architecture
3. Why need IoT
4. Challenges in IoT healthcare
4.1 Security and privacy issue
4.2 Data accuracy
4.3 Connectivity
4.4 Compatibility
4.5 Privacy
5. IoT-driven system in IoT healthcare
5.1 IoT-based infant check system
5.2 A Medical-IoT-based framework
5.3 Heterogeneous Internet of Medical Things platforms
6. RFID technology and its working
6.1 Tag
6.2 Antenna
6.3 Management system
7. Current RFID technology
7.1 Energy source
7.2 Passive tag
7.3 Advantages of passive tag
7.4 Disadvantages of passive tag
7.5 Active tag
7.6 Advantages of active tag
7.7 Disadvantages of active tag
7.8 Semi-passive tag
8. RFID in healthcare
8.1 Patient monitoring
8.2 Patient tracking
8.3 Monitoring section
8.4 Tracking section
8.5 Hospital equipment monitoring
8.6 Drug management system
8.7 Hospital supply chain management system
8.8 Human implantation
8.9 Blood management system using RFID
9. RFID technology for IoT-based personal healthcare
10. RFID security concern
10.1 Security issue of tag data
10.2 Eavesdropping
10.3 Spoofing
10.4 Daniel service attack
10.5 Traffic analysis
10.6 RFID reader issues
10.6.1 How to make RFID tag protected
10.6.2 Protection of RFID reader
11. Recommendation
12. Conclusion
References
Further reading
12. Low-cost system in the analysis of the recovery of mobility through inertial navigation techniques and virtual reality
1. Introduction
2. Materials and methods
2.1 Proposal
2.2 Gait analysis
2.3 Hardware component
2.4 Inertial navigation unit
2.5 Data acquisition unit
2.6 Wireless transmission unit
2.7 Virtual reality unit
2.8 Video game unit
2.9 Physical support unit
2.10 Registration unit
2.11 How the proposal works
3. Results
4. Conclusions
Bibliography
13. Control and remote monitoring of muscle activity and stimulation in the rehabilitation process for muscle recovery
1. Introduction
2. Materials and methods
2.1 Control unit
2.2 Evaluation unit
2.3 Stimulation unit
2.4 Computer application unit
2.5 How the proposal works
3. Results
4. Conclusions
Bibliography
14. Healthcare technology trade-offs for IoT ecosystems from a developing country perspective: case of Egypt
1. Introduction
2. Current state of the art
3. Architecture and components
4. Architecture and components
4.1 Requirements and limitations
4.2 Feasibility and effectiveness
4.3 Security and privacy
5. Emerging applications
6. Opportunities and challenges for the future
7. Conclusions
References
15. Study of asian diabetic subjects based on gender, age, and insulin parameters: healthcare application with IoT and Big Data
1. Introduction and background
1.1 History of IoT
1.2 IoT as medical healthcare
1.3 Future perspective of IoT
2. Big Data characteristics
2.1 Applications of Big Data: healthcare
2.2 Role of Big Data in IoT
2.3 Big Data tools
2.4 Big Data security
3. Tension-type headache
3.1 Right time to seek medical emergency?
3.2 Preventions
3.3 Tension-type headache, stress, and its causes
3.4 Types of stress
3.5 Stress symptoms
3.5.1 Emotional stress
3.5.2 Physical symptoms of stress
3.5.3 Cognitive symptoms of stress
3.5.4 Behavioral symptoms of stress
3.5.5 Long-term stress effect
3.6 Handling of stress
4. Migraine versus TTH
4.1 Chronic headache
4.2 Hypertension
5. Mental health
6. Diabetes mellitus and its symptoms
6.1 Type-1 diabetes
6.2 Type-2 diabetes
6.3 CVD, CAN, and CAD
6.4 Insulin
6.5 Hypoglycemia
6.6 Depression
6.7 Obesity
6.8 Diets for diagnosing the Type-2 diabetes
7. Literature survey
8. Results, interpretation, and discussion
9. About the study and analysis
9.1 Gender distribution
9.2 Age group distribution
9.3 Age group and gender distribution
9.4 Analysis: TTH distribution in the sample
9.5 Diabetes type distribution in the sample
9.6 Diabetes and insulin consumption
9.7 Diabetes type and gender distribution
10. Novelties in our work
11. Future scope, limitations, and possible applications
12. Tableau S/W, applications with benefits
13. Recommendations and future considerations
14. Conclusion
Acknowledgments
References
Further reading
16. Design and development of IoT-based decision support system for dengue analysis and prediction: case study on Sri Lankan co ...
1. Introduction
2. Internet of things, cloud computing, and fog computing
2.1 Internet of things
2.2 Internet of medical things
2.3 Cloud computing
2.4 Fog computing
3. IoT-based decision support system for dengue analysis and prediction
3.1 Fuzzy Rule Neural Classification method for disease prediction and diagnosis for dengue severity
4. Proposed architecture for dengue analysis and prediction decision support system
4.1 Proposed system functionalities
4.2 Data sources
4.3 Patient health data
4.4 Medicinal data
4.5 Cloud layer
4.6 Fog computing layer
4.7 IoT security layer
4.8 Drug and food recommendation system
4.9 Dengue analysis and prediction flowchart diagram
4.10 Use case diagram
5. Conclusion and future works
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
Z

Citation preview

HEALTHCARE PARADIGMS IN THE INTERNET OF THINGS ECOSYSTEM Edited by

VALENTINA EMILIA BALAS Professor Department of Automatics and Applied Software University Aurel Vlaicu Arad, Romania

SOUVIK PAL Associate Professor Department of Computer Science and Engineering Global Institute of Management and Technology West Bengal, India

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-819664-9 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Mara Conner Acquisitions Editor: Chris Katsaropoulos Editorial Project Manager: Amy Moone Production Project Manager: Sreejith Viswanathan Cover Designer: Christian J. Bilbow Typeset by TNQ Technologies

Contributors Aya Sedky Adly Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt

Bhanu Chander Department of Computer Science and Engineering, Pondicherry University, Pondicherry, India

Afnan Sedky Adly Faculty of Physical Therapy, Cardiovascular-Respiratory Disorders and Geriatrics, Laser Applications in Physical Medicine, Cairo University, Cairo, Egypt; Faculty of Physical Therapy, Internal Medicine, Beni-Suef University, Beni-Suef, Egypt

D.K. Chaturvedi Dayalbagh Educational Institute, Agra, Uttar Pradesh, India Pruthviraj Choudhari Department of Computer Science Engineering, MANIT, Bhopal, MP, India

Mahmoud Sedky Adly Royal College of Surgeons of Edinburgh, Scotland, United Kingdom

Chandreyee Chowdhury Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

Navneet Arora Indian Institute of Technology, Roorkee, Uttarakhand, India

Mónica Diaz Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Muhammad Waseem Ashraf GC University, Lahore, Pakistan

Trina Dutta Department of Chemistry, JIS College of Engineering, Kalyani, West Bengal, India

Wilver Auccahuasi Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Edward Flores Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Justiniano Aybar Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Alfonso Fuentes Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Valentina Emilia Balas Department of Automatics and Applied Software, University Aurel Vlaicu Arad, Romania

Vishal Goyal Department of Computer Science, Punjabi University, Patiala, India

Grisi Bernardo Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Mayank Gupta Tata Consultancy Noida, Uttar Pradesh, India

Madelaine Bernardo Universidad Continental, Facultad de Ingeniería, Huancayo, Perú

Aboobucker Ilmudeen Department of Management and Information Technology, Faculty of Management and Commerce, South Eastern University of Sri Lanka, Oluvil, Sri Lanka

Riddhi Kumari Bhadoria Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, West Bengal, India

Services,

Sweta Jain Department of Computer Science Engineering, MANIT, Bhopal, MP, India

Heena Farooq Bhat Department of Computer Science, University of Kashmir, Srinagar, J&K, India

Waqas Khalid The Lahore, Pakistan

University

of

Lahore,

Asif Iqbal Khan Department of Computer Science, University of Kashmir, Srinagar, J&K, India

Suparna Biswas Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, West Bengal, India

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Contributors

Prateeti Kumar Quantitative Trading Department, Edelweiss Financial Services, Mumbai, India Kumaravelan Department of Computer Science and Engineering, Pondicherry University, Pondicherry, India P lalitha Surya Kumari Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, India Sushobhan Majumdar Department of Geography, Jadavpur University, Kolkata, West Bengal, India Elizabeth Oré Universidad Continental, Facultad de Ingeniería, Huancayo, Perú Shabir Ahmad Parah Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India Subhadeep Pramanik Analytics Lab, Marsh & McLennan Companies, Mumbai, India Chi-Man Pun Department of Computer and Information Science, University of Macau, Macau SAR, China Mamoon Rashid School of Computer Science & Engineering, Lovely Professional University, Jalandhar, India Rohit Rastogi ABES Engineering Ghaziabad, Uttar Pradesh, India

College,

Sathi Roy Jadavpur University, Kolkata, West Bengal, India Jayita Saha Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India Santosh Satya Indian Institute of Technology, Delhi, India Mamta Saxena Director General, Min. of Statistics and Planning, GoI, New Delhi, India Fernando Sernaque Universidad Continental, Facultad de Ingeniería, Huancayo, Perú Junaid Latief Shah Department of Information Technology, Sri Pratap College, Cluster University Srinagar, Srinagar, J&K, India Ayushi Shrivastava Department of Computer Science Engineering, MANIT, Bhopal, MP, India Harjeet Singh Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India Parul Singhal ABES Engineering Ghaziabad, Uttar Pradesh, India Shahzadi Tayyaba Lahore, Pakistan

College,

The University of Lahore,

Fasee Ullah Department of Computer and Information Science, University of Macau, Macau SAR, China Aabid Rashid Wani Department of Electronics & Communication, Shri Mata Vaishno Devi University, Katra, India

Preface This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Internet of Things (IoT)-enabled healthcare technologies. It also provides a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of IoT healthcare. This book aims to attract researchers and practitioners who are working in Information Technology and Computer Science. This book is about basics and high-level concepts regarding healthcare paradigm in the context of Internet of Things. It is becoming increasingly important to develop adaptive, patient-centric, energyaware, secure, and privacy-aware mechanisms in IoT-based health applications. The IoT-enabled healthcare mechanisms are required to develop a smarter mankind using IoT-enabled technologies. The book serves as a useful guide for industry persons and also helps beginners to learn things from basic to advance in the area of better healthcare. The book is organized into 16 chapters. Chapter 1 discussed IoT as a diversified subject due to its varied meanings and perceptions and requires sound technical knowledge and understanding before its use. It will lead to the development of efficient mechanisms with high scalability and interoperability features among the things or objects. IoT is a reality that is progressing day by day, connecting billions of people and

things to form a vast global network. IoT has applications in various domains like agriculture, industry, military, and personal spaces. There are potential research challenges and issues in IoT that act as a hurdle in the complete exploration of IoT in real-time implementation. Various organizations and enterprises have encouraged further research and study in IoT, which would prove essential in the global acceptance of IoT. Chapter 2 focus on how devices are used and the way world interacts with current healthcare system; it can be said without any doubt that IOT is bringing ground-breaking revolution in healthcare industry. This has severe enactment scope, but is not limited to hospitals, patients, insurance agencies, families, etc. Here we will talk about how IoTbased tactics are transfiguring healthcare industries in contrast with traditional approach. Chapter 3 presents a new Big Data pipeline solution for storing and processing IoT medical data. The proposed Big Data processing platform uses Apache Flume for efficiently collecting and transferring large amounts of IoT data from Cloud-based server into Hadoop Distributed File System for storage of IoT-based sensor medical data. Recursive Feature Elimination with Cross Validation (RFECV) is used for eliminating the features of less importance. Apache Spark is to be used for processing this realtime data. Next the authors propose the use of hybrid prediction model of density-based spatial clustering of applications with noise (DBSCAN) to remove sensor data outliers

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Preface

and provide better accuracy in diabetes disease detection by using Random Forest machine learning classification technique. The authors believe that this Big Data pipeline will greatly help in efficient storage of IoT application medical data and will provide a viable solution for effective processing and predicting disease from medical IoT data. Chapter 4 deals with the concept of monitoring vital signs of patients and sends the detected reading of vital signs of patients to medical doctors for optimal actions. There are routing and medium access control (MAC) protocols discussed in detail with pros and cons regarding dissemination of patient’s data toward the body coordinator and medical staff. In the same way, IoT has introduced to cover up the limitations of WBAN in transmission of data with the architectural design. At the end, some open challenges are discussed of WBAN and IoT. Chapter 5 presented spatiotemporal diffusion pattern of the malaria affected areas in the places near the Kolkata district. By the district-level analysis it has been found that the urban areas are more affected by the malaria than the rural areas. The hotspot analysis showed the spatial pattern of malaria disease. This study represents useful information about the malaria affected areas of West Bengal. It may help the health departments of West Bengal and policymakers to make planning strategies to prevent from malaria. The methodology used in this malaria study can also be applied to the other studies like dengue, influenza, etc. Chapter 6 explores seamless applications dispensed by Cloud IoT platform and contemplates discussion on factors driving Cloud IoT health integration. The chapter presents a conceptual architectural framework for healthcare monitoring system that considers a range of aspects including data collection, transmission, and processing

including cloud storage. The chapter also discusses a use case scenario that identifies actors and data flows responsible for transforming sensor data into real-time transmission to cloud. Also, a brief discussion on design considerations for healthcare architecture will be provided. The work in this chapter also highlights security issues affecting IoT layered architecture including vulnerabilities inherent in the Cloud. These vulnerabilities could render healthcare services nonfunctional and critical patient information can be abused by malevolent users. Also a brief discussion on some potential mitigation measures will be provided. The chapter also elaborates discussion on various Cloud IoT platforms that aim at solving heterogeneity issues between the Cloud and Things. Finally, the chapter concludes by identifying some open research issues and challenges hampering Cloud IoT ebased healthcare adoption. Chapter 7 analyzes the IoT-based healthcare which is getting immensely popularized because it is cost-effective, user-friendly, intelligent, and efficient providing locationaware support both in normal and emergency scenarios. Here functional framework of IoT-based healthcare with the state-of-theart literature survey have been illustrated, followed by location-aware protocols, learning techniques for intelligent healthcare, and future research directives igniting interests of researchers in this emerging domain. Chapter 8 aims how quality of treatment can be improved through constant medical supervision of patients under free-living conditions which augment the existing medical infrastructure. Challenges such as providing energy efficiency, timely data delivery, and reliable delivery of health data need to be taken care of while designing protocols for such a system. In this chapter, the system architecture for remote health

Preface

monitoring, issues in designing protocols for such a system are discussed. Both proactive system and energy-aware state-of-the-art protocols are thoroughly reviewed along with protocol standards. Open issues are also described to indicate the need to work further in this domain. Chapter 9 discussed regarding detailed preface to wearable sensor networks, sensing technology, human activity monitoring computable indications and respective measurements, electronic-based wearable sensor devices, and energy resources technologies and various wearable sensor appliances. Wireless transmission in underwater is one of the enabling technologies for the development of future ocean observation technology, and it is one of the speedily rising skills. Underwater sensor networks (UWSNs) are a collection of sensors and autonomous vehicles that position to perform monitoring appliances. UWSNs gather records in association with seismic, robots, and pollution monitoring appliances. Energy-saving methods is one of the prominent issues in UWSNs where routing protocols play the main role since underwater sensor node batteries are difficult to replace. Preface of underwater sensors and its architecture, routing protocols and their methods, applications of UWSNs, and issues with future challenges are discussed briefly. Chapter 10 focuses history, introduction of IoT and RFID technology, challenges of RFID technology, applications of RFID technology, IoT healthcare networks, IoT healthcare industry developments and status, system for in-home healthcare, the foodIoT survey of technologies, and applications of Internet of Things. Chapter 11 is about RFID in healthcare with IoT. The main focus has been given on IoT and RFID in healthcare. After the discussion of IoT, in second half of this chapter

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authors have discussed the RFID technology in healthcare including working principle and implementation in healthcare system. Finally some security concern of this technology has also been discussed with solution. Chapter 12 is based on the use of video game technologies with a virtual reality approach based on the video game “Minecraft,” as mechanism of registration of the displacement at the moment of the march, an inertial navigation circuit is used to register the direction, displacement, deviation at the time the child walks interacting with the game, giving the feeling that the child is inside the video game; interaction and control is done wirelessly using a Bluetooth connection or a Wi-Fi connection depending of the distance between the child and the workstation; the results in the tests show that the child has better acceptance at the time he performs the rehabilitation exercises and at the time he moves to analyze the progress because it is part of the video game; as well as you can record the movements made and can graph it to assess its center of gravity and linearity in the displacement. Chapter 13 exemplified control and remote monitoring of muscle activity and stimulation in the rehabilitation process for muscle recovery. Chapter 14 focuses on the evolution of the Internet of Things over the past years that has impacted a new era of healthcare ecosystems in the context of Egypt. IoT-based healthcare ecosystems consist of interconnected devices which communicate with each other in order to monitor, collect, process, share, and analyze data by secured means with the support of Cloud computing and Big Data analytics. Elderly, patients who need rehabilitation, or those with chronic diseases usually need expensive long-term care which could be reduced by the IoT

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health services with the assistance of sensors and wearable devices. For a developing country such as Egypt, an efficient architecture that is compatible with the feasible technologies, requirements, and market demands is currently considered mandatory for integrating Internet of Things with healthcare and ecosystems standards and technologies. This chapter will be addressing these concerns along with the emerging applications and challenges for the future. Chapter 15 aims to analyze diabetes with the latest IoT and Big Data analysis techniques and its correlation with stress (TTH) on human health. Authors have tried to include age, gender, and insulin factor and its correlation with diabetes. IoT helps us to connect each other, i.e., it is known a smart connecting thing (a sort of “Universal Global Neural Network” in Cloud). It comprises of smart connecting machine with other machine, object, and a lot more. Big Data refers to huge sets of data which are also large enough in terms of variety and velocity. Due to this, it becomes more difficult to handle, organize, store, process, and manipulate such data using traditional techniques of storage and processing. Stress especially TTH (tension-type headache) is a serious problem in today’s world. Now every person in this world is facing headache and stressrelated problems in daily life. This chapter tries to make some study over that.

Chapter 16 presents a fresh approach in fuzzy ruleebased neural classification with IoT, Cloud computing, and fog computing to analyze and predict dengue outbreak. The proposed fog-driven IoT architecture where each component is seamlessly connected to each other to execute disease management, preventative care, clinical monitoring, early warning systems, e-medicine, and drug and food recommender system. This IoT-based decision support system aims to stop, control, and enable forecasting of eruptions of dengue, facilitating medical officers the information and insights to handle the outbreak, well in advance. We are sincerely thankful to Almighty to supporting and standing at all times with us, whether it’s good or tough times and given ways to concede us. Starting from the call for chapters till the finalization of chapters, all the editors have given their contributions amicably, which it’s a positive sign of significant teamwork. The editors are sincerely thankful to Chris Katsaropoulos for providing constructive inputs and allowing an opportunity to edit this important book. We are thankful to a reviewer who hails from different places in and around the globe shared their support and stand firm toward quality chapter submission. Valentina Emilia Balas Souvik Pal

About the editors Valentina Emilia Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a PhD in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 300 research papers in refereed journals and international conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling, and Simulation. She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board of several national and international journals, and evaluator expert for national, international projects, and PhD Thesis. Dr. Balas is the director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in eight editions 2005e18 held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair, and member in Steering, Advisory or International Program Committees.

She is a member of EUSFLAT, SIAM, a senior member of IEEE, member in TCd Fuzzy Systems (IEEE CIS), member in TCd Emergent Technologies (IEEE CIS), and member in TCdSoft Computing (IEEE SMCS). Dr. Balas was past Vice-President (Awards) of IFSA International Fuzzy Systems Association Council (2013e2015) and is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), a multidisciplinary academic body, India. Dr. Balas is the director of the Department of International Relations, Programs and Projects and Head of the Intelligent Systems Research Centre in Aurel Vlaicu University of Arad, Romania. Dr. Souvik Pal is an Associate Professor in the Department of Computer Science and Engineering, Global Institute of Management and Technology, West Bengal, India. Prior to that, he had been associated with Brainware University, Kolkata; JIS College of Engineering, Nadia; Elitte College of Engineering, Kolkata; and Nalanda Institute of Technology, Bhubaneswar. Dr. Pal has received his B. Tech, M. Tech, and PhD degrees in the field of Computer Science and Engineering. He has more than a decade of academic experience. He is author/co-editor of 12 books from reputed publishers like Elsevier/Springer/CRC Press/Wiley, and he is owner of 3 patents. He is the series editor of Scrivener-Wiley Publishing, USA. Dr. Pal

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has published a number of research papers in Scopus/SCI-indexed international journals and conferences. He is the organizing Chair of RICE 2019, Vietnam; RICE 2020, Vietnam; ICICIT 2019, Tunisia. He has been invited as a Keynote Speaker in ICICCT 2019, Turkey; ICTIDS 2019, Malaysia. His

professional activities include roles as Associate Editor/Editorial Board Member for more than 100 international journals/ conferences of high repute and impact. His research area includes Cloud Computing, Big Data, Internet of Things, Wireless Sensor Network, and Data Analytics.

About the book The Book is intended to discuss the evolution of healthcare-related issues using Internet of Things (IoT). The main focus of this volume is to bring all the IoT-enabled healthcare-related technologies in a single platform, so that Undergraduate and Postgraduate students, Researchers, Academicians, and Industry people can easily understand the IoT-based healthcare systems. The book focuses on functional framework workflow in IoT-enabled healthcare technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT-enabled healthcare ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book will take the readers on a journey that begins with understanding the healthcare monitoring paradigm in IoT-enabled technologies and how it can be applied in various aspects. It walks readers through engaging with real-time challenges and builds a safe infrastructure for IoT-based healthcare. This book helps researchers and practitioners to understand the e-healthcare architecture through IoT and the state-of-the-art in IoT

countermeasures. It also differentiates heterogeneous platforms in IoT-enabled infrastructure from traditional ad hoc or infrastructural networks. It provides a comprehensive discussion on functional framework for IoT-based healthcare systems, intelligent medicine box, RFID technology, HMI, cognitive interpretation, BCI, remote health monitoring systems, wearable sensors, WBAN, and security and privacy issues in IoT-based healthcare monitoring systems. This book brings together some of the top IoT-enabled healthcare experts throughout the world who contribute their knowledge regarding different IoT-based e-healthcare aspects. This book aims to provide the concepts of related technologies regarding patient-care and medical data management, and novel findings of the researchers through its Chapter Organization. The primary audience for the book incorporates specialists, researchers, graduate understudies, designers, experts, and engineers who are occupied with research and healthcarerelated issues. The book will be organized in independent chapters to provide readers great readability, adaptability, and flexibility.

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C H A P T E R

1 The fundamentals of Internet of Things: architectures, enabling technologies, and applications Sweta Jain, Pruthviraj Choudhari, Ayushi Shrivastava Department of Computer Science Engineering, MANIT, Bhopal, MP, India

1. Introduction With the increasing use of the Internet and its variety of applications, there is an increase in the number of interconnected and Internet-connected devices. Nowadays, the Internet is used in every type of organization, such as academic, research, business, personal use, etc. There is rapid development and enhancements in the field of Internet which provides motivation for further research in the same or connected domains. The current generation has become quite habitual of Internet use, and life without it seems to be impossible; everyday work and even household chores make use of the Internet. It seems technology is gradually becoming human-centric over the years. This evolution of technology will remove any kind of lack of transparency among the people, their businesses, and things. It is not only affecting people or businesses but also the workplaces and homes. Over the coming 10 years, smart machines or smart object technologies will be very much in use. One of these smart technologies is the Internet of Things (IoT). Fig. 1.1 is a pictorial representation of the IoT. It is a fast-developing network of sensors deployed over a huge variety of things or objects. The primary components of IoT, namely, sensors, communication networks, and actuators, are expected to induce automation in all fields. The primary aim is to develop a smart environment where the collection and exchange of data between all entities are possible. IoT focuses on the improvement of all aspects of human living by simplifying complex physical systems to virtual systems. Gartner hype cycle 2016 (NIH Statement on Sharing Scientific Research) shows that one of the trending topics of the present time is the IoT. The studies done by Cisco estimated that the expected number of Internet-connected objects by 2020 would be around 50 billion, which is only 2.77% of the 1.8 trillion things that are capable of being connected (The Internet of

Healthcare Paradigms in the Internet of Things Ecosystem https://doi.org/10.1016/B978-0-12-819664-9.00001-6

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© 2021 Elsevier Inc. All rights reserved.

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

FIGURE 1.1

A pictorial representation of the Internet of Things.

Things and big data: Unlocking the power). It is also predicted that in future, the number of Internet-connected devices will be six times the total population (Business Insider: Chart: By 2020).

2. Internet of Things The IoT refers to the intricate internetworking of daily use objects or devices, programmed hardware, software, sensors, and network connections. The internet connections can be wired or wireless, things can be living or nonliving. Sensors are devices that are embedded or attached to the things with the ability to sense or read data and also store the data which can be used for further analysis. Using such devices or things, for example, with the use of a simple smartphone that we carry, our day-to-day activities can be simplified, automated, tracked, or analyzed efficiently. The fundamental elements of IoT, such as sensors, sensor networks, real-time localization, machine learning, etc., have been put to use for many years in the field of information technology (Whitmore, Agarwal, & Xu, 2014). There is no universal or formal definition of the concept of IoT that has been invariably accepted across the globe until now. However, there are many definitions that are helpful in understanding the meaning of IoT clearly. The term Internet of Things originated in the 1980s and was devised in the year 1999 at MIT when it was introduced by Mr. Kevin Auston, the Executive Director at the Auto-ID Labs at MIT, and that idea spread worldwide from there onward. IoT is also considered as global integration of physical devices (Kortuem, Kawsar, Sundramoorthy, & Fitton, 2010). These physical devices or smart objects can be divided into three categories based on interactivities, functions, awareness, and representations in terms of programming models that have activity-aware, policy-aware, and process-aware objects. IoT can be defined over three perspectives, namely, the perspective of things or the devices that will be used as the sensing objects, the perspective of Internet or a uniform framework to which all objects are connected, and the perspective of semantics or the communication

2. Internet of Things

3

protocols over which the processing occurs (Yang, Liu, & Liang, 2010). IoT is considered to be a method of universal computing over devices with unique addressing schemes, having the capability to communicate and exchange data among themselves (Agrawal & Vieira, 2013). IoT will enable the objects and people using those objects to be connected in any circumstances, including any place, at any time with anyone or anything, and work on any network or path or service or communication mechanism (Kumar & Patel, 2014). IoT has been defined as an open network of object’s capacity to share resources and data, organize, react, and respond to changes or circumstances in the surroundings, automatically (Madakam, Ramaswamy, & Tripathi, 2015) (Fig. 1.2). In order to understand the actual purpose of IoT and its effect over our lives in the next few years, consider a simple example of a smart home. Let us say, that a person goes off to work and forgets to switch off the lights or cooling system of his house, with the help of smart home infrastructure; he can do the same remotely away from his house. Similarly, when he comes back home from office in the evening, then as he enters his house, the air conditioner in the living room automatically sets the room temperature according to the data received from the temperature sensors placed over his body. Moreover the refrigerator suggests food items like an energy drink or fruits for leveling up energy levels again using the data from sensors placed on his body; the music system sets the music depending on his mood, maybe rock or soft music, the lights adjust themselves to that which comforts the person or his eyes, etc. This is what can happen over a few years from now, with the use of IoT. This proves that IoT will definitely improve our lives to a greater extent. IoT is a paradigm with multiple visions (Atzori, Iera, & Morabito, 2010). The consortium CASAGRAS has defined it beyond the basic radio frequency identification (RFID)-centered approach. The focus in this consortium is about a global vision to provide human-centric services by enabling objects and computer systems to automatically connect to each other as well as among themselves (Dunkels & Vasseur, 2008).

FIGURE 1.2

The major components of IoT are people, data, and things.

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

IoT spreads across a wide band of sectors and can deliver a massive range of functionalities. This will create many employment opportunities as well as profitable gains, in terms of revenue. These sectors include the health industry, environmental industry, consumer-centric industries, etc. Thus, it can be said that IoT will undergo tremendous growth over the coming years providing tremendous opportunities and motivation in research, application, and employment.

3. Architecture of IoT An architectural model is required to understand a system and know how it functions. There have been various proposals for IoT architecture in the past literature. One of the basic IoT architecture mainly consists of three components: (i) hardware, which is made of sensors and actuators; (ii) middleware for data processing and data transfer; and (iii) presentation for ease, understandability, and portability. The architecture has three layers: the application, network, and perception layers (Kumar & Patel, 2014) (Fig. 1.3). The International Telecommunications Union or ITU presented an architecture comprising of the sensing, access, network, middleware, and the application layer (Madakam et al., 2015). The OSI model of networking and this model are quite similar. Similar architectural standards have been proposed by the European FP7 research project and IoT Forum (Madakam et al., 2015) (Fig. 1.4). Another architectural model is given in a pyramidal form (Fig. 1.5), having the applications or services on the top, followed by the IoT management services over the Internet, the gateway functions to provide connectivity, and lastly the things or the sensor devices at the base of the pyramid by (IoT architecturedIoT software and hardware architecture). From the review of various models, the architecture of the IoT can be compiled and generalized in three layers: • Application tier: It is the presentation layer which provides an interface to users, using intelligent computer technology, management services, authentication and authorization services, etc.

FIGURE 1.3 The IoT architectural model (Kumar & Patel, 2014).

3. Architecture of IoT

FIGURE 1.4

FIGURE 1.5

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IoT Forum architecture (Madakam et al., 2015).

Pyramidal architecture (IoT architecturedIoT software and hardware architecture).

• Network tier: It has the communication protocols providing for Internet connections, network infrastructure, and gateway functions. This layer can also be called as the wireless sensor network (WSN) layer. This layer is the most important of all the three layers as it is the main functioning unit of the architecture, just like the central nervous system of the human body or the central processing unit (CPU) of a computer. • Physical tier: It has the data collector things or sensors, RFID, raw data, and real-time information which are coordinated and collaborated to be forwarded to the upper processing layer. Gubbi, Buyya, Marusic, and Palaniswami (2013) have proposed a cloud-centric framework integrating IoT with cloud computing. It has an application layer at the top, cloud computing layer at the middle level, and WSN layer at the bottom.

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

4. Enabling technologies of IoT 4.1 Radio frequency identification RFID system is made of tags and readers. Tags or labels are several in number, have an address, and are attached to objects. A tag is a very small microchip having an antenna. Electromagnetic field is used to send or receive data from an object through a tag. The data is stored on the tags in an electronic format, which can be read by a reader only when the two of them are in a specified range. The reader sends a signal to get the data; the antenna on the tag receives and acknowledges the signal by sending the data along. Readers can be one or more. The tags come in three configurations: • PRAT (Passive Reader Active Tag): The passive reader gets signals from tags working on batteries. The range for transmission is from 1 to 2000 feet. • ARPT (Active Reader Passive Tag): This tag is mostly used in applications. There is no battery power, and so the tags extract energy from the reader’s signal so as to send its own data signal. • ARAT (Active Reader Active Tag): These tags can work over low as well as high frequencies. Proximity is required for signal transmission from both sides in this configuration. Hitachi constructed a tag with the dimensions of 0.4*0.4*0.15 mm. The RFID makes realtime monitoring possible without the need of the person being actually present at the place of monitoring. Some of the applications where RFID is used are inventory management, tracking of products, payments, goods or baggage tracking, and product lifecycle management (Agrawal & Vieira, 2013). These RFID applications can be integrated with other technologies to develop useful and optimized systems. The creation of nanomachines capable of electromagnetic communication, interconnection with micro- or macrodevices as well as with the already present communication networks together will enable the Internet of Nano-Things (Akyildiz & Jomet, 2010).

4.2 Electronic product code An Electronic Product Code (EPC) is a data string that is 96-bit long and is stored on a tag that contains data (Fig. 1.6). These are used for uniquely identifying the tags. The length of the string is coded as follows (Agrawal & Vieira, 2013): • The starting 8 bits represent the header. This header is used for the identification of the version of the protocol being used. • The next 28 bits refer to the unique identification of the organization that manages the data of the tag. • The next 24 bits are used to denote an object class so that the kind of product can be identified. • The next 36 bits are a unique serial number of the tag. • The last two bits are set by the organizations that distributed the tag.

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FIGURE 1.6 96-bit EPC representation.

4.3 Wireless sensor network WSN is a combination of many nodes that have sensors, controllers that are used to sense and monitor the data and the environment interaction. This helps in establishing connectivity between computing devices, individuals, and surroundings. WSN is one of the steering forces behind the IoT (WSN will drive IoT). The processing of the middle network tier of the architecture of IoT is based on WSN. The hardware configuration of a sensor has four parts: power management module, wireless transceiver, sensor, and a microcontroller. The deployment of sensors in a topology, their detection, connection to the network followed by routing, and transmission of information are some of the important tasks in a WSN. The selection of an access network technology such as WLAN (wireless local area network), WMAN (wireless metropolitan area network), WPAN (wireless personal area network), and WWAN (wireless wide area network) depends on the distance and speed of access (Yinbiao et al., 2014). WSN is an essential element of IoT as it helps in combining heterogeneous data, systems, and applications. The WSNs carry the immense potential for becoming a part of IoT. The requirement and consequences of the complete integration of the Internet and WSN are still under study (Alcaraz, Najera, Lopez, & Roman, 2010). There are many challenges faced in the integration of WSN and IoT, such as security, quality of service, configuration, data privacy, and data management. Three integration methods have been discussed, namely, independent network which connects WSN and Internet through a direct gateway, hybrid network with dual sensor node, and access point network with multiple sensor nodes (Christin, Reinhardt, Mogre, & Steinmetz, 2009). The current trend is to use the 6LoWPAN/IPv6 standard in place of the existing ones to establish connectivity in WSN and the Internet, which will facilitate the smart objects to function in IoT applications. IP based, non-IP based, high level, and middleware solutions have been proposed for the challenges faced in several scenarios (Mainetti, Patrono, & Vilei, 2011). Wireless technology is unreliable by nature. Hence, there is a need of low power consuming and reliable WSN network for making wireless sensors easily accessible to the IoT, as there is constant hunger for more and more sensor processed data to store, measure, and analyze those daily activities that are yet to be automated (Yu & Watteyne, 2013).

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

4.4 Near field communication Near-field communication (NFC) technology is used to relay and send data in small amounts between two devices when held nearby, which is similar to RFID. They have a wireless link as they are the outcome of an integrated RFID reader over a mobile phone. They utilize low power and have short ranges. It is a type of radio communication that works by either touching the devices or bringing them closer in the proximity of each other. The operating range of the NFC device, approximately 20 cm, depends upon the size of the device’s antenna. Due to this, remote location NFC oriented communication is not possible, which makes it safer. For example, a person should be physically present at a shop for payment. NFC acts as a significant technology to connect smart objects in IoT. Mobile NFC is another concept that has the potential to put simple devices to great use. Like the transformation of our mobile phones to payment gateways, at times of payments through credit cards (Agrawal & Vieira, 2013).

4.5 Actuator An actuator is a specialized device that is responsible for making motions, using some power source or hydraulic fluid or electric current. It transforms energy from one of these sources into kinetic energy. It can create various types of motions, such as oscillatory, rotary, or simple linear motion. It can cover up to 30 feet of short distances. The speed of communication is usually less than 1 Mbps. Actuators are used in industries with manufacturing or mechanical components. Actuators are of three types, namely, the electrical actuator, which uses motors; a hydraulic actuator, which uses hydraulic fluid; and pneumatic actuator, which uses compressed air. Among these, the electrical actuator is widely in use nowadays (Madakam et al., 2015).

4.6 ZigBee ZigBee is a flexible wireless networking technology that has been developed for shortrange applications by the ZigBee Alliance, founded in 2001. The primary goal of ZigBee is to improve the application of WSN. It is highly scalable and reliable, cheap, and has low power consumption. It works in a range of 100 m and a 250 Kbps bandwidth (Arampatzis, Lygeros, & Manesis, 2005). The architectural protocol stack of ZigBee has four layers, namely, physical layer, medium access control layer, followed by the network layer, and application layer. It supports tree and mesh topology. Applications like home automation, smart energy, industrial applications, medical field, etc., are supported by ZigBee. The first two PHY and MAC layers use the IEEE 802.15.4 standard. A simplified specification of ZigBee called RF4CE is used for star topology only (Mainetti et al., 2011). The ZigBee wireless network can also be used efficiently in a remote health monitoring system (Yu & Liu, 2011).

4.7 Z-Wave Z-Wave is a wireless networking standard that is primarily targeted for home automation applications (Knight, 2006). Home automation systems require a network to relay the control signal from a switch to a central system, but these signals are very rarely used; hence, the ZWave was designed to keep in mind the low data rate requirement but high reliability and

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scalability requirement with low power consumption. Z-Wave has many things in common with ZigBee: both are wireless standards for low power devices, both use mesh topology for networking, and both provide relatively low throughput. There is a certain difference also in the two technologies. Z-Wave is a proprietary technology, unlike ZigBee. Z-Wave supports data rate up to 20 kbps, which is much less as compared to support of 250 kbps data rate of ZigBee. Z-Wave operates in 868 MHz frequency band, whereas ZigBee operates in 2.4 Ghz. Z-Wave has left the encryption for the software side, whereas ZigBee uses the 128bit AES in the hardware (Knight, 2006).

4.8 Bluetooth LE Bluetooth is a standard of wireless technology employed to exchange data between fixed and mobile devices over a short distance using short-wavelength UHF radio waves in the ISM (Bluetooth - Wikipedia). Bluetooth LE (Low Energy), an IEEE 802.15.1 standard, is a subset of Bluetooth standard with a new protocol for very low power applications. Bluetooth operates at 2.4e2.485 GHz (Samuel, 2016). Bluetooth LE is used in healthcare, fitness, beacons, security, and home entertainment industries.

5. Applications of IoT The world currently is undergoing a technological revolution, and the IoT is going to be the most viral phenomenon in the coming future. This technology will not only take over the existing ones but also use them as components to construct even bigger applications than those already running. Machine-to-machine communication has been made possible through the IoT paradigm through the use of private Wi-Fi (Anis, Gadallah, & Elhennawy, 2016). IoT is playing a vital role in many sectors presently. The rest of this section contains some applications of IoT.

5.1 National • Military and defense: This is the most important application of IoT considering it with respect to a national perspective. Using sensors and smart devices helps to simplify tasks, for example, smart devices can be used for reading enemy data, encroachments, unwanted or suspicious activities at the border, communication between personnel, interpreting any wireless signals being sent or received, secure data transfer, etc. • Disaster alerting and recovery: Disaster prone areas can be planted with smart applications and objects to predict natural disasters and take measures accordingly. Using IoT technology, local people of that area can be alerted through application interfaces at homes or in hand that can be used to make them aware of the situations and if any emergency action is to be taken by them can be conveyed easily (Gubbi et al., 2013). • Remote monitoring: IoT can be used to remotely handle many situations at locations where immediate services cannot be provided. These can be rural or tribal areas that are far from cities, where smart objects can be implanted for data collection, analysis, and monitoring for any need or emergency responses.

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

• Utility management: Management of essential utilities provided by the Government can be regulated efficiently so that less wastage occurs. The amount and manner in which such necessities are used can be monitored and controlled accordingly. For example, two of such utilities can be: Smart grid and smart metering of a power supply is the management of electric power supply to people, by calculating where and in which city or state the usage is exceeding or being scarcely available and thereby providing equal supplies to such areas. Smart water supply can be made possible through smart sensors deployed at various locations, which can predict the number of water requirements of specific regions, based on the requirement of humans living in that area, depending on the weather conditions.

5.2 Transportation • Traffic management and public transport: Real-time traffic information collection, storage, aggregation, and analysis can lead to intelligent transportation as well as path optimization for travelers or tourists. This can be done by enabling cars, buses, trains, taxis, and other means of transports with sensors and actuators that have processing capabilities. Traffic problems can also be quickly sorted using such techniques. Traffic information grid (TIG) is an example of an application of IoT in this domain, which has been implemented on the Shanghai Grid (Yang, Liu, & Liang, 2010). • Live parking: Parking of vehicles is a big problem nowadays due to the increased number of vehicles on the road. IoT applications can provide guidance with finding a parking spot that can save time and fuel. Sensors can be buried in parking areas which can detect the arrival and departure of a vehicle. Prior booking of parking by a vehicle can also be implemented. • Assisted driving: The information provided by the sensors planted in vehicles, roads, and railway tracks can be used to assist the drivers in taking the most efficient path to reach the destination. This will help to reduce delays, on-time delivery of goods, traffic, etc. Least time travel and least cost travel can be achieved. • Augmented maps: These are maps with tags that can be mostly used by tourists. Using the NFC enabled smartphones, the tags can help in knowing about nearby hotels, restaurants, places to visit, and directions to reach, by merely scanning the tag and browsing the information received by connecting to the Internet. • Mobile ticketing: Posters or pamphlets or brochures containing advertisements of place can be embedded with NFC tags. Mobile phones can be used to scan the tags and read information about the mode of transport available, stations, cost, number of seats, etc., and book tickets online (Broll et al., 2009). • Emergency response and logistics: At the time of emergency IoT applications can be used to provide fast and efficient services with the minimum waiting time. As discussed above, optimized path selection, minimum congestion, assisted driving, and smart maps are some of the functionalities which can together ensure any fault tolerance, live monitoring, and least delay. For example, providing a way to an ambulance, preventing any rail accident, proper availability of public buses are some uses. The logistics management comprising RFID sensors can be used for emergency response (Xu, Yang, & Yang, 2013).

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5.3 Healthcare • Tracking: A person and his health can be traced using a combination of RFID and NFC technology. For example, the measurement of body temperature, blood pressure, heartbeat rate, etc., using sensors. In case of any emergency, the system can automatically notify the personal doctor or a first aid service or an ambulance to reach a person in need. Using smartphones, SMS services, the Internet, a pulse meter, and a microcontrol unit with Bluetooth modules, a wireless healthcare system has been proposed that finds a nearby doctor and alerts the person’s family in case of any abnormality detected (Yan et al., 2010). • Pharmaceutical products: Medicine and other pharmaceutical products is the key to healthcare. Using sensor technology, smart labels can be used for packaging products, tracking the product all through its supply chain, and monitoring their status. Required storage conditions can be met using the information obtained. • Remote patient monitoring: A monitoring device with a transceiver is attached to the patient, which collects biosignal data from sensors and sends it to the eHealth server through radio access network (Niyato et al., 2009). This is a very promising application of IoT. A clinical case study on diabetes has been conducted, showing the selection of the most suitable channel for information transfer (Sehgal & Agrawal, 2014). For continuous mobile patient biosignal monitoring, a combination of ZigBee and GPRS technology is used (Yu & Liu, 2011). • Smart medical equipment: Smart medical machines can be used to transfer the decisionmaking power from humans to machines to ensure proper healthcare of a patient because a doctor may not always be physically present at all times. An architecture of battery-powered wearable healthcare equipment using the MSP430 microcontroller has been proposed in Warbhe and Karmore (2015). Similarly, machines like MRI, CT scanners, pulse monitors, etc., can be programmed for making critical decisions. • Smart ambulances: In case of a medical emergency like a person getting a heart attack at home or a road accident at a public place, ambulance services should be readily available. A person’s healthcare device notifies the doctor, which in turn can notify the ambulance service to reach the patient or victim on time. Street cameras can send the location of the patient or the accident to the ambulance. The ambulance can then use GPS to track and reach the person.

5.4 Home and personal use • Social networking: This involves sharing and updating of our activities online. At present, there are many social networking sites or web portals that can be integrated with IoT. The real-time updates of the social activities of a user can be given by using location tracking by GPS or RFID (Atzori et al., 2010). The collection of such information from a user can be used by him to know about any of his friends or social event or a famous place nearby to visit. • Payment: Credit card cloning is the payment through the smartphones without the need for carrying the credit card along and has been made possible by technologies such as the NFC. They are safe and have authorization control. • Search engine of things: A search engine of things can be one of the IoT applications, where the last seen location can be obtained. This can be made possible by integrating sensors having location tracking facilities. This will prove to be very helpful and will prevent the loss of essential items in daily life.

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

• Entertainment: The development of real-time games is greatly influenced by IoT. Music systems that can set up music based on the mood of a person, which in turn is read by the sensors on his body, is another example. • Utilities and home appliances: Regular appliances at home, like the refrigerator, lighting system, ventilation, and air conditioning system, power supply, or water pump, can be modified by employing the IoT concepts. Depending upon environmental conditions, the refrigerator can automatically cool the food items up to a specific temperature, an air conditioner can adjust the room temperature, and the lighting system can adjust the brightness according to day or night. A monitoring system for water consumption of households across two countries has been illustrated in Yang et al. (2015). • Security and surveillance: IoT plays a vital role in security automation. IoT devices can be used for the security of homes, buildings, shops, and car parking. Security video feeds from smart cams can be used to monitor the activity in the premises in real-time and may generate responses based on it (Rehman, Asif, & Ahmad 2017).

5.5 Community and social utilities • Food production and sustainability: According to the 2014 National Geographic, about 33% of the total food is misused in the form of wastage of food or food loss (Sustainable Life Media). The issue here is not just to produce a sufficient amount of food but also to produce the yield and use the produced yield in an efficient manner. IoT provides modules to record weather conditions, water level, fertilizer and pesticide usage or spoilage, etc. From these measurements, food crop production can be efficiently managed. • Intelligent security systems: For the safety of a community, sensing terminals can be set up by the use of rotating cameras, access control, electronic bars, or railing to prevent intrusion. Even if an intrusion occurs, the exact position of the intruder can be known using electronic maps, which simultaneously trigger the alarms at homes or the security guard posts. The guards can then track the presence of illegal objects in the premises; automatic lights can turn on by tracing the criminal, video, and image files of the incident can be created. This type of an IoT application has the potential to prevent many crimes occurring nowadays and hence is of great use. • Fire prevention systems: Electrical equipment accompanies grave dangers. Fire alarms can be set up by the use of video monitors, temperature sensors, alarm functions, sensing window fences, cameras, smoke sensors, home network, and sensor networks (Kumar & Patel, 2014).

5.6 Smart environment • Smart homes and offices: IoT has the capability to transparently incorporate heterogeneous systems with functionalities to provide smart services. Its incorporation in our homes and offices will lead to a more comfortable life. Simple applications like room heating, room lighting, food preservation, and other household chores will become automated. Also, domestic accidents can be avoided that are caused due to human carelessness as automation logic will take over.

5. Applications of IoT

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• Smart city: Integrating the IoT enabled smart objects into the physical environment can help in improving the reliability and flexibility of the existing systems. The concept of a smart city is providing functionalities as significant as air quality or traffic monitoring and as small as notifying people when their trash bins are full. An example of IoT deployment with the help of the city municipality is the smart city of Padova, Italy (Zanella, Bui, Castellani, Vangelista, & Zorzi, 2014). • Smart grid: It is a concept that is used for modernizing the electricity grid. Global awareness of energy consumption has been observed, particularly with regard to the measure of the efficiency of energy and the use of renewable energies. There are various characteristics of smart grid infrastructure, which are kind of related to smart homes. Some of them are: providing consumers control options and timely information, integrating smart appliances into consumer devices, deployment of technologies that are smart and automated to optimize the physical operations of devices. The electricity system proves to be flexible and interactive, which gives feedback in real-time by allowing the flow of electricity and information from home to the grid and vice versa (Dragos, Yuxiang, & Petr, 2018). • Smart gym/museum: Not only homes and offices, but museums or gyms or bookstores or conference rooms can be made smart by the use of IoT. The gym experience can be enhanced by programming the machines with user profile having their exercise routines that can only be accessed by the user with his unique identification id. Similarly, museums can provide a web-enhanced experience. To know more about the picture, article, or sculpture on display, the transceiver or sensor placed nearby can display a URL or direct information on the smartphones or PDAs of the visitors. • Smart industrial plants: Automation is the key to industrial development. The use of RFID tags in production units helps to monitor the progress of processing and to decide beforehand the actions required further. An ERP (enterprise resource planning) system of the plant can make decisions and notify the person in charge. This may also help to prevent any accident or breakdown.

5.7 Industrial • Supply chain management: A supply chain management process of any industry broadly includes the manufacturing unit, a distribution unit, inventory management, and customer-supplier. Much communication is involved among these units. Any miscommunication can cause delays and human errors. To avoid this and automate the entire chain, the products on display and the shelves on which they are kept can have smart labels and sensors, which keep track of the number of items left on the shelves. For example, suppose in the summer season, the sunscreen products are in high demand, the shelves on which they are kept notify the inventory as soon as the threshold or a minimum number of items is reached. Followed by this, the inventory either supplies or asks the distribution unit to send manufactured products urgently to the inventory. Similarly, in the rainy season, umbrellas and raincoats can be tracked. An instant notification system helps in enhancing the business (Pawara & Hiray, 2013). • Monitoring of perishable items/expiry date of items: Applications wherein the stocks such as medical items, tinned or packaged food items that have an expiry date or are perishable with time can be monitored by using smart labels to the products. These

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labels can notify the person in charge of changing the stock or perform the required actions. This can also be called smart retail. • Industry 4.0, also known as the fourth industrial revolution, is focused on achieving intelligent, efficient, and organized manufacturing processes. The idea of Industry 4.0 is integrating all the information related to communication and industrial technologies to build flexible and personalized production models with real-time interactions between products, people, and devices. It is mainly focused on building a cyber-physical system (CPS) to envision a smart factory that promotes the manufacturing processes that are more customized, digital, and efficient. The role of IoT in Industrial 4.0 is very essential. The IoT provides the way in realizing the intelligent identification, tracking, monitoring, and management with the help of RFID devices, various sensors, GPS, and laser scanners (Zhou, Liu, & Zhou, 2015).

5.8 Agriculture • Weather data analysis: The data is collected from different sensors and stored in every sensor location map with the help of RDF (Resource Descriptive Framework) datasets. For the analysis process, the system uses data on relative humidity, the temperature of the air, speed of wind, pressure, and visibility. The learning phase analyzes weather data in an effective manner in the clustering of data and detection of sensor failure; the unsupervised learning algorithm is used. Wind speed and humidity are the two factors that are used to detect abnormal data and faulty sensors (Ikram et al., 2019). • Weed control system (WCS): To control weed production in fields, various methods are proposed, which involves the discovery of the weed using techniques of image processing and texture-based weeds classification using the robotic system. The weed species identification is done with the help of various features. The design components of WCS consist of ATMEGA8 microcontroller, herbicide sprayer, camera, Raspberry Pi, Node MCU microcontroller, and sensors. The image which contains weeds is captured using a robot-fixed camera; this image is used as an input for image processing. This system is cost-effective, which can be accessed remotely to reduce weed growth by minimizing weed production (Ikram et al., 2019). • Cotton leaf detection system: The detection of leaf disease in cotton and control is a very demanding task for farmers. The proposed system monitors soil conditions by detecting and controlling leaf diseases. The system includes soil conditions monitoring sensors and Raspberry Pi. Soil conditions such as moisture, humidity, and temperature are detected by sensors that notify them about timely watering and spraying decisions on crops. This automated system gives greater accuracy for the detection of diseases of cotton leaves (Ikram et al., 2019).

5.9 Futuristic There are three futuristic applications discussed below (Atzori et al., 2010): • Enhanced gaming room: The gaming system can be designed where the level of energy and excitement of a player can be measured using sensors placed on the players as well as the game room. These sensors can sense heartbeat, blood pressure, voice, visual data,

6. Research challenges and issues in IoT

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location, acceleration of a user, temperature, humidity, and other noises. These determine the status of a player. He has to jump or crawl to reach all objects without touching the floor. A player’s controller can detect the objects to be reached in the room using his RFID tags. Some may be near while some far from the player. Voice controls from the game indicate whether his move was right. Based on a player’s performance, the game room automatically sets the difficulty levels to keep up the excitement level of the player. • Robot taxi: A taxi with or without a driver providing on time and a smooth service has been proposed. The machine-to-machine communication between such cars can prevent accidents, as they repel other devices magnetically. It works over real-time traffic, take the most optimized path by merely reading the destination from the passenger, help in traffic avoidance, and park them when not in use. It can be booked by pointing on a map to mark the source of origin of the trip. It can trace the passenger through GPS. At the base stations, their batteries can be recharged by employing actuators to use. • City information model: A beneficial approach to the planning and design of a city using a city information model (CIM) has been proposed. The municipality of a city keeps a record of various components that can be analyzed in real-time using IoT. Infrastructure like houses, buildings, offices, roads, and pedestrian paths and utilities like railway tracks, sewers lines, and public transport passages can be monitored, and related information can be provided to third parties. In order to perform any construction, the CIM is referred for approval. Any proposal which is incompatible with the CIM is rejected. This is an energy-efficient and cost-effective system which also reduces manual overhead.

6. Research challenges and issues in IoT In simple terms, it can be said that worldwide connectivity, global identification of objects, and the ability to send and receive data are all that are needed for an IoT system. However, the practical implementation of IoT encompasses several research challenges and issues that still need to be resolved. In this section, the major research challenges and issues for the implementation of IoT are explored.

6.1 Massive scaling or addressing Millions of devices are currently connected to the Internet. There will be millions of other devices that will connect to the Internet with the implementation of IoT applications. These objects will require a unique ID. Though the IPv4 identifies nodes with 32-bit addresses currently and with the 128 bit IPv6, it is possible to identify 1038 addresses, but still efficient addressing schemes are required as the number of nodes will become incredibly high in few years. The addressing of the RFID tags into the IPv6 is also required. Similar to the Domain Name Servers (DNS), Object Name Servers (ONS) will be required to associate objects with their respective related tags (Stankovic, 2014).

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

6.2 Creating knowledge, Big Data, and data mining A huge amount of raw data will be created by the IoT applications to be given as input to the system for processing. Big Data’s role in IoT is to process and store a large amount of data in real-time using various storage technologies. Big Data requires lightning-fast analysis with large queries for the IoT to gain quick insights from data in order to make quick decisions as unstructured data is collected from the Internet in IoT. It is, therefore, compelling to need Big Data in IoT. As there is enabling of on-demand as well as real-time action in IoT and Big Data, the deployment of these technologies is of great importance (Samuel, 2016). In this view, edge computing popularity is also becoming very high. Efficient data mining techniques will be required for correct decision-making power. The inferences that are drawn from the raw data should be trustworthy. These inferences can be accompanied by a confidence or trust parameter. Energy efficiency and congestion or overload control should also be maintained along with the processing of data. Taking correct decisions is an important parameter in an IoT application. For example, an application finding high room temperature at home may decide to turn on the air conditioner, while some other application finding no person at home may decide to turn it off. Such anomalies must be found and resolved correctly (Stankovic, 2014).

6.3 Interoperability IoT devices and protocols are very diverse. There are many use cases of IoT, likewise many communication protocols. Each device supports a common protocol for enabling communication and cooperation between them. The network should provide a mechanism for seamless interdevice interconnection, service description, discovery mechanism, including publishing, which should be interoperable (Ninikrishna et al., 2017).

6.4 Cloud computing In the network, which is the global and dynamic nature of infrastructure, the IoT methodology focuses on intercommunicating smart devices. It allows for omnipresent computing scenarios. Cloud-based IoT is a platform that enables the cost-effective use of applications, information, and infrastructure. Cloud-based IoT carries data to the cloud from the real world. One of the important unresolved issues is providing authorization policies while allowing only users who are authorized should have access to data, which is sensitive; in order to preserve user privacy and data integrity, this is very important (Alam et al., 2017). When multicloud approaches are adopted by end-users, the heterogeneity challenge can be exacerbated, and services will, therefore, rely on multiple providers in the improvement of the performance of an application. It takes a high bandwidth to transfer the enormous size of data from IoT devices to the cloud platform. Therefore, the important point here is that there should be required network performance to transfer data to a cloud platform, and this is due to the growth in broadband that does not keep pace with the evolution of storage and computation (Atlam et al., 2017). Therefore, generating efficiency of energy in the processing of data and transmission remains a crucial open issue. There are various issues in IoT, which are privacy, security, limited power, performance, reliability, and limited storage. In order to overcome these challenges, cloud integration in IoT is very useful.

6. Research challenges and issues in IoT

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6.5 Fog computing Fog computing is an extension of cloud computing, and inherits cloud computing concepts like multitenancy, resource orchestration, and elastic supply (Jianbing, Kuan, Xiaodong, & Xuemin, 2018). There is a significant trend in connecting ubiquitous devices that are smart to the Internet for Augmented Reality (AR), smart metering, smart transport, and wearable computing applications in IoT. Fog computing was introduced with the advance of IoT to bring service provision of distributing the storage, available computing, and networking resources at network edges. Fog nodes face threats to privacy and security. Fog computing cannot be considered safe as it still inherits different risks of security from cloud computing. If a user accesses fog node providing multiple services that are deployed at different locations, there is a possibility of revealing of the trajectory to the fog nodes (Jianbing et al., 2018). The fog computing decentralization, real-time service having low latency, and user mobility impede the realization of identity authentication.

6.6 Security A high level of heterogeneity in the system leads to higher risks of threats on the system. The large number of things to be used, important data stored on the systems and the fact that the processing and communication occur over a wireless channel, makes the IoT systems more susceptible to attack and thereby requires an even greater level of security as compared to the existing one. The existing security algorithms and methods are not enough for securing the entire IoT system (Roman, Najera, & Lopez, 2011). Therefore, these methods need to be smaller and faster. A smart, secure home-based blockchain framework can be analyzed based on security objectives of confidentiality, integrity, and availability (Ali et al., 2017). This method ensures access to data only for the true user-based on different important components of the smart home tier and different transactions and procedures of blockchain (Ali et al., 2017). A comprehensive survey of blockchain framework in IoT scenarios can be found in Ali et al. (2019).

6.7 Privacy IoT applications include the collection and storage of huge data, most of which can be personal data, financial data, confidential data, etc. The privacy of such data during processing and communication is very crucial. Proper policies must be enforced for security, transparency, and consistency of data. The temporary information collected should not be stored for long durations. Proper authentication and authorization mechanisms must be incorporated and implemented. As humans are involved in the loop, the trust factor among users is very important (Balte, Kashid, & Patil, 2015).

6.8 Standardization To implement the concept of IoT in reality, a uniform standardization body is required that sets standards that should be acceptable globally. Several efforts have been directed by various scientific communities but have not been integrated properly in a comprehensive manner until now. Contributions from many organizations such as the Auto-ID labs, ISO, ITU, IETF, EPC Global, ETSI, etc., are constantly made, but the universal acceptance of the final standard is still needed to be achieved (Atzori et al., 2010).

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1. The fundamentals of Internet of Things: architectures, enabling technologies, and applications

7. Summary IoT is the next step in the world of the Internet, which enables communication and establishes connections between anything at any time and any place. The ongoing research showed that IoT is one of the upcoming technologies of the world and is also the most promising. The importance of IoT and its impact on the living standard, which will lead to the betterment of the upcoming generation, is clearly explained. The primary enabling technologies that assist the practical implementation of IoT have been discussed. Current and future applications of IoT in various domains have been highlighted, and how these applications can be integrated to form more complex and beneficial applications has been mentioned. The pros and cons of the existing architectural models of IoT have been identified, and a generalized architecture of IoT is presented. Major research challenges and various issues involved in the deployment of IoT have been explained. There is still an enormous number of open research areas and studies in the field of IoT that need to be addressed. Some of the current research topics are data dissemination between nodes, data fusion, energy consumption, efficiency in terms of battery life, channel selection, broadcasting the data in an efficient manner such that redundant data is not broadcasted, data management, concurrent access, scalability, and mobility of the nodes. Successful and optimized researches can lead to a well-developed and functioning IoT system. In summary, IoT is one such marvel of science, which, if accomplished successfully, will change the future of humanity.

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C H A P T E R

2 IoT for healthcare industries: a tale of revolution Trina Dutta1, Subhadeep Pramanik2, Prateeti Kumar3 1

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Department of Chemistry, JIS College of Engineering, Kalyani, West Bengal, India; Analytics Lab, Marsh & McLennan Companies, Mumbai, India; 3Quantitative Trading Department, Edelweiss Financial Services, Mumbai, India

1. Introduction The Internet of Things or IoT is a scheme of interconnected machines which are capable of transmitting data without human intervention. The “Thing” mentioned in the term Internet of Things can vary from an automobile which has in-built sensors to warn driver about health of the car parts, an implant that can monitor heart conditions or any other devices that can transmit data. This data is then stored over cloud and using data analysis tools, Big Data and other technologies are used in various applications. In very near future, laboratories won’t be the only ones to witness medical miracles. More than that, it will definitely not be some fluke discoveries. As the IoT ecosystem makes way into our everyday life, new avenues for smart healthcare technologies have opened up. Healthcare services are undergoing a drastic shift with increased involvement of technology giants, data scientists, and sensor-enabled device manufacturer. Traditional glucometers, pedometers, calorie counters, and sphygmomanometers (blood pressure monitor) are being replaced by smartphones, wearables, and other medical devices. Soon these devices will find their way to a doctor’s toolkit. The ecosystem of connected devices will enable healthcare professional to tailor the treatments required for patients. IoT ecosystem will enable healthcare providers to predict, prevent, and cure, thus providing chance to increase longevity of life. IoT takes away some of the burden off the healthcare providers. Remote patient monitoring, treatment observation, and a lot other tasks are all possible to be handled with medical devices that are IoT enabled.

Healthcare Paradigms in the Internet of Things Ecosystem https://doi.org/10.1016/B978-0-12-819664-9.00002-8

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© 2021 Elsevier Inc. All rights reserved.

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2. IoT for healthcare industries: a tale of revolution

2. Component and mechanism of IoT Any IoT application involves various layers which make up a complete IoT ecosystem, starting from the components that created the physical device to data transmission and analysis. Fig. 2.1 depicts the layers of IoT application.

2.1 Sensor Basically, a sensor is equipment which can detect environmental changes. A sensor has no use unless it is attached to an electric system. Once that is done, it becomes the key component. Sensors are capable of measuring physical singularity, e.g., temperature, humidity, etc., and converting it into a digital signal. Base of a good sensor consists the below attributes. • It must be delicate to the singularity under measurement • It must not be affected by other environmental singularities • While converting to digital signal or gathering the environmental data, sensor should not alter it anyhow A vast number of sensors are offered with us nowadays to quantify almost every physical phenomenon around us. Knowingly or unknowingly we all have come across the following sensors at some point of Life: thermometer, light sensor, pressure sensor, motion sensors, gyroscope, gas sensors, and accelerometer. Fig. 2.2 shows different types of sensors available. Key points for a sensor are as follows: • Range: The upper limit and lower limit of the values of the parameter that the sensor is measuring. • Sensitivity: The minimum variance of the measurement that causes a distinct change in the output signal. • Resolution: The smallest alteration in the phenomenon that the sensor is able to sense.

Sensors and controllers

Gateway Device

Communicaon Network

Soware for analysis and translang data

FIGURE 2.1 IoT architecturedlayers of IoT

Healthcare services

2. Component and mechanism of IoT

FIGURE 2.2

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Different types of sensors.

2.2 Sensor classification Sensors can be clustered by numerous standards: • Passive/Active: Passive sensors are the ones which don’t require an external power source, while active sensors require an external power source to perform its job. • One type of cataloging is done based upon the technique used for measuring and sensing. Examples are electrical, chemical, etc. • Analog/Digital: Sensors which generate an analog signal, i.e., continuous signal, are called analog sensors. The sensors that transmit discrete signals are termed as digital signal. A huge variety of classifications are available to cluster sensors. We just discussed most basic ones here.

2.3 Uses of sensors in internet of things Introduction of prototyping boards and low price of sensors made it easy for us to use them in IoT engagements. In both feature-wise and specification-wise we can find multiple prototyping boards in the marketplace depending on project requirement. The most popular boards are Arduino Uno and Raspberry Pi 2.

2.4 Gateway Gateway devices work as a bridge linking IoT devices, systems, sensors, and tools. Gateways also link IoT systems with the cloud. These devices provide a way to process and store locally by linking the field and cloud. They also offer autonomous field device control based on sensor inputs.

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FIGURE 2.3

IoT gateway architecture.

2.5 How does an IoT gateway device work? Due to many reasons it often becomes impossible for the devices directly speak to the systems. A lot of the sensors and devices don’t have provision for protocols such as Wi-Fi or Bluetooth as those demands more energy consumption. Some devices need to perform aggregation on data as in raw form it is huge and invaluable. The IoT gateway performs a bucket of critical tasks like protocol translation, encryption, processing, data management, data filtration, etc. It basically sits between devices and sensors and systems for cloud communication. A pictorial representation to understand this well is given. Fig. 2.3 shows the architecture of IoT gateway.

2.6 Need for gateway devices in IoT 2.6.1 Reducing the crack between operational technology and information technology By system performance optimization, gateways narrow the gap between IT ops and IT infrastructure. This is achieved by ops, data collection, and real-time processing. Gateways can do several OT and IT optimizations. Below are few examples: • • • • •

Optimizing costs High scalability Telecommunications expenses Quicker production Reduce risks

2.6.2 Additional security level With growing number of devices and sensors, the number of communications happening over public and private network will also grow. The communications among the sensors,

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devices, cloud, and gateway must be secured so that no data gets tampered or gets to unauthorized hands. This part is usually handled with PKI (Public Key Infrastructure). This assigns every “thing” which is communicating, a unique identity, i.e., acryptographic pair of keys that enables message encryption. Gateway makes the implementation incredibly easy. 2.6.3 Real-time updates in the field Gateways offer more processing powers that enable us to process data real time and send updates to all devices/sensors. Sensors having very limited computational power, they are not capable of processing the data on their own in real time. Gateways also enable us to push firmware upgrades to devices/sensors with ease.

2.7 Communication network There are, namely, six wireless communication protocols as follows: • • • • • •

Satellite Wi-Fi Radio frequency (RF) RFID Bluetooth Near-field communication (NFC) We are going to discuss these in brief keeping IoT as our backdrop.

2.7.1 Satellite Satellite communications allow cell phones to communicate between a phone and subsequent antenna around 10 to 15 miles: Usually referred as GSM, GPRS, CDMA, 2G/GSM, 3G, 4G/LTE, EDGE, and others based on connection speed. In the world of IoT, this type of communiqué is familiarized as “M2M” (machine-to-machine) as it enables machines, e.g., phone, to send and receive information through the cell network. 2.7.2 Satellite communicationdadvantages and disadvantages Pros: • The connection is very stable • It is universally compatible Cons: • It does not offer direct message transfer from a phone to a piece of equipment (must be done via satellite) • Maintenance and operation costs are very high • Power consumption is very high Example: Utility meters send information to a remote server, digital advertisements, Internet connected cars, etc.

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2.7.3 Wi-Fi This is a wireless local area network (WLAN) which practices the IEEE 802.11 standard via 2.4 GHz UHF and 5 GHz ISM frequencies. This allows devices to access Internet within 70 ft range. 2.7.4 Advantages and disadvantages Advantages: • It is universally compatible with smartphones • It is very affordable • The control and security are robust on it Disadvantages: • The power usage on this is relatively high • The network created is quite instable and inconsistent Example: We all use Wi-Fi networks to access Internet. 2.7.5 Radio frequency These are most probably the easiest communication mode amid devices. Protocols such as ZigBee or Z-Wave practice a low-power RF radio retrofitted or hooked on systems and devices. Z-Wave ranges approximately till 100 ft (30 m). Every RF band is mapped to its country, e.g.: Europed868.42 MHz SRD Band USd908.42 MHz band Israeld916 MHz Hong Kongd919.82 MHz Australia/New Zealandd921.42 MHz Indiad865.2 MHz ZigBee practices IEEE 802.15.4 standard. It is power efficient but range is limited between 10 and 100 m. 2.7.6 Advantages and disadvantages Advantages: • It consumes less power • It is a simple technology • It is not dependent on latest phone functionality Disadvantages: • Smartphones do not support this technology • Central hub is necessary for getting Internet to RF devices

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Example: Typical television remote uses RF, which allows it to change channels remotely. Other examples are traffic management systems, electrical meters with in-home displays, wireless light switches, etc. 2.7.7 Radio frequency identification Radio frequency identification vastly referred as RFID is the wireless use of electromagnetic fields to recognize objects. Generally, one would install an active reader or reading tags that contain a stored information mostly authentication replies. This is referred as Active Reader Passive Tag (ARPT) system by experts. RFID range can be between 10 cm and 200m. Active Reader Active Tag (ARAT) system utilizes active tags triggered with an interrogator signal from the active reader. Bands RFID runs on: • • • • •

120e150 kHz (10 cm) 3.56 MHz (10 cm-1m) 433 MHz (1e100m) 865e868 MHz (Europe) 902e928 MHz (North America) (1e12m).

2.7.8 Advantages and disadvantages Advantages: • It doesn’t need power • This has better presence and broadly used in IoT Disadvantages: • • • •

This is heavily insecure Cost per card is ongoing Tags are mandatory for using this This has compatibility issues with smartphones

Examples: This is used in factory data collection, animal identification, road tolls, building access, etc. This is many times attached to inventory also to track them and prevent theft. 2.7.9 Bluetooth This is a standard for wireless technology for swapping information over small distances (via short-wavelength UHF radio waves between ISM band 2.4 and 2.485 GHz). The frequencies are as same as Wi-Fi which makes these two technologies appear very alike. Nevertheless, they have diverse uses. There are, namely, three different styles of Bluetooth technologies, i.e.: • Bluetooth: It is relatively older technology. This technology is relatively high battery draining. The security threat is more and very complicated to pair. These are not much seen nowadays.

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• BLE (Bluetooth 4.0, Bluetooth Low Energy): This was introduced by Nokia and currently used by all major OS, e.g., Windows, iOS, Blackberry, Android, Windows Phone, Linux, etc. This technology is fast and energy efficient. • iBeacon: This is the trademark for a simplified Bluetooth-based communication technique majorly used by Apple. Essentially it is a Bluetooth 4.0 sender which transmits an ID named UUID, which can be recognized by iPhone. This offers simplified implementation for vendors. Furthermore, this is easier to use for nontechnically trained consumers. While being dissimilar technically, on abstract level, this is comparable to NFC. Pros: • • • •

It is very energy efficient It is very low in cost The upgrade is very easy The signal doesn’t get interfered by obstacles Cons:

• It loses connection under certain scenarios • Bandwidth is low when compared to Wi-Fi • It does not offer long-range communications Example: Bluetooth can be seen in many devices ranging from mobiles, laptops, music systems, etc. 2.7.10 Near-field communication

The base technology for this is electromagnetic induction between two-loop antennas positioned within each other’s near field, successfully creating an air-core transformer. It functions inside the universally accessible and unlicensed RF ISM band of 13.56 MHz on ISO/ IEC 18000-3 air interface and at rates ranging between 106kbps and 424 kbps. It comprises an initiator and a target. Initiator actively produces an RF field which can power a passive target, i.e., an unpowered chip a.k.a. “tag”. This allows NFC targets to take very simple form factors, e.g., batteryless cards, stickers, tags, key fobs, etc. Peer-to-peer communication is possible here in case both are powered devices. 2.7.11 Modes of NFC

• Passive communication mode: Initiator delivers a carrier field and target responds by modulating the present field. In this case, target may draw its power from the initiatorgenerated electromagnetic field. This makes the target a transponder. • Active communication mode: In this case, initiator and target both interconnect by producing their own fields alternately. While waiting for data, the device disengages its RF field. Usually both devices have power supplies here. 2.7.12 Advantages and disadvantages

Advantages: • Setup is extremely simple; it offers a low-speed connection with extremely simple setup • It can bootstrap more efficient wireless connections

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• It supports encryption of NFC • There are less private RFID systems Disadvantages: • It has a very short range Example: It is available on new Android phones and at Apple Pay on new iPhones as payment authentication technology.

2.8 Data analytics IoT and data analytics are two counter parts of each other. With data exponentially growing over time, the importance of data analytics is also growing directly. As per the trends, there should be as much as 31 billion IoT connected devices by the year of 2020. Data analytics will play the key part in understanding the data generated from all these devices. Data analytics is the only thing that can harness the value from the data generated from IoT devices. It is the process of converting data sets to extract articulate assumptions and actionable insights. These judgments are mostly in the form of patterns and trends, and these help business organizations to act proactively. 2.8.1 Data analytics and IoT combineddbusiness impact IoT project’s success and growth are heavily dependent on data analytics. Tools with analytical capabilities are driving the way businesses make their decisions. The different ways by which these analytical tools are allowing business to make effective decision are listed below. • Volume: Sensor-embedded IoT devices generate an enormous amount of data which is stored in huge clusters. These huge volume of data needs to be maintained in order to analyze and extract relevant insights from them. Different data analytical software are already available in the market to analyze and extract information from the huge volume of data within a short time frame. These software not only maintain historical data, analytical tools can also draw insight from real-time data. • Structure: The data gathered for analysis in healthcare services can vary a big time in terms of the structure of the data. These data can fall under structured, semi-structured, and unstructured category. Data can vary in terms of data types and formats. Analytical tools will permit the business to analyze these different sets of data to draw unified conclusions to the problem at hand. Fig. 2.4 shows different types of devices communicating and transferring data. • Revenue driving: As done in other industries, data analytics can be used to understand customer’s choices and preferences. These are done typically by analyzing historic data related to an individual and by extracting patterns from that data. These insights can be used to develop newer services and offers that will attract the target customers. Thus, in turn customers will be more inclined to the services provided in that specific facility and the facility will book a better revenue.

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FIGURE 2.4 IoT connected devices.

• Competitive edge: IoT is the current revolutionary technology in today’s world. A lot of developers and service providers in the market are experimenting with IoT. Businesses can gain a significant edge in the market with the use of data analytics with IoT. Various data analytics techniques are available and may be applied in IoT investments to gain a competitive edge. Below we discuss some of the data analytics techniques. Fig. 2.5 describes data analysis and IoT architecture. • Streaming analytics: Streaming analytics is the process of analyzing real-time data streams to detect urgent situations. This type of analysis is sometimes called stream processing and utilizes in motion datasets. Traffic analysis, air fleet tracking, and fraudulent financial transactions tracking are some of the well-known applications that use this technique. • Spatial analytics: Analyzing geographic patterns to draw insights into object locations is referred to a spatial analytics. Smart parking system and other location-based IoT applications can benefit from this technique. • Prescriptive analysis: The combination of descriptive and predictive analysis is referred to as prescriptive analysis. Commercial IoT applications use this technique. This form is best applicable to understand steps of actions to be taken in a particular situation. • Time series analysis: This is the data analytics method that is used to analyze timebased data to extract trends and patterns. Weather forecasting and health monitoring are few of the applications that use this form of analysis. With the rapid advancement in technology, new areas and applications are emerging where data analytics can be applied along with IoT. It has been observed that IoT investments have significantly benefitted from the use of data analytics. Data analysis on product usage

3. IoT in healthcare

FIGURE 2.5

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IoT with data analytics.

can be used to carry out actionable marketing. Through the use of video sensors coupled with data analysis safety and surveillance abilities can be increased. Healthcare is one of the major sectors and utilizing data analytics in this sector can provide huge boon to the healthcare industry. Data analytics has huge potential for lowering healthcare costs, remote healthcare services, better diagnosis, proactive treatment, etc.

3. IoT in healthcare 3.1 IoTdthe new resident doctor Disease management platforms and remote monitoring will assist better care coordination across the whole healthcare system. In collaboration with Michael J. Fox Foundation, Intel is working to improve remote care facility for Parkinson’s disease. A platform is being developed by them which will collect data through wearables and observe symptoms that would else go unnoticed. This type of integrated, connected platforms will facilitate biopharmaceuticals to disrupt the traditional clinical research model with more adaptive and simplified design and will result in the implementation of patient-centric healthcare system. IoTization can reduce the need to move patients with chronic illness or aging patients to care homes. Predictive models and insights gathered from smart healthcare services will enable patients confined to bed to monitor their health conditions by monitoring various parameters. IoTization can enable real-time patient access to healthcare professionals, thus providing solutions for two of the biggest problems of today’s healthcare systemdtime and accessibility.

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Although healthcare industry was slow to accept IoT than other industries, the IoT is transforming the way we keep people safe and healthy these days.

3.2 History of IoT in healthcare For the last 10 years or more, patients are being introduced to IoT devices in different forms. For some patients, following up for different parameters, e.g., temperature monitors, fetal monitors, ECG, blood glucose monitor, etc., is very important but it needs to be done by medical professionals. Introduction of IoT devices has changed this scenario up to a great extent by reducing the need of direct patientephysician communication. IoT has opened the gateway for real-time and more valuable data for medical professionals. Primitive IoT applications in healthcare had smart beds which could detect whether vacant or not or if a patient was trying to get up, without manual intervention. Another application was home medication dispenser which could notify when medication wasn’t taken or any other alerts and could upload the data to cloud for proper actions.

3.3 Challenges in current healthcare system The problem areas in healthcare system where IoT can have the biggest impact are the aging population, increased number of patients with a chronic disease (e.g., diabetes, asthma, etc.), and the general inefficiency of the healthcare systems shown in Fig. 2.6.

Aging population Increasing

Increased wait time

chronic diseases Problems in healthcare

Rising medical cost

Adherence monitoring Shortage of healthcare workforce

FIGURE 2.6 Challenges of current healthcare system.

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• Aging population: IoMT or Internet of Medical Things could be a very powerful tool for an aging population. Studies suggest, by 2025, 1.2 billion of the 8 billion people on the earth will be elderly. Elderly people are likely to have more health-related issues. As a result the cost of healthcare will increase as our population continues to age. Elderly people require more routine checkups and more monitored environment (Traditional healthcare challenges and how IoT can change). • Chronic diseases: Patients with chronic diseases require frequent visits to the doctor’s office to monitor their condition. By the use of IoT the need for face-to-face visit can be reduced which in turn will reduce the medical costs. IoT devices can collect and send patients’ health information and vital data to the doctor through network and the doctor can use those information to provide remote healthcare support. • Failing to adhere to doctor’s orders: In the traditional healthcare system, doctors have no way to track if the patient is following his or her orders or not. Many a times patients don’t adhere to the medication or lifestyle prescribed by the doctor once they leave the healthcare facility. This lack of cooperation increases the risk of falling ill again as the disease cannot be fully cured (Traditional healthcare challenges and how IoT can change,; Sokol, McGuigan, Verbrugge, & Epstein, 2005). • Lack of healthcare personnel: The rate at which requirements are increasing for healthcare services demands increased number of staff to provide services. Workforce includes doctors, nurse, nutritionists, caretakers, assistants, etc. • Rising medical cost: Increase in chronic diseases and increased aging population rise the expenditure for medical services. One of the key problem areas in healthcare services today is the increased cost (Traditional healthcare challenges and how IoT can change). • Increased wait time: Minor checkups at the clinic have become as tedious as a trip to the emergency room. Apart from the medical expenses, emergency room waiting times are sometimes too long (beckershospitalreview).

3.4 IoT healthcare services and applications IoT is slowly finding its way into our lives through the wide range of applications that are currently present and being worked on. Fig. 2.7 shows the wide range of spectrum where IoT can be used. • Mobile Health/mHealth: Mobile health or mHealth is a term used to describe the use of wireless devices such as mobile phones to provide medical care. mHealth applications enable caregivers to guide patients from a remote location. mHealth allows caregivers to communicate with patients through secure messaging system. Patients can get diagnosis and treatment without the need of meeting face to face with healthcare providers (Pirouzan Group). • IoT for ambient assisted living: The incapacitated and aging population can benefit a lot from the use of IoT technologies. IoT-enabled devices can help monitor vitals (e.g., heart rate, diabetes) and perform real-time monitoring using location-aware devices. Here IoT can play a huge role. IoT devices can monitor glucose and track vitals. These devices can also track activities and sleep patterns of the patient. One major problem with senior patients is that they often not remember to take their medicines at the right

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FIGURE 2.7

Healthcare applications in IoT.

time. IoT devices can help remind people to take medicines on time and note down exact timings of medication which will later help healthcare providers diagnose the patient. Portable IoT devices which can perform routine health checks like blood and urine test, blood pressure levels, etc., are a boon to loved ones of the patients who are not able to stay and supervise the patient. These devices can perform routine checks and send reports to the doctor as well as any family member or caregiver. In this way family members can remotely monitor the health of their elderly living away from them. Fig. 2.8 shows the process of remote monitoring. These devices can also send warnings in case a memory care patient breaches their boundary (Dohr, Modre-Opsrian, Drobics, Hayn, & Schreier, 2010; Istepanian, Hu, Philip, & Sungoor, 2011; Suryadevara, Kelly, & Mukhopadhyay, 2014). • IoT in medication: Not only treatment and diagnosis, IoT also enables users to maintain adherence to medication. It is often noticed that patients forget to take medications on time and doctors do not receive that information correctly. Studies suggest smart pill bottle technologies in combination with wearable audio sensors and classification techniques are competent to assess adherence to medication with high accuracy (Kalantarian, Motamed, Alshurafa, & Sarrafzadeh, 2016). IoT devices can prevent adverse drug reaction (ADR). NFC-enabled smart pill bottle, knowledge-based system, and electronic health record can considerably prevent the undesirable consequences of wrong medications (Jara et al., 2010; Kalantarian et al., 2016). Fig. 2.9 shows how digital medication monitoring can help track adherence to medication and enable doctors to design a better treatment for the patient.

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FIGURE 2.8 Remote patient monitoring with IoT.

FIGURE 2.9 Adherence monitoring with smart medicine.

• IoT for specially abled people: According to a survey conducted by WHO, it shows that almost a billion people live with some or the other kind of disability. Lives of these disabled individuals can significantly be improved by the use of smart gloves, smart watch, and other IoT-aware devices. Smart gloves are devices consisting of low-cost inertia sensors that help people with hearing loss to communicate with individuals not having knowledge of sign language (Kumar, Verma, & Prasad, 2012; Li et al., 2010; Sarji, 2008). Patients with speech disorder can use smart watches to improve their speech functions (Dubey, Goldberg, & Makodiya, 2015; Dubey, Goldberg, & Abtahi, 2015). Education can be made more accessible to special needs students with the help of IoT. IoT technologies like Nano Retina Eyeglasses are able to fine-tune the visuals with the

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2. IoT for healthcare industries: a tale of revolution

FIGURE 2.10 Different types of medical implants.

help of retina implants. These IoT devices are already in the market (The Wireless Nano Retina Eyeglasses). Technology can help us gather data from patients about their special needs from a remote location and help fulfill those needs. • IoT-enabled medical implants: Apart from wearables, IoT is also helping us with a lot of medical devices that are implantable. These are highly sophisticated devices and they are mostly available in miniaturized, dependable form inserted within the body to improve the individual’s body functions. Fig. 2.10 shows different types of medical implants. Some examples of these implants are: • Pacemakers: Pacemakers help regulating the heart rate by stimulating the heart muscle (Barold, Stroobandt, & Sinnaeve, 2010). • Deep brain stimulation: Essential tremor patients and Parkinson’s disease patients can highly benefit from these systems. Deep brain simulators or DBS is a type of brain pacemaker. These devices provide electrical impulses into deep brain regions so that movements are decreased (Lee & Kondziolka, 2005). • Cochlear implants: Electronic implants that are placed inside ear to improve hearing abilities. These implants consist of circuits, microcontroller, and batteries for power management. IoT is continuously being researched to make medical implants more power efficient and secure. • Gastric electrical stimulation (GES): Electrical implant inserted into abdomen to treat gastroparesis patients. Gastroparesis disturbs the nervous system and stomach muscles. This delays the procedure of emptying food from the stomach into the small

3. IoT in healthcare

•

•

•

•

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intestine. Some of the conditions that cause gastroparesis are diabetes and nervous system disorders. GES controls smooth muscles of the lower stomach by sending mild electrical signal to the nerves. This process helps reduce nausea and vomiting tendency in patients. GES is only used to treat patients whose condition cannot be treated with normal medication. • Implanted insulin pens: Insulin pumps are a type of medical implant that can inject insulin into the peritoneal cavity. Peritoneal cavity has rich blood vessel supply, so it can efficiently absorb insulin. Although these insulin pump users said they observed an improved health condition, it has not gained a lot acceptance. The reason for this is that users have to travel a long distance to get their pumps refilled several time a year. Data analysis and research to improve healthcare: By using Big Data analytics, the vast amount of medical data that is stored from various IoT devices are processed and deep insights are being drawn upon that data. Data for DNA sequences are being used to analyze different types of genes and their association to a disease. These researches can help find cure for currently incurable diseases. The different data sources are: 1. Clinical data 2. DNA sequences 3. Drug development 4. Sensor data 5. Social data Tracking staff, patients, and inventory: Hospitals need to track their patients, equipment, staff member, etc., throughout the building. Without the ability to track, hospitals will not be able to maintain maximum security for their staff and patients. The activity of tracking is quite easy for small institutions, but becomes a huge problem when dealing with multiple facilities, housing large number of patients, staff, and thousands of equipment. Many hospitals are looking into IoT and real-time location trackers to help their asset management. With the help of IoT devices, the day-to-day monitoring of hospital activities is inexpensive, effective and also provides the hospital with technological boost. Enhanced drug management: One of the magical inventions of modern technology in the healthcare domain is the new form of prescription medication. Along with regular medication doctors can now prescribe sensor-enabled medicines. These pills contain microscopic sensors that can transmit signal to external device, a kind of patch attached to patient’s body. These pills are used to ensure proper usage and dosage of prescribed medication. The data sent by these sensor-based pills are invaluable when it comes to reevaluating patient’s diagnosis, tracking usage of medication, and reminding the patient to take medicines (siliconangle). Reducing the wait time in emergency room: Emergency room wait time can take hours to complete. Thanks to IoT, now we have solutions to manage emergency room care more effectively. One hospital in New York in partnership with GE Healthcare is using new IoT-based software, Auto Bed. Auto Bed tracks occupancy among 1200 units and factor 15 different metrics to measure the needs of patients. This way system can effectively assign emergency care beds to patients who are in need of inpatient care (beckershospitalreview).

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• Smart beds: Smart beds started making their way into hospitals a long time back but it is only recently they have started gaining popularity in the market. These smart beds are acquiring detailed information about patients’ positions and vitals. Smart beds also help nurses to detect and prevent possible issues related to bedsores (internetofthingsagenda). • Connected contact lenses: Patients with diabetes have the chance to develop glaucoma, a condition where pressure increases within the eyeball and eventually results in loss of sight. Medical smart contact lenses are a concept where a patient’s eye glucose level can be tracked and early warning can be provided to avoid said condition. In 2014, Google Life Sciences (a subsidiary of Alphabet) announced the project of developing a smart contact lens that would assess glucose level from tear and provide an early notice for diabetics to alert them when glucose level goes above or below specified limits. The project was being developed in partnership with Alcon, pharmaceutical division of Novartis. The project attracted a lot of skepticism regarding the method of tracking glucose from tears, which were eventually proven right. So the project was shelved. However, Verily believes there are other uses for smart lenses and they are continuing to work on smart lens projects (Verily smart lens program). In 2018, Swiss company Sensimed got approved for marketing and sale of a smart contact lens, which tracks changes in eye dimensions which can lead to glaucoma. This was first invented in 2010 and received CE mark and FDA approval in 2018 (econsultancy; Sensimed smart contact lenses). • The Apple Watch app to monitor depression: IoT not only is helping in physical health of individuals but also has potential applications in cognitive health of a person. Cognition Kit Limited, a cognitive health measurement platform in collaboration with Takeda Pharmaceuticals U.S.A, in 2017 started exploring the utilization of Apple Watch to monitor and analyze patients suffering from MDD. In November 2017, the outcome of this study was presented at CNS Summit (prnewswire). According to this study, app’s daily assessment results to match up with the detailed reports created through cognition tests and patients’ reports. It was observed that there was a huge level of compliance in using from the patients’ side. Although the research was only an investigative pilot, this shows us the possibility for IoT wearable devices to be a solution to help patients with depression (econsultancy). • Coagulation testing: A first of its kind device created for anticoagulated patients is a Bluetooth-enabled device which enables patients to keep a track of how quickly their blood clots. This was launched by Roche in 2016. This device has the facility to send results to healthcare provider, add reminders to test, etc., which reduces the number of visits to the doctor’s office and lowers the risk of stroke or bleeding (econsultancy; coaguchek). • Connected inhalers: One more chronic disease apart from diabetes which affects a huge number of people worldwide is asthma. IoT has found its way in helping asthma patients in the form of connected inhalers. Currently the largest manufacturer of connected inhaler is Propeller Health, a digital therapeutics company. Smart inhalers are designed to help patients with chronic obstructive pulmonary disease, which includes emphysema and chronic bronchitis and asthma. These smart inhalers are attached with sensors and connect to an app which provides allergen forecasts, track usage of emergency medicines, etc. One major benefit of using smart inhalers is increased adherence to the care

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process designed by the doctor. The sensor records and reports use of the inhaler and that report is sent to the doctor. This provides the doctor with better clarity on a patient’s adherence to the process and provides patients with the clarity on whether the process is working for them (Propeller health connected inhalers). • Cancer treatment: A randomized clinical trial of 357 patients receives head and neck cancer treatment. This treatment used a Bluetooth-enabled weighing scale and blood pressure cuff. These devices coupled with symptom tracking app sent updates to patient’s doctors on symptoms and responses to the treatment on a daily basis. The patients using this monitoring treatment known as CYCORE reported less severe symptoms to the cancer compared to a different group of patient who had daily visits with the doctor without any additional monitoring system. The president of ASCO, Bruce E. Johnson, said that the smart technology “helped simplify care for both patients and their care providers by enabling emerging side effects to be identified and addressed quickly and efficiently to ease the burden of treatment.” Study showed the likely benefits of IoT in monitoring a patient and improving the patient’s contact with the doctor in such a way that it does not hinder the patient’s daily life. • Automated insulin delivery (closed-loop system): There is an open source project called OpenAPS which can be called the most interesting discovery of this time. Open Artificial Pancreas System, in short OpenAPS, is a closed-loop insulin delivery system. This is different from a continuous glucose monitoring device, as along with measuring the glucose level in the bloodstream, this device helps deliver insulin into a patient’s body. This is why this device is called closed-loop. Dana Lewis and husband Scott Leibrand started OpenAPS in 2015. Using a Raspberry Pi computer and CGM OpenAPS software can complete the loop and continuously alter the insulin amount into a patient’s body. Automating insulin delivery can significantly change the lives of diabetics. Keeping patient’s blood glucose level at a safe level becomes much easier job when if an automated device can monitor and adjust the insulin level into patient’s body. This prevents the blood sugar level from going too high and low (known as hyperglycemia and hypoglycemia). OpenAPS is not a solution that people can use just out of the box. This requires people to build their own system. Despite that it is gaining popularity in the diabetic community. According to OpenAPS website as of January 15, 2018, there are more than 1078þ individuals around the world.

3.5 IoMT industrial market The industries for IoT devices for medical applications are rapidly increasing which is generating new ideas and discussions of new novel application areas. The continuous increase in IoT enables research to take new directions and enhances business opportunities to reduce cost, create better technology-enabled products, etc. New devices and products are being developed and moved to the market by research labs, startups, and companies (International Journal of Machine Learning and Networked Collaborative Engineering, ISSN: 2581-3242138). According to Allied Market Report, the IoMT will get to $136 billion by 2021 (alliedmarketresearch; marketsandmarkets).

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Currently IoMT is majorly focused on wearable and smart technology. Devices equipped with RFID and NFC have flooded the healthcare market. Sponsorship from government, decreasing sensor costs, and high-speed networks are some of the important factors that are giving a boost to the rise of IoT industry. Below we discuss about some of the companies which are using disruptive technologies to bring innovative ways for a healthier better lifestyle (Companies supporting healthcare IoT market). • Orbita: Orbita created an app called Orbita Voice which creates and integrates with smart home devices such as Google Home/Amazon Echo. Orbita Voice gathers data from these smart home devices and sends collected data to care providers. Orbita is helping make the lives of patients with chronic diseases easier (Orbita voice app). • Naya Health: Smart breastfeeding pumps designed by Naya Health operate using hydraulic suction rather than electric pumps. These are cheaper and quieter than hospitalgrade electric breast pumps. These pumps can be connected with smart pump app that displays milk volume, and other pumping session data. • Breathometer: Breathometer, a company specializing in breathe analysis technology, has developed IoT-enabled breath analyzer named Mint (Breathe analyzers). This breathe analyzer gives users a better understanding of their oral health by analyzing bacteria and breathe contents. Mint embedded with an app gives users instant feedback. This can recognize damaging microorganisms in the user’s oral cavity and advise the user on how to maintain the health of oral cavity. • Carré Technologies: Carré Technologies has developed Hexoskin, a line of IoT-enabled clothing to monitor heart rate, sleep cycles, and breathing style (Wearable smart clothings). Hexoskin can be adapted at both warm and cold environments. Carré Technologies is a technology company specializing in wearable technologies that connects to the network and collects data points more than 40,000 per minute. • TruInject: TruInject is an injection training system with IoT connectivity that helps train healthcare providers to sharpen their skills in using injectable medicines. TruInject comes with a lifelike model and associated software for the training process. This method of training in injectable medication helps decrease patient discomfort (TrueInject). • Keriton: Keritonkare Nurse, an app designed by Keriton, helps reduce the workload of NICU nurses. Keriton’s app can save more than 10000 work hours per year. Keritonkare nurse monitors feeding cycles, feeding inventory with the use of IoT technology (Keriton). • Meru Health: Meru Health uses IoT technology to improve mental healthcare services worldwide. Their mission is to make mental healthcare accessible to every individual. Meru developed a digital program called Ascend, which helps patients lead a better quality of life through daily support (MeruHealth). Meru Health is designing costefficient, accessible digital programs for people who undergo depression. • LifeFuels: This is a company creating smart bottles that offer users with information about their hydration level on a day-to-day basis. They monitor the user’s hydration through the use of smart bottles and based on that data recommends users on how they should continue with their hydration. LifeFuels also offers a straightforward but tasty

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set of flavored beverage and nutrient concentrates to help recover user’s general health (Lifefuels smart bottles). • Backbone Labs: Poor body posture can result into sever back pain and many other problems. So Backbone Labs with the help of chiropractors produced a cost-effective solution for posture correction. Backbone Labs is helping everyday people improve body posture of with wearable tech solutions. They use and harness with IoT capabilities along with an app and vibrating motors. When user wears this harnesses while sitting it will vibrate and alert the user in case user’s posture is incorrect.

3.6 IoT healthcare marketdtarget audience • • • •

Healthcare facilities, such as hospitals, clinics, and surgical centers Technology service personnel Organizations and individuals providing IoT services Healthcare solution vendors who work with IoT-enabled solutions

The growing need for IoT opens up new business opportunities for service providers in different fields. The need for smart platforms enabled with technology is redefining the healthcare business model. These changes come with the increased requirement for automation, more collaboration, and technologies with better prediction abilities. Facility providers have massive opening to drive this technical move. Key use cases for this are as follows: • Optimization of the supply chain: Cardinal Health, a healthcare services company, conducted a survey in 2015, which estimated that about $5 billion medical waste is generated due to improper supply chain tracking and ineffective inventory management. Hospitals are trying to better their supply chain and inventory management. Thus, it has become an exciting opportunity for healthcare supply chain management (healthcareitnews). • Reduced machine downtime: According to studies, there is huge downtime expenditure for all healthcare-related outages. The adoption of IoT comes with direct increase in business opportunities. For instance, companies are working on collecting data from existing devices to analyze anomalies and defects. This research will in turn help make better devices in future. Better devices result in increased equipment sales. Modern-day sensors are capable of eliminating defects during production (forbesindia).

4. Reducing healthcare costs with IoT According to estimates represented by Goldman Sachs, annual healthcare costs can be reduced with the help of IoT. There can be potential $300 billion savings. Some of the areas where expenditure can be reduced by using technology are described below: • In traditional system, a huge cost is involved in managing and tracking hospital equipment, patients, and staff. Smart tracking technologies can help track equipment, staff, patients. This will improve the overall workflow which results in a better healthcare environment.

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• Real-time patient monitoring systems help in reduced visits to the doctor’s office. This also assures adherence to treatment which reduces the chances of repeating the whole treatment due to lack of adherence (internetofthingsagenda). So extra costs due to wrong treatment and wrong medication can be reduced.

5. Challenges of using IoT in the healthcare segment While IoMT-enabled healthcare services come with promise of a smooth patient-centric healthcare system with reduced costs, IoT-based healthcare has its own set of challenges. As connected everythingdsmart cars, smart hospitals, and smart devicesdincreases so grows the complexity to implement IoT-based smart ecosystems. Many instances are found where companies implementing smart systems failed to understand the complexities involved in the overall process. Following are some of the challenges to be overcome for a successful IoT-based healthcare model. A Cisco survey conducted in 2017 revealed that only 26% of the companies that are trying to create IoT-based services have become successful in their innovations (gatewaytechnolabs). Some of the reasons for these failures include improper integration of application and lack of expertise. The development of IoT involves understanding and knowledge of embedded systems, app development, analytics, Big Data, cloud, sensors, and a lot more. Here we will discuss some of the obstacles faced by IoT. • Tremendous amount of data: As the number of IoT devices increases the amount of data generated by these devices is also increasing. As suggested by some forecast by 2025, healthcare will generate the most data of any other sector. With the decrease in sensor cost it has become easier and cheaper to collect data from sensors attached to human body within a short amount of time. IoT ecosystem has to handle this humongous amount of data which will be of different variety, velocity, and volume (Ghazal et al., 2013; Hashem et al., 2015; Raghupathi & Raghupathi, 2014). Companies who are planning to use IoT devices should be prepared for an increase in data storage needs. • Regulatory and standardization challenges: The applications around IoT are still being explored so regulations and standardization are still not at a concrete stage. IoT touches upon a wide range of disciplines which are managed and maintained by a varied range of regulatory body. Due to this the standardization becomes much more difficult. This difficulty only increases when healthcare sector is considered in conjunction with IoT. There is a strict medical standard defined by regulatory bodies which needs to be followed for any and all equipment and devices. For example, in the United States any wireless medical device requires approval from three different regulatory agencies (Mahn, 2013). (i) Centers for Medicare & Medicaid Services (ii) Federal Communications commission (iii) Food and Drug Administration (FDA) • Security and privacy: Most significant challenge with IoT app development is security and privacy. By security not only means network security, it means security of all the components that make up IoT application. Any IoT application is typically connected over a network and involves a huge data traveling through various gateways. The

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humongous amount of data traveling through the network is vulnerable to hacking and other form of cyberattacks. Following are some key factors of IoT security: 1. Data exchange security: IoT devices transmit a vast quantity of information. This information is then transferred from sensors and other equipment to a platform or gateway and persisted on the cloud. So it is extremely important to secure all the network transfer by using data encryption protocols. 2. Physical security: IoT physical devices usually do not require much attention so they are often left unattended. That makes these devices easy to be tampered by the hackers. Thus, while development some security measures need to be added to the devices to make them tamper proof. 3. Cloud storage security: All the data accumulated from a variety of equipment and sensors are stored in the cloud. Although it is said that cloud storage is secure, it is the task of cloud developers to keep the cloud platform secure with proper encryptions and access control. 4. Privacy updates: The data collection from user devices needs to happen in a regulated way so that user’s privacy can be maintained. All fitness trackers collect user information based on guidelines specified by HIPAA. All IoT devices have to adhere to regulations. • Lack of skill set: All the challenges mentioned above can only be handled with the help of proper skilled resources. The diverse field of IoT requires resources that are well aware of software and hardware implementations. The right talent will help overcome the major challenges and will be an important factor in the growth of IoT. • Outdated infrastructure: Old infrastructure is a huge problem when it comes to IoT. The concepts and technology used to make IoT a success are mostly built on top of new infrastructure. So hospitals with outdated infrastructures are failing to adapt the new IoT-based healthcare model. Sponsorship and funding can help these hospitals achieve the base infrastructure required to put in place an IoT-enabled healthcare system. While there are challenges that make IoT application development complex it is important that different stakeholders in the field understand the complexities, adapt and adopt technology advancements to drive IoT development.

6. Conclusion IoT has changed the healthcare ecosystem and it is still changing. Despite all the challenges IoT is gaining immense popularity in the healthcare segment. Patients have begun to embrace the utilization of IoMT devices to manage their health. More and more organizations are diving into providing services in the IoT-based healthcare domain. Doctors have started to use connected devices to improve treatment outcomes to give patients a better healthcare experience. Device creators, technology professionals, healthcare service providers, and researchers are all coming together to improve the quality and longevity of human life.

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References https://www.alliedmarketresearch.com/press-release/internet-of-things-iot-healthcare-market.html. http://www.alleantia.com/en/iot-gateway/. Barold, S. S., Stroobandt, R. X., & Sinnaeve, A. F. (2010). Cardiac Pacemakers and resynchronization step by step: An illustrated guide. John Wiley & Sons. https://www.beckershospitalreview.com/healthcare-information-technology/how-bed-tracking-technologyallowed-mt-sinai-medical-center-to-reduce-admission-wait-times.html. Breathe analyzers .https://www.breathometer.com/. http://www.coaguchek.com/coaguchek_patient/en/home/products/inrange.html- coagulation testing device. Companies supporting healthcare IoT market. available at: https://www.disruptordaily.com/iot-supporting-healthcare/. https://www.digitalistmag.com/iot/2018/01/26/iot-personalized-medicine-digital-transformation-is-creatingnew-business-models-for-life-science-05790043 (smart medication). Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., & Schreier, G. (2010). The internet of things for ambient assisted living. In 2010 seventh international conference on information technology (pp. 804e809). IEEE. Dubey, H., Goldberg, J. C., Abtahi, M., Mahler, L., & Mankodiya, K. (2015a). Echowear: Smartwatch technology for voice and speech treatments of patients with Parkinson’s disease. In Proceedings of the conference on wireless health (p. 15). ACM. Dubey, H., Goldberg, J. C., Makodiya, K., & Mahler, L. (2015b). A multi-smartwatch system for assessing speech characteristics of people with dysarthria in group settings. In Proceedings E-health networking, applications and services, healthcom, 2015 IEEE 17th international conference on, Boston, USA. https://dzone.com/articles/introduction-to-iot-sensors. https://econsultancy.com/internet-of-things-healthcare/. https://www.electronicshub.org/different-types-sensors/. https://www.fingent.com/blog/role-of-data-analytics-in-internet-of-things-iot. http://www.forbesindia.com/blog/health/why-healthcare-will-never-be-the-same-again-with-iot/. https://www.gatewaytechnolabs.com/blog/2017/07/how-iot-and-connected-devices-are-leading-the-healthcarerevolution/. https://www.getkisi.com/blog/internet-of-things-communication-protocols. Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A., et al. (2013). Bigbench: Towards an industry standard benchmark for big data analytics. In Proceedings of the 2013 ACM SIGMOD international conference on management of data (pp. 1197e1208). ACM. https://www.globalsign.com/en/blog/what-is-an-iot-gateway-device/. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47, 98e115. https://www.healthcareitnews.com/news/supply-chain-issues-costly-healthcare-cardinal-health-survey-finds. http://internetofthingsagenda.techtarget.com/feature/Using-an-IoT-gateway-to-connect-the-Things-to-the-cloud. https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/IoMT-A-pulse-on-the-internet-of-medical-things. https://internetofthingsagenda.techtarget.com/feature/Can-we-expect-the-Internet-of-Things-in-healthcare. https://www.iotforall.com/5-challenges-facing-iot-healthcare-2019/. Istepanian, R., Hu, S., Philip, N., & Sungoor, A. (2011). The potential of internet of mhealth things m-iot for noninvasive glucose level sensing. In Engineering in medicine and Biology society, EMBC, 2011 annual international conference of the IEEE (pp. 5264e5266). IEEE. Jara, A. J., Belchi, F. J., Alcolea, A. F., Santa, J., Zamora-Izquierdo, M. A., & Gómez- Skarmeta, A. F. (2010). A pharmaceutical intelligent information system to detect allergies and adverse drugs reactions based on internet of things. In Pervasive computing and communications workshops, PERCOM workshops, 2010 8th IEEE international conference on, IEEE (pp. 809e812). Kalantarian, H., Motamed, B., Alshurafa, N., & Sarrafzadeh, M. (2016). A wearable sensor system for medication adherence prediction. Artificial Intelligence in Medicine, 69, 43e52. Keriton. https://www.keriton.com/. Kumar, P., Verma, J., & Prasad, S. (2012). Hand data glove: A wearable real-time device for human-computer interaction. International Journal of Advanced Science and Technology, 43. Lee, J. Y., & Kondziolka, D. (2005). Thalamic deep brain stimulation for management of essential tremor. Journal of Neurosurgery, 103(3), 400e403.

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Li, Y., Chen, X., Tian, J., Zhang, X., Wang, K., & Yang, J. (2010). Automatic recognition of sign language subwords based on portable accelerometer and emg sensors. In International conference on multimodal interfaces and the workshop on machine learning for multimodal interaction (p. 17). ACM. Lifefuels, smart bottles. Available at: https://www.lifefuels.com. Mahn, T. G. (2013). Wireless medical technologies: Navigating government regulation in the new medical age. In Fishs regulatory & government Affairs group. https://www.marketsandmarkets.com/Market-Reports/iot-healthcare-market-160082804.html. MeruHealth .https://www.meruhealth.com. Orbita voice app. https://orbita.ai/. Pirouzan Group. Available at: http://pirouzansystem.com/. https://www.polytechnichub.com/advantages-disadvantages-bluetooth/. https://www.prnewswire.com/news-releases/takeda-and-cognition-kit-present-results-from-digital-wearabletechnology-study-in-patients-with-major-depressive-disorder-mdd-300558846.html. Propeller health, connected inhalers. https://www.propellerhealth.com/how-it-works/. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1). Sarji, D. K. (2008). Handtalk: Assistive technology for the deaf. Computer, 41(7), 84e86. Sensimed smart contact lenses. https://eyewire.news/articles/sensimed-announces-approval-of-the-sensimedtriggerfish-in-japan/. https://siliconangle.com/2015/06/30/smart-drugs-where-iot-meets-healthcare-a-market-snapshot/-enhanced drug management. Sokol, M. C., McGuigan, K. A., Verbrugge, R. R., & Epstein, R. S. (2005). Impact of medication adherence on hospitalization risk and healthcare cost. Medical Care, 43(6), 521e530. Suryadevara, N., Kelly, S., & Mukhopadhyay, S. (2014). Ambient assisted living environment towards internet of things using multifarious sensors integrated with xbee platform. In Internet of things (pp. 217e231). Springer. https://www.techopedia.com/definition/31460/internet-of-things-analytics-iot-analytics. ,. The Wireless Nano Retina Eyeglasses. Available at: http://www.nano-retina.com/. https://www.touchendocrinology.com/wp-content/uploads/sites/5/2016/04/Figure_1_Closed_loop.png. Traditional healthcare challenges and how IoT can change : https://www.forbes.com/sites/bernardmarr/2018/01/25/ why-the-internet-of-medical-things-iomt-will-start-to-transform-healthcare-in-2018/#321a89774a3c. TrueInject. Available at: https://www.truinject.com/. Verily smart lens program: https://blog.verily.com/2018/11/update-on-our-smart-lens-program-with.html. Wearable smart clothings .https://www.hexoskin.com/.

C H A P T E R

3 Big data based hybrid machine learning model for improving performance of medical Internet of Things data in healthcare systems Mamoon Rashid1, Harjeet Singh2, Vishal Goyal3, Shabir Ahmad Parah4, Aabid Rashid Wani5 School of Computer Science & Engineering, Lovely Professional University, Jalandhar, India; Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India; 3Department of Computer Science, Punjabi University, Patiala, India; 4Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, India; 5Department of Electronics & Communication, Shri Mata Vaishno Devi University, Katra, India

1 2

1. Introduction to Big Data and IoT Big Data Analytics along with Internet of Things (IoT) finds its use in areas of smart cities, healthcare, agriculture, and industrial automation units. The challenge of large amount of data generation in IoT devices is fulfilled by Big Data technologies in terms of its storage and processing (Chen, Chen, & Feng, 2016). The advanced IoT devices and their applications have given rise to voluminous data in different varieties (Mavromoustakis, Mastorakis, & Batalla, 2016). On the other side, Big Data technologies have discovered new kind of opportunities for developing IoT based systems. Therefore IoT based systems and Big Data technologies integration will create new challenges in terms of storage and processing which needs to be addressed by the researchers (Rashid, Goyal, Parah, & Singh, 2019; Rashid, Hamid, & Parah, 2019; Rashid, Singh, & Goyal, 2019).

Healthcare Paradigms in the Internet of Things Ecosystem https://doi.org/10.1016/B978-0-12-819664-9.00003-X

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© 2021 Elsevier Inc. All rights reserved.

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3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data

1.1 Types of Big Data The classification of Big Data is mostly given in terms of structure of data. The structure of data depends usually on its organization. Based on this, Big Data is classified into structured, unstructured, and semistructured data (Oussous, Benjelloun, Lahcen, & Belfkih, 2018). These types are explained below: Structured Data: Structured data is having fixed format and is easily stored, processed, and accessed. Structured data is always following particular order as in row and column format and always results into ordered output. This data is easy to process as the format of data is always known in advance. All traditional databases containing data in row column format belong to this category. Unstructured Data: Unstructured data is usually huge data which is not in organized manner. This kind of data remains usually unknown and poses numerous challenges while processing for valuable insights as output. Moreover this data is not having any kind of order and is raw in nature. Data in the form of images, audio, video, and sensor based data belong to this category. Semistructured Data: This kind of data usually contains both the forms but remain undefined. Usually this kind of data is not organized inherently at the beginning but it can be turned into structure form while taking its analysis. Representation of data in terms of XML files belongs to this category. The different types of Big Data are shown in Fig. 3.1.

1.2 Advantages of Big Data processing Improvement in Business Intelligence: The organizations which are using Big Data platforms access social media like Twitter, Facebook by using various application programming interfaces (API) and get enough insights to fine tune their strategies for the betterment of organization. Improvement in Customer Services: Big Data technologies resulted in a new kind of feedback systems for customers which are far better than traditional systems for getting feedback. The use of Natural Language Processing makes it possible on top of Big Data platforms for efficient evaluation of customer responses. Improvement in Operational Efficiency: Big Data platforms are used for the identification of significant data which is required for its processing and are quite productive in data warehouses.

FIGURE 3.1 Different types of Big Data.

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1.3 Architecture of IoT The IoT is defined as Internet of Everything which is dynamic network of machines capable of interacting with each other (Lee, I. et al. 2015). The essence of IoT is realized when communication is taking place in connecting devices and its integration with customer support systems, business analytics, and business intelligence applications. The stage-wise architecture of IoT is given by Boyes, Hallaq, Cunningham, and Watson (2018). The various things are inputted to stage of architecture where the presence of sensors remains in wired or wireless manner. The Internet Gateways and Data Acquisition Systems are used in next stage for data aggregation and its control (Singh & Rashid, 2015). The preprocessing and various analytics are performed in next stage for which services in terms data center or cloud is to be used. The whole process is explained in IoT architecture given in Fig. 3.2.

1.4 Standards of IoT applications There is no clear line for classifying IoT standards; however, the major standard protocols in use are based on IoT Data Link protocols (Salman & Jain, 2017). Physical layer and MAC layer protocols are mostly used by various IoT standards. IEEE 802.15.4: This standard is used in MAC layer and specifies source and destination addresses, headers, format of frames, identification in communication between the nodes. Low cost communication and high reliability is enabled in IoT by the use of channel hopping and synchronization in terms of time. IEEE 802.11ah: This kind of standard is used in traditional networking as Wi-Fi for IoT applications. This standard is used for friendly communication of power in sensors and supports lower overhead. This standard covers features of Synchronization Frame, Shorter MAC Frames, and Efficient Bidirectional Packet Exchange. WirelessHART: This standard works on MAC layer and uses time-division multiple access. This standard is more secure and reliable than other standards as it uses efficient algorithms for encryption purposes. Z-Wave: This standard works on MAC layer and was designed specifically for automation of homes. This standard works on mastereslave configuration where the master sends small messages to slaves and is used for point-to-point short distances.

FIGURE 3.2

Stage-wise architecture of IoT.

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ZigBee: This standard is a common one and is used for communication in healthcare systems and remote controls. This standard is meant for medium-level communications. DASH7: This standard is used on MAC layer and is used in RFID devices. This standard supports mastereslave architecture and is very suitable for IoT applications and is designed for IPv6 addressing. HomePlug: This standard is MAC based and is used in smart grid applications. The beauty of this standard lies in its power saving mode where it allows nodes to sleep when not required and wakes them only whenever required.

2. Relationship between Internet of Things and Big Data IoT is a conversion of range of things into smart objects like refrigerators, vehicles, or any electric and electronic gadgets. In IoT, sensors and computer-based chips are used for gathering data in case of those devices which cannot be linked to the internet (Riggins & Wamba, 2015). Whenever this gathered data from smart devices demonstrate volume, velocity, and variety, then the role of Big Data comes into picture along with IoT. Usually the data acquired via various sensors remains quite voluminous and carries data in terms of both structured and unstructured forms. The challenge of velocity in Big Data is the speed of data at which it is getting processed and always shows its presence in terms of IoT based data. Variety is the data in different forms and is one of forms in IoT based data. The major challenge in IoT is the way to handle large volumes of data which is getting generated from IoT devices. Big Data tools are having the capacity to handle this IoT based data with its continuous streaming nature of information. IoT and Big Data are cohesively related as IoT based data is usually raw in nature and it is Big Data analytics tools which are extracting information from this raw data to get valuable insights to bring smartness in IoT systems. However, the scale for conducting data in IoT is completely different and analytics platform should take care of exact solutions for extracting accurate data. The relation between Big Data and IoT is outlined by Ahmed, Imran et al. (2017), Ahmed, Yaqoob et al. (2017). There are various kinds of application domains like agriculture, shipping, and logistics organizations which are making use of Big Data and IoE together for offering insights and analysis (Rashid, Singh, Goyal, Ahmad, & Mogla, 2020). In agriculture, the crop fields are connected to monitoring systems for observing moisture levels in fields and later this data is provided to agriculture farmers for timely information. The shipping organizations are using sensor data and Big Data analytics for improving efficiency in terms of delivery of various vehicles to maintain their mileage and speeds.

3. Role of Big Data and IoT in Healthcare Systems In the era of E-Health systems, the inclusion of IoT has brought a greater change in the healthcare paradigms by promising the availability and accessibility of data with quite easiness (Bhatt, Dey, & Ashour, 2017). Industry specialists like Gartner predict the possibility of connecting 25 billion devices by year 2025 to IoT (Gartner, 2014). It will include medical

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devices for measuring heart rates, blood sugar, heart rates, mood swings, and body masses at various levels. All such data generated will be generated using efficient IoT systems which will be later processed with Big Data pipelines for meaningful insights (Manogaran et al., 2018). At present, amount of data generated in healthcare systems is growing exponentially due to the use of technologies like mHealth and biosensors. This includes the data coming from various softwares, electronic health records (EHR), and electronic patient record outcomes (ePRO). The role of Big Data in healthcare systems is to process the amount of bigger data within small time intervals and thus to minimize computation time (Dey, Hassanien, Bhatt, Ashour, & Satapathy, 2018). Big Data can predict diseases and thus avoid deaths which are preventable. The flow of processing in a Big Data pipeline is to take data from various kinds of sources like insurance and medical records and then to outline detailed picture of an individual within less time (Dimitrov, 2016). Big Data together with IoT healthcare becomes quite valuable where the patients’ data will be received by cloud platforms as a part of IoT and then later processed with the help of Big Data processing tools (Rashid, Goyal et al., 2019; Rashid, Hamid et al., 2019; Rashid, Singh et al., 2019). IoT systems with Big Data pipelines provide platforms where applications are managed and then to run analytics, to store and secure medical data (Rashid & Chawla, 2013). The security model for healthcare data is outlined using geographical locations and IP addresses (Rashid, Goyal et al., 2019; Rashid, Hamid et al., 2019; Rashid, Singh et al., 2019). This research has used the concept of constant key length encryption technique to secure healthcare data on cloud system. The role of IoT and Big Data in healthcare systems is shown in Fig. 3.3.

FIGURE 3.3

Role of IoT and Big Data in healthcare systems.

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3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data

4. Architecture of Apache Flume and Spark Apache Flume is a Big Data tool which is used for ingesting data on Hadoop Distributed File System (HDFS) for storage purpose (Hoffman, 2015). This tool is quite beneficial when one needs to collect and transport huge amount of real-time streaming data from various sources like social media and sensors in the form of temperature, pressure, and humidity. Apache Flume is a scalable tool when it comes to large streaming of real data and anytime if the read rate of data from any generator exceeds the rate of writing, this tool provides flow for maintaining the read and write rates steadily (Makeshwar, Kalra, Rajput, & Singh, 2015). The basic architecture of Apache Flume is given in Fig. 3.4.

4.1 Components of Apache Flume The major components used in Apache Flume architecture are events, agents, and clients. Flume Events: Event is the fundamental unit of data which moves inside Flume pipeline between data source and HDFS for final storage. Flume Events are data units which are carried out from source to destination with various kinds of headers associated with the data. Flume Agents: Flume Agents are responsible for the carriage of data from source to sink and receive data from clients. Apache Flume makes use of multiple agents for data transfer purposes until it reaches to final destination. Every Flume Agent internally contains three subcomponents in the form of source, channel, and sink. The source is used for receiving data from the data generator and transfers this received data to one of channels in medium. The channel is component which transfers the events received from source and acts as transient buffer until these events are taken by sink. The sink is used for storing the data in file systems like HDFS or HBase. Flume Clients: Client is the component which generates data in the form of events and then transfers this data to agents for transporting to HDFS environment. Apache Spark is a cluster-based system which is used for real-time processing. AMPLab developed Spark at UC Berkeley and later Apache Foundation made it open source under the name of Apache Spark. This project of Apache became quite prominent and popular in recent times by solving Big Data problems with faster computations (Meng et al., 2016). The overview of Apache Spark platform is shown in Fig. 3.5.

FIGURE 3.4

Architecture of Apache Flume for storage of real-time data.

4. Architecture of Apache Flume and Spark

FIGURE 3.5

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Overview of Apache Spark platform.

There is always a confusion among Big Data developers who often think Spark as another technology like that of Apache Hadoop. Although Apache Spark tends to act as replacement for Apache Hadoop in terms of its MapReduce programming model, however, it also provides support for MapReduce model to work with HDFS. Spark is actually meant for real-time computation and can run on HDFS to leverage its storage. Apache Spark uses inmemory computation for computing petabytes of data efficiently which is distributed across a cluster of thousands of physical servers. Usually the speed of Apache Spark is 100x times faster than conventional Hadoop which makes it quite capable for performing real-time analytics.

4.2 Components of Apache Spark The major components in Apache Spark are Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX (Salloum, Dautov, Chen, Peng, & Huang, 2016) and are shown in Fig. 3.6. Spark SQL: This component of Apache Spark is mainly used for processing structured data in terms of data frames. Spark SQL lets Big Data programmers to query structured data by using spark programs written in languages like java, python, or scala. Spark SQL can integrate with Apache Hive for running queries on existing warehouses using HiveQL queries. Spark SQL provides greater performance and scalability by providing an optimizer to run queries at faster rates. Moreover it makes use of spark engine for scaling thousands of nodes to make it scalable and fault tolerant when running queries. Spark Streaming: While using Hadoop for streaming, it is feasible to store data streams in terms of HDFS and later analyze them in terms of batches; however, Spark Streaming

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FIGURE 3.6 Core components of Apache Spark.

provides a way for processing and analyzing data in real time whenever it arrives without storing it in terms of batches. Earlier in Hadoop framework, programmers were supposed to use MapReduce programming model for processing of batches and later Apache project of Storm was required for processing batches in real time. The drawback of this approach lies in managing codebases by the developers which are running on different frameworks and there is always a challenge to keep these codebases in sync with one another. To counter this problem of batch streaming, spark introduced the concept of dividing streams into continuous micro batches and thus combining streaming with that of batch processing to get manipulated then using Spark API. This concept eventually minimized the overhead involved as code is now shared and runs on same framework for both streaming and batch operations. The overview of spark streaming is shown in Fig. 3.7. Spark MLlib: This library of Apache Spark includes a framework which is used for building machine learning models. This framework provides a support for making any

FIGURE 3.7 Overview of Spark Streaming.

5. Data analytics for IoT using Big Data analytics

55

transformations on given dataset including preprocessing, feature extraction and selection, cleaning of data on various forms. As Apache spark has the ability to store data in memory and to do in-memory computation, this feature makes it an excellent choice for training machine learning models and thus to reduce training time in building models. Big Data developers can make use of python language for writing programs and then saving them with MLlib and later importing into scala or java based environments. The overview of Spark MLlib library for creating machine learning models is shown in Fig. 3.8. GraphX Library: This library of Apache Spark is mainly used for processing graphs and their structures. This library is quite rich in graphical algorithms and uses Spark’s core features of Resilient Distributed Datasets (RDDs) for the modeling of data. The beauty of GraphX library is doing graph operations on data frames using GraphFrames package which results into faster processing of graph queries.

5. Data analytics for IoT using Big Data analytics The need of Big Data platforms and analytics environment in IoT has increased manyfold in last few years and provides valuable benefits and improvement in processes of decisionmaking. Therefore the requirements and demands of Big Data analytics platforms in IoT have increased for better analytics in its data processing. The inclusion of Big Data pipelines in IoT has completely changed the way for storing and analyzing the data. The bigger amounts of data generated by various sensor devices can be effectively processed by Big Data analytics for the extraction of meaningful insights. This section of chapter outlines key requirements required by IoT environment for processing data in Big Data analytics platform. Connectivity: Better connectivity is one of the important requirements in IoT environment for Big Data analytics on large amounts of machine generated sensor data (Ahmed, Imran et al., 2017; Ahmed, Yaqoob et al., 2017). Reliable connectivity is a way for connecting infrastructures with high performance with various kinds of objects for enabling services of IoT. Streaming Analytics: This kind of data analytics is another key requirement in IoT environments and deals with real-time data in motion. Data streams which are real time in nature

FIGURE 3.8

Overview of machine learning model creation in Apache Spark.

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3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data

are analyzed for the detection of critical situations. Big Data analytics platforms require data on the fly and process it in the form of data streams (Tönjes et al., 2014). Storage: Another key requirement in IoT based Big Data platforms is the storage of huge amounts of data generated by various IoT objects on some commodity storage units with low latency factors in its analytics. M2M communication protocols are widely used in most IoT services for handling large streams and provide benefits of cloud systems in terms of distributed storage (Suciu et al., 2015). Quality of Services: The requirement in IoT based Big Data platforms is the quality of services in mobile devices and IoT sensors in terms of resource management. The quality of service must be quite efficient in IoT based network where efficient data transfer is required from various devices and objects to Big Data platforms (Jin, Gubbi, Luo, & Palaniswami, 2012).

6. Big Data pipeline for IoT data storage and processing This section of chapter provides the novel outline of Big Data pipeline for processing IoT sensor data keeping various requirements into consideration.

6.1 Proposed Big Data pipeline for processing IoT medical data The proposed model takes care of storage of IoT based sensor data and then processes it on the Big Data real-time engine and later uses hybrid prediction model for predicting diabetes from trained healthcare data. The hybrid prediction model for prediction is discussed in Section 6.2. The idea is to connect IoT end point to IoT sensors for sensing the data. The IoT end point is supported with IoT backend server. IoT end point is connected with several sensors and backend server over wireless network. Message Queuing Telemetry Transport (MQTT) is the protocol which is used between IoT end point with backend server. The IoT sensor based data is transmitted to the cloud server which is later stored in Big Data storage unit of HDFS in terms of large volumes. The keywords for data ingestion in Apache Flume are used for diabetes medical data. The outliers in stored sensor data are filtered with the help of clustering method of density-based spatial clustering of applications with noise (DBSCAN) and the unwanted features are eliminated using Recursive Feature Elimination with Cross Validation (RFECV) where from the predictions are taken by applying machine learning classification technique of Random Forest (RF). The data in HDFS is processed on real-time basis with the help of Spark pipeline. The structure of proposed pipeline for storage and processing of IoT data is given in Fig. 3.9.

6.2 Hybrid prediction model for diabetes detection Outlier detection is done with the help of DBSCAN for a given dataset (Ester, Kriegel, Sander, & Xu, 1996). After the removal of outliers from sensor data, RF classifier is used for the prediction of diabetes trained data. 6.2.1 Step procedure of DBSCAN algorithm For given dataset of T, DBSCAN will work on two parameters: ε (eps) and minPts. ε is the measure of distance between two assumed points of “p” and “q” for checking the density

6. Big Data pipeline for IoT data storage and processing

FIGURE 3.9

57

Big Data based pipeline for processing IoT based medical data.

reachability from neighbors. minPts are the minimum number of points which are required to form the cluster. Step 1: Any point (P) is assumed as starting point which has not been visited before. Step 2: Select neighbors of this arbitrary starting point (P) on the basis of distance with ε. Step 3: If density of neighbors is achieved for the point (P), then it is marked as visited and clustering begins. Otherwise it is marked as noise. Step 4: If P is in cluster, then ε in its neighborhood is in cluster as well. Step 5: Repeat Step 2 for all ε neighborhood points until all points in cluster are taken. Step 6: New point which is unvisited and marked as clustering point or noise. Step 7: Repeat Step 2 to Step 6 until all points are visited and marked. RF classifier is an ensemble algorithm which operates based on individual large number of decision trees (Rodriguez-Galiano, Ghimire, Rogan, Chica-Olmo, & Rigol-Sanchez, 2012). Each individual decision tree in RF algorithm divides the class prediction and then the voting classifier is used to decide the model prediction based on maximum number of votes as shown in Fig. 3.10. The reason for better performance in RF classifier is the presence of low correlation in large number of individual models. The uncorrelated ensemble models produce ensemble predictions which are always better and accurate than the predictions given by individual classifiers (Liaw & Wiener, 2002). RF ensemble algorithm requires low correlations among decision trees to yield better results. The data from IoT sensors is stored on HDFS with the help of Apache Flume. The collected dataset is labeled with attributes for classification purposes. Once the outliers in data are

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3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data

FIGURE 3.10

Prediction using Random Forest classifier.

done using DBSCAN and features are eliminated using RFECV, the refined data is finally inputted to ensemble classifier for the prediction of type of diabetes in data. The achieved results are compared with Logistic Regression, Naïve Bayes, and RF. The proposed model of RFECV þ DBSCAN þ RF showed increase in classification accuracy in comparison to existing state-of-the-art. The performance of hybrid prediction model in terms of various performance measures is shown in Table 3.1. Proposed model is evaluated for performance in terms of Logistic Regression, Recursive Feature Elimination with Cross Validation (RFECV) with Logistic Regression, RFECV and PCA with Logistic Regression, RF with DBSCAN and RFECV classifiers, and the results achieved for hybrid prediction model of RFECV þ DBSCAN þ RF are better than other classifiers. The comparison performance accuracy is plotted in Fig. 3.11. Proposed model is evaluated for precision in terms of Logistic Regression, Recursive Feature Elimination with Cross Validation (RFECV) with Logistic Regression, RFECV and PCA with Logistic Regression, RF with DBSCAN and RFECV classifiers, and the results

TABLE 3.1

Comparison of prediction models for medical data.

Model

Accuracy (%)

Precision (%)

Recall (%)

F-Measure (%)

Roc_AUC (%)

Logistic regression

77.987

75.061

54.330

62.689

85.178

RFECV þ logistic regression

78.052

76.951

52.469

61.825

84.353

RFECV þ PCA þ logistic regression

77.987

78.161

50.885

60.933

84.527

RFECV þ DBSCAN þ RF

96.810

96.635

85.972

95.781

98.169

6. Big Data pipeline for IoT data storage and processing

FIGURE 3.11

59

Comparison of accuracy for various classifiers.

achieved for hybrid prediction model of RFECV þ DBSCAN þ RF are better than other classifiers. The comparison performance measure for precision is plotted in Fig. 3.12. Proposed model is evaluated for recall in terms of Logistic Regression, Recursive Feature Elimination with Cross Validation (RFECV) with Logistic Regression, RFECV and PCA with Logistic Regression, RF with DBSCAN and RFECV, and the results achieved for hybrid prediction model of RFECV þ DBSCAN þ SVM are better than other classifiers. The comparison performance measure in terms of recall is plotted in Fig. 3.13. Proposed model is evaluated for f-measure in terms of Logistic Regression, Recursive Feature Elimination with Cross Validation (RFECV) with Logistic Regression, RFECV and PCA with Logistic Regression, RF with DBSCAN and RFECV, and the results achieved for hybrid prediction model of RFECV þ DBSCAN þ SVM are better than other classifiers. The comparison performance measure in terms of recall is plotted in Fig. 3.14. Proposed model is evaluated for Roc_AUC in terms of Logistic Regression, Recursive Feature Elimination with Cross Validation (RFECV) with Logistic Regression, RFECV and PCA with Logistic Regression, RF with DBSCAN and RFECV, and the results achieved for hybrid prediction model of RFECV þ DBSCAN þ SVM are better than other classifiers. The comparison performance measure in terms of recall is plotted in Fig. 3.15.

FIGURE 3.12

Comparison of precision for various classifiers.

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3. Big data based hybrid machine learning model for improving performance of medical Internet of Things data

FIGURE 3.13

Comparison of recall for various classifiers.

FIGURE 3.14 Comparison of F-measure for various classifiers.

FIGURE 3.15

Comparison of Roc_AUC for various classifiers.

References

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7. Conclusion and future directions In this research, the authors have tried to make an attempt to solve the challenge of realtime processing of sensor based data and process it for classification using hybrid machine learning classifier. The IoT based sensor data is stored on HDFS which is processed on Big Data processing pipeline. The outliers in stored data were removed with the help of DBSCAN and then unwanted features were eliminated using RFECV. RF ensemble classifier is used for classifying classes in medical data. The results suggest that this hybrid prediction model is scalable for data processing of IoT based sensor data and prediction classification accuracy is much better than the models in state-of-the-art. However, there is still room of improvement for this model to use efficient feature selection with deep learning where the data to be trained is of multiclass type.

References Ahmed, E., Imran, M., Guizani, M., Rayes, A., Lloret, J., Han, G., et al. (2017). Enabling mobile and wireless technologies for smart cities. IEEE Communications Magazine, 55(1), 74e75. Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., et al. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459e471. Bhatt, C., Dey, N., & Ashour, A. S. (Eds.). (2017). Internet of things and big data technologies for next generation healthcare. Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1e12. Chen, Z., Chen, S., & Feng, X. (2016). A design of distributed storage and processing system for internet of vehicles. In 2016 8th international conference on wireless communications & signal processing (WCSP) (pp. 1e5). IEEE. Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare informatics research, 22(3), 156e163. Dey, N., Hassanien, A. E., Bhatt, C., Ashour, A. S., & Satapathy, S. C. (Eds.). (2018). Internet of things and big data analytics toward next-generation intelligence. Berlin: Springer. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96(34), 226e231. Gartner. (2014). Gartner says the Internet of Things will transform the data center. Available from http://www.gartner. com/newsroom/id/2684616. Hoffman, S. (2015). Apache flume: Distributed log collection for hadoop. Packt Publishing Ltd. Jin, J., Gubbi, J., Luo, T., & Palaniswami, M. (2012). Network architecture and QoS issues in the internet of things for a smart city. In Communications and information technologies (ISCIT), 2012 international symposium on (pp. 956e961). IEEE. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18e22. Makeshwar, P. B., Kalra, A., Rajput, N. S., & Singh, K. P. (2015). Computational scalability with Apache Flume and Mahout for large scale round the clock analysis of sensor network data. In 2015 national conference on recent advances in electronics & computer engineering (RAECE) (pp. 306e311). IEEE. Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375e387. Mavromoustakis, C. X., Mastorakis, G., & Batalla, J. M. (Eds.). (2016). Internet of things (IoT) in 5G mobile technologies (Vol. 8). Springer. Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., et al. (2016). Mllib: Machine learning in Apache spark. Journal of Machine Learning Research, 17(1), 1235e1241. Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431e448. Rashid, M., Goyal, V., Parah, S. A., & Singh, H. (2019). Drug prediction in healthcare using big data and machine learning. In Hidden Link prediction in stochastic social networks (pp. 79e92). IGI Global.

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Rashid, M., Hamid, A., & Parah, S. A. (2019). Analysis of streaming data using big data and hybrid machine learning approach. In A. Singh, & A. Mohan (Eds.), Handbook of multimedia information security: Techniques and applications. Cham: Springer. Rashid, M., Singh, H., & Goyal, V. (2019). Cloud storage privacy in health care systems based on IP and geo-location validation using K-mean clustering technique. International Journal of E-Health and Medical Communications, 10(4), 54e65. Rashid, M., Singh, H., Goyal, V., Ahmad, N., & Mogla, N. (2020). Efficient big data-based storage and processing model in internet of things for improving accuracy fault detection in industrial processes. In Security and privacy issues in sensor networks and IoT (pp. 215e230). IGI Global. Rashid, M., & Chawla, R. (2013). Securing data storage by extending role based access control. International Journal of Cloud Applications and Computing, 3(4), 28e37. https://doi.org/10.4018/ijcac.2013100103 Riggins, F. J., & Wamba, S. F. (2015). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In System sciences (HICSS), 2015 48th Hawaii international conference on (pp. 1531e1540). IEEE. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93e104. Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3e4), 145e164. Salman, T., & Jain, R. (2017). A survey of protocols and standards for internet of things. Advanced Computing and Communications, 1(1). Singh, P., & Rashid, E. (2015). Smart home automation deployment on third party cloud using internet of things. Journal of Bioinformatics and Intelligent Control, 4(1), 31e34. Suciu, G., Suciu, V., Martian, A., Craciunescu, R., Vulpe, A., Marcu, I., et al. (2015). Big data, internet of things and cloud convergenceean architecture for secure e-health applications. Journal of Medical Systems, 39(11), 141. Tönjes, R., Barnaghi, P., Ali, M., Mileo, A., Hauswirth, M., Ganz, F., et al. (2014). Real time iot stream processing and large-scale data analytics for smart city applications. In Poster session, European conference on networks and communications.

Further reading Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. Iqbal, M. H., & Soomro, T. R. (2015). Big data analysis: Apache storm perspective. International Journal of Computer Trends and Technology, 19(1), 9e14. Lee, I., & Lee, K. (2015). The internet of things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431e440.

C H A P T E R

4 The role of Internet of Things for adaptive traffic prioritization in wireless body area networks Fasee Ullah, Chi-Man Pun Department of Computer and Information Science, University of Macau, Macau SAR, China

List of acronyms ADC BMS CAP CFP CSMA/CA EAP GSM GTS IEEE IoT MAC PHY RAP SAR TDMA TG WBAN WSNs

Analog to Digital converter Biomedical Sensor Contention Access Period Contention-Free Period Carrier-Sense Multiple Access/Collision Avoidance Exclusive access phase Global System for Mobile Guaranteed Time slot Institute of Electrical and Electronics Engineers Internet of Things Medium Access Control Physical layer Random access phase Specific Absorption Rate Time-division multiple access Task Group Wireless body area network Wireless sensor networks

1. Introduction to wireless body area network A wireless body area network (WBAN) is increasingly gaining popularity in the domain of health monitoring using various biomedical sensors (BMSs). The aim of monitoring of vital signs of a person improves the standard living styles in different communities which assist

Healthcare Paradigms in the Internet of Things Ecosystem https://doi.org/10.1016/B978-0-12-819664-9.00004-1

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© 2021 Elsevier Inc. All rights reserved.

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4. The role of Internet of Things for adaptive traffic prioritization in wireless body area networks

in reducing the cost of regularly health checkup and informs the medical doctors in an emergency situation if there any life critical condition happens. Furthermore, various BMSs are deployed to monitor vital signs of a patient including heartbeat rate, temperature, glucose level, respiratory rate, ECG-based sensor, EMG-based sensor, and EEG-based sensor (Chao, Zeadally, & Hu, 2016). The deployment of BMSs for vital signs monitoring of a patient have been discovered in three methods that are on-body, implanted, and off-body sensors (Ullah, Hanan, Omprakash, Lloret, & Arshad, 2017; Ullah, Abdullah, Kaiwartya, & Cao, 2017), as shown in Fig. 4.1. On-body sensors are usually placed directly on the patient’s body for a particular monitoring of vital signs or alternatively, BMSs are stitched in shirt (clothes) of the patient. Examples of sensors are blood pressure, temperature, and ECG. Implanted sensors are tucked inside patients to monitor various vital signs that could be the lungs, kidney, and heart. In a recent development, endoscopy sensor is the best example. Off-body sensors are put around the patient to monitor different physical activities. These three methods based deployment of BMSs are connected with the centralized sensors, which is known as body coordinator, as shown in Fig. 4.1 and is responsible to receive monitored data and forward to the medical doctors for necessary actions.

FIGURE 4.1

Deployment of BMSs for monitoring of patient health condition.

2. Hardware architecture of biomedical sensor

65

2. Hardware architecture of biomedical sensor The core architecture of BMS has the closest resemblance to the ordinary sensors in wireless sensor networks (WSNs). However, BMSs in WBAN are used for monitoring of vital signs which are placed in the category of heterogeneity while sensors in WSN are used to monitor homogenous types of environments like detection of mines in battlefield has one type and all deployed sensors have the same nature of detection. Moreover, the architecture of BMS comprises of physiological signal sensor and a radio transceiver (Ullah, Abdullah, Kaiwartya, Kumar, & Arshad, 2017; Verma et al., 2017), as shown in Fig. 4.2. The purpose of the physiological sensor is to gain the changes in monitoring of vital signs. For example, monitor the changes in temperature of a body. The gained changes in the detection of vital signs are formed of analogue, which is converted into a digital representation for further processes. At the end, the radio transceiver employs as sender and receiver of a device, whereas the digitized data forwards to the centralized sensor and the centralized sensor transmits to the medical doctor. The physiological sensor observes data in the form of analog which is converted from analog to digital converter (ADC) for decision-making purposes with the support of main controller, working as main microprocessor. Sometimes, the microprocessor needs inputs for currently executing processes which are stored shortly in registers, and the microprocessor executes another process during interleave between processes. The power

FIGURE 4.2

Core hardware architecture of BMS.

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4. The role of Internet of Things for adaptive traffic prioritization in wireless body area networks

management unit is assigned to all parts of the BMS as shown in Fig. 4.2. However, this power unit is impractical to replace for implanted sensors. To overcome this issue, another future technology has been introduced that is the harvesting energy unit, which will charge itself from the heat of the body.

2.1 Methods for deployment of biomedical sensors There are different BMSs used to monitor different vital signs of a patient. The deployment of BMSs is performed in three methods as aforementioned and as shown in Fig. 4.1. The first method is on-body sensors where different BMSs are placed or stitched on/in the shirt of the body of the patient, as shown in Fig. 4.3A and B. The implantation of BMSs is the second method where different BMSs are inserted via surgery or swallowable, as depicted in Fig. 4.4A and B. The third method sensors are based on the monitoring of different physical activities of a patient like how he is sitting on the bed, sleeping position and duration and also observes the improvement in health, as shown in Fig. 4.5.

FIGURE 4.3 Wearable sensor: (A) on-body temperature (Wearble sensor, 2019) and (B) stitched in shirt of patient (Example of wearble sensor, 2019).

FIGURE 4.4 Blood pressure sensor implantation (Implementation of BP sensor, 2019): (A) sensor inserted with specialized inserter and (B) close insertion site.

2. Hardware architecture of biomedical sensor

67

FIGURE 4.5 Deployment of sensors around a patient for monitoring of different physical activities like sleeping time and different positions (Deployment of sensors around paitient, 2019).

2.2 Data dissemination of sensory information The topology is the physical arrangement of deployment of sensors to monitor vital signs of patients. Usually, the preferred topologies in WBAN are star and mesh (Hammi, Khatoun, Zeadally, Fayad, & Khoukhi, 2018). In star topology, all deployed sensors are connected directly with a centralized node which is responsible to receive data from sensors and forwards to the medical doctor for necessary actions. The whole scenario is presented in Fig. 4.6. While the mesh topology based sensors are preferred when sensors are away from the range of centralized node or that sensor has minimum energy and is not able to transmit. In addition, BMSs use relay/intermediated nodes when the designated nodes are hotspots. Mesh topology is depicted in Fig. 4.7. Three nodes (connected via wireless links on a patient's body; black in print version) are designated as relay nodes for data transmission to the centralized node, where this node will forward data to the medical doctor for diagnoses of diseases and optimal treatment.

2.3 Requirements of BMSs in WBAN The different types of BMSs are presented in detail as depicted in Table 4.1. Table 4.1 shows the different types of biosensors with the required data transmission rate and their purposes.

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4. The role of Internet of Things for adaptive traffic prioritization in wireless body area networks

FIGURE 4.6 Star topology.

3. Applications of medical sensors The applications of medical biosensors have been categorized into existing applications and future applications (Ullah, Abdullah, Kaiwartya, Kumar, et al., 2017; Verma et al., 2017; Wang et al., 2018). First of all, the existing applications of biosensors are presented. A WBAN has gained a wide popularity in the monitoring of health in various domains due to which it is applicable in monitoring vital signs of firefighters and soldiers during their field operations. Furthermore, during different sports activities like swimming and car racing, different deployed BMSs monitor various vital signs to maintain health fitness. In the same fashion, different postural movement detection is employed like to authenticate the person and open door automatically. Last but not least, the telemedicine is one the accepted technology to treat the patients remotely with the support of the paramedic staffs. In the future applications of biosensors, the most famous one would be to assist the car’s driver for adjusting the seat of the car using embedded sensors with the support of back pressure of the body. During pushing of car seat, the embedded sensors will monitor temperature, blood pressure, heartbeat rate, and ECG. Moreover, the chip-based cards will not be used directly to communicate with electronic machines in the future but it will be converted into smart cards which will do communication from packets like using ATM machines and authentication of employee at office entry without proving its identity.

4. Data dissemination protocols in WBAN The data dissemination architecture of WBAN is classified into routing layer and medium access control (MAC) layers (Ullah, Abdullah, Abdul-salaam, & Arshad, 2017). IEEE organization has published official documentation for guidelines of designing of MAC and PHY

4. Data dissemination protocols in WBAN

69

FIGURE 4.7 Data dissemination using mesh topology on the patient’s body.

layers. However, IEEE has no publication document regarding designing of routing protocols. Routing and MAC layers are discussed with highlighted points in the following.

4.1 Routing protocols The data communication in routing layer is different from the existing layers of WSNs due to heterogeneous data as compared to homogenous data, respectively. Therefore, there are five types of routing protocols that exist in routing of WBAN that are temperature-aware, cluster, QoS, postural movement, and cross-layer protocols (Ahmad et al., 2014; Ullah, Abdullah, Kaiwartya, Kumar, et al., 2017), as shown in Fig. 4.8.

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4. The role of Internet of Things for adaptive traffic prioritization in wireless body area networks

TABLE 4.1

Summary of BMSs.

Type of sensor

Required transmission rate

Purpose

Heartbeat

1.99 kbps

Monitor the changes in ranges of threshold values of heart

Respiration

0.22e9 kbps

Measures the level of respiration

Blood pressure