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Internet of Things From Data to Insight
 1119545269, 9781119545262

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The Internet of Things From Data to Insight

Edited by John Davies British Telecommunications plc, Ipswich, UK

Carolina Fortuna Jožef Stefan Institute, Ljubljana, Slovenia

This edition first published 2020 © 2020 John Wiley & Sons Ltd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of John Davies and Carolina Fortuna to be identified as the authors of the editorial material in this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Applied for HB ISBN: 9781119545262 Cover Design: Wiley Cover image: Bangkok cityscape © Mongkol Chuewong/Getty Images, Internet of things © MONOPOLY919/Shutterstock, Business data market elements © Macrovector/Shutterstock Set in 9.5/12.5pt STIXTwoText by SPi Global, Chennai, India Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY 10 9 8 7 6 5 4 3 2 1

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Contents About the Editors xi List of Contributors xiii Acknowledgments xvii 1 1.1 1.2 1.3 1.4

2 2.1 2.2 2.2.1 2.3 2.3.1 2.3.2 2.3.3 2.4 2.4.1 2.5

3 3.1 3.2

Introduction 1 John Davies and Carolina Fortuna Stakeholders in IoT Ecosystems 3 Human and IoT Sensing, Reasoning, and Actuation: An Analogy 4 Replicability and Re-use in IoT 5 Overview 6 References 7 Connecting Devices: Access Networks 9 Paul Putland Introduction 9 Overview of Access Networks 10 Existing Technologies Are Able to Cover a Number of IoT Scenarios 10 Low-Power Wide Area Network (LPWAN) 12 Long-Range (LoRa) Low-Power Wide Area Network 14 Sigfox Low-Power Wide Area Network 14 Weightless Low-Power Wide Area Network 15 Cellular Technologies 15 Emerging 5G Cellular Technology 16 Conclusion 18 References 18 Edge Computing 21 Mohammad Hossein Zoualfaghari, Simon Beddus, and Salman Taherizadeh Introduction 21 Edge Computing Fundamentals 22

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Contents

3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.4 3.4.1 3.4.2 3.4.3 3.5 3.6 3.7

Edge Compute Strategies 22 Network Connectivity 25 Edge Computing Architecture 25 Device Overview 25 Edge Application Modules 26 IoT Runtime Environment 26 Device Management 27 Secure Runtime Environment 27 Implementing Edge Computing Solutions 28 Starter Configuration 28 Developer Tools 28 Edge Computing Frameworks 29 Zero-Touch Device On-boarding 30 Applying Edge Computing 32 Conclusions 33 References 33

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Data Platforms: Interoperability and Insight John Davies and Mike Fisher Introduction 37 IoT Ecosystems 38 Context 40 Aspects of Interoperability 41 Discovery 41 Access Control 43 Data Access 44 Conclusion 48 References 49

4.1 4.2 4.3 4.4 4.4.1 4.4.2 4.4.3 4.5

5 5.1 5.2 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5 5.3 5.4 5.4.1 5.4.2 5.4.3 5.5

Streaming Data Processing for IoT 51 Carolina Fortuna and Timotej Gale Introduction 51 Fundamentals 52 Compression 52 Dimensionality Reduction 52 Summarization 53 Learning and Mining 53 Visualization 53 Architectures and Languages 54 Stream Analytics and Spectrum Sensing 56 Real-Time Notifications 57 Statistical Reporting 57 Custom Applications 58 Summary 59 References 60

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Contents

6 6.1 6.2 6.3 6.3.1 6.3.2 6.4 6.5 6.5.1 6.5.2 6.5.3 6.6 6.7

7 7.1 7.2 7.3 7.4 7.4.1 7.4.2 7.5 7.6

8 8.1 8.2 8.3 8.4 8.5 8.6

9 9.1 9.2 9.3

Applied Machine Vision and IoT 63 V. García, N. Sánchez, J.A. Rodrigo, J.M. Menéndez, and J. Lalueza Introduction: Machine Vision and the Proliferation of Smart Internet of Things Driven Environments 63 Machine Vision Fundamentals 65 Overview of Relevant Work: Current Trends in Machine Vision in IoT 67 Improved Perception for IoT 67 Improved Interpretation and Learning for IoT 68 A Generic Deep Learning Framework for Improved Situation Awareness 69 Evaluating the Impact of Deep Learning in Different IoT Related Verticals 70 Sensing Critical Infrastructures Using Cognitive Drone-Based Systems 70 Sensing Public Spaces Using Smart Embedded Systems 71 Preventive Maintenance Service Comparison Based on Drone High-Definition Images 72 Best Practice 74 Summary 75 References 75 Data Representation and Reasoning 79 Maria Maleshkova and Nicolas Seydoux Introduction 79 Fundamentals 80 Semantic IoT and Semantic WoT (SWoT) 81 Semantics for IoT Integration 82 IoT Ontologies and IoT-O 83 The Digital Twin Approach 85 Use Case 87 Summary 88 References 89 Crowdsourcing and Human-in-the-Loop for IoT 91 Luis-Daniel Ibáñez, Neal Reeves, and Elena Simperl Introduction 91 Crowdsourcing 92 Human-in-the-Loop 95 Spatial Crowdsourcing 97 Participatory Sensing 99 Conclusion 100 References 101 IoT Security: Experience Is an Expensive Teacher 107 Paul Kearney Introduction 107 Why Is IoT Security Different from IT Security? 108 What Is Being Done to Address IoT Security Challenges? 110

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9.3.1 9.3.2 9.3.3 9.4 9.4.1 9.4.2 9.4.3 9.5

Governments 110 Standards Bodies 111 Industry Groups 112 Picking the Low-Hanging Fruit 113 Basic Hygiene Factors 113 Methodologies and Compliance Frameworks 115 Labeling Schemes and Consumer Advice 116 Summary 117 References 118

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IoT Data Privacy 121 Norihiro Okui, Vanessa Bracamonte, Shinsaku Kiyomoto, and Alistair Duke Introduction 121 Basic Concepts in IoT Data Privacy 122 What Is Personal Data? 122 General Requirements for Data Privacy 123 Personal Data and IoT 124 Existing Privacy Preservation Approaches 126 Toward a Standards-Based Approach in Support of PIMS Business Models 128 A Data Handling Framework Based on Consent Information and Privacy Preferences 129 A Data Handling Framework 129 Privacy Preference Manager (PPM) 130 Implementation of the Framework 131 Standardization for a User-Centric Data Handling Architecture 132 Introduction to oneM2M 132 PPM in oneM2M 133 Example Use Cases 133 Services Based on Home Energy Data 133 HEMS Service 133 Delivery Service 134 Conclusions 137 References 137

10.1 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 10.3 10.3.1 10.3.2 10.3.3 10.4 10.4.1 10.4.2 10.5 10.5.1 10.5.2 10.5.3 10.6

Blockchain: Enabling Trust on the Internet of Things 141 Giampaolo Fiorentino, Carmelita Occhipinti, Antonello Corsi, Evandro Moro, John Davies, and Alistair Duke 11.1 Introduction 141 11.2 Distributed Ledger Technologies and the Blockchain 143 11.2.1 Distributed Ledger Technology Overview 143 11.2.2 Basic Concepts and Architecture 145 11.2.2.1 Consensus Algorithm 148 11.2.3 When to Deploy DLT 149 11.3 The Ledger of Things: Blockchain and IoT 150 11.4 Benefits and Challenges 150 11

Contents

11.5 11.6

Blockchain Use Cases Conclusion 154 References 154

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Healthcare 159 Duarte Gonçalves-Ferreira, Joana Ferreira, Bruno Oliveira, Ricardo Cruz-Correia, and Pedro Pereira Rodrigues Internet of Things in Healthcare Settings 159 Monitoring Patient Status in Hospitals 160 IoT from Healthcare to Everyday Life 160 Systems Interoperability 161 BigEHR: A Federated Repository for a Holistic Lifelong Health Record 163 Why a Federated Design? 164 System Architecture 164 Gathering IoT Health-Related Data 165 From Inside the Hospitals 166 Feeding Data from Outside Sources 166 Extracting Meaningful Information from IoT Data 167 Privacy Concerns 167 Distributed Reasoning 167 Outlook 168 Acknowledgments 169 References 169

12.1 12.1.1 12.1.2 12.1.3 12.2 12.2.1 12.2.2 12.3 12.3.1 12.3.2 12.4 12.4.1 12.4.2 12.5

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13.1 13.2 13.2.1 13.3 13.4 13.4.1 13.5

14 14.1 14.2 14.3 14.4 14.5

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Smart Energy 173 Artemis Voulkidis, Theodore Zahariadis, Konstantinos Kalaboukas, Francesca Santori, and Matevž Vuˇcnik Introduction 173 Use Case Description 175 The Role of 5G in the Smart Grid IoT Context 177 Reference Architecture 178 Use Case Validation 182 AMI-Based Continuous Power Quality Assessment System 183 Conclusion 187 Acknowledgment 187 References 187 Road Transport and Air Quality 189 Charles Carter and Chris Rushton Introduction 189 The Air Pollution Challenge 191 Road Traffic Air Pollution Reduction Strategies 193 Monitoring Air Pollution Using IoT 194 Use Case: Reducing Emissions Through an IoT-Based Advanced Traffic Management System 196

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14.6 14.7

Limitations of Average Speed Air Quality Modeling 201 Future Roadmap and Summary 202 References 203

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Conclusion 207 John Davies and Carolina Fortuna Origins and Evolution 207 Why Now? 207 Falling Costs and Miniaturization 208 Societal Challenges and Resource Efficiency 208 Information Sharing Comes of Age 208 Managing Complexity 208 Technological Readiness 208 Maximizing the Value of Data 209 Commercial Opportunities 209 A Glimpse of the Future 210 References 212

15.1 15.2 15.2.1 15.2.2 15.2.3 15.2.4 15.2.5 15.3 15.4 15.5

Index 213

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About the Editors Professor John Davies is Chief Researcher in BT’s Research & Innovation Department, where he leads a team focused on Internet of Things technologies. He has a strong track record of researching and innovating and his current research interests include the application of Internet of Things and semantic technologies to smart cities, smart transport, business intelligence, and information integration. He currently leads BT’s contribution to the UK flagship Manchester-based CityVerve IoT smart city programme as well as the H2020 NRG-5 Smart Energy project and he co-wrote the Hypercat IoT standard. John has authored several technical books and written over 90 scientific publications. He is the inventor of several patents. He is a Fellow of the British Computer Society and a Chartered Engineer. John is a visiting professor at a number of UK universities and holds a PhD in Artificial Intelligence from the University of Essex, UK. Carolina Fortuna is a Senior Research Fellow at the Jožef Stefan Institute. She received her BSc in 2006 from the Technical University of Cluj-Napoca, Romania, her PhD in 2013 in Slovenia, was a postdoctoral research associate at Ghent University 2014–2015 in Belgium, and a visiting researcher at Stanford University, USA, in 2017. Her research is interdisciplinary focusing on semantic technologies with applications in modeling of communication and sensor systems and on combining semantic technologies, statistical learning and networks for analyzing large datasets. She has participated in multiple collaborative research projects, taking leadership roles in several. She has co-authored over 50 peer-reviewed publications and gained industry insight by working with Bloomberg LP (New York) and Siemens PSE (Romania).

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List of Contributors Simon Beddus British Telecommunications plc Ipswich UK

Alistair Duke British Telecommunications plc Ipswich UK

Vanessa Bracamonte KDDI Research, Inc. Saitama Japan

Joana Ferreira CINTESIS – Center for Health Technology and Services Research Faculdade de Medicina da Universidade do Porto Porto Portugal

Charles Carter Smart Cities Journalist London UK Antonello Corsi Engineering Ingegneria Informatica Spa Roma Italy Ricardo Cruz-Correia CINTESIS – Center for Health Technology and Services Research Faculdade de Medicina da Universidade do Porto Porto Portugal John Davies British Telecommunications plc Ipswich UK

Mike Fisher British Telecommunications plc Ipswich UK Giampaolo Fiorentino Engineering Ingegneria Informatica Spa Roma Italy Carolina Fortuna Jožef Stefan Institute Ljubljana Slovenia Timotej Gale Jožef Stefan Institute Ljubljana Slovenia

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List of Contributors

V. García Visiona Madrid Spain Duarte Gonçalves-Ferreira CINTESIS – Center for Health Technology and Services Research Faculdade de Medicina da Universidade do Porto Porto Portugal Luis-Daniel Ibáñez Web and Internet Science Department University of Southampton Southampton UK Konstantinos Kalaboukas Singularlogic SA Athens Greece Paul Kearney Department of Computer Science Birmingham City University Birmingham UK and Etisalat–BT Innovation Centere (EBTIC) Abu Dhabi United Arab Emirates Shinsaku Kiyomoto KDDI Research, Inc. Saitama Japan J. Lalueza Visiona Madrid Spain

Maria Maleshkova Computer Science Institute University of Bonn Bonn Germany J. M. Menéndez Grupo de Aplicación de Telecomunicaciones Visuales Universidad Politecnica de Madrid Madrid Spain Evandro Moro British Telecommunications plc Ipswich UK Carmelita Occhipinti Cybernetics Lab Cardito Italy Norihiro Okui KDDI Research, Inc. Saitama Japan Bruno Oliveira CINTESIS – Center for Health Technology and Services Research Faculdade de Medicina da Universidade do Porto Porto Portugal Paul Putland British Telecommunications plc Ipswich UK

List of Contributors

Neal Reeves Web and Internet Science Department University of Southampton Southampton UK

Elena Simperl Web and Internet Science Department University of Southampton Southampton UK

A. Rodrigo Visiona Madrid Spain

Salman Taherizadeh Jožef Stefan Institute Ljubljana Slovenia

Pedro Pereira Rodrigues CINTESIS – Center for Health Technology and Services Research Faculdade de Medicina da Universidade do Porto Porto Portugal

Artemis Voulkidis Power Operations Ltd Swindon UK

Chris Ruston Connected Places Catapult London UK N. Sánchez Visiona Madrid Spain Francesca Santori ASM Terni Italy Nicolas Seydoux Departments of SARA and MELODI LAAS-CNRS, CNRS, INSA, IRIT University of Toulouse Toulouse France

Matevž Vuˇcnik Jožef Stefan Institute Ljubljana Slovenia Theodore Zahariadis TEI of Sterea Ellada Lamia Greece Mohammad Hossein Zoualfaghari British Telecommunications plc Ipswich UK

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Acknowledgments The editors wish to thank Dr. Andrew Reeves for proofreading a number of chapters, Paul Deans for valuable input on graphical design, and Maruša Mazej for reworking the figures and thereby improving the appearance and accessibility of the book. Chapters 11 and 13 are partly based on work done on the NRG5 project, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 762013.

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1 Introduction John Davies 1 and Carolina Fortuna 2 1 2

British Telecommunications plc, Ipswich, UK Jožef Stefan Institute, Ljubljana, Slovenia

The physical world is becoming ever more closely connected to information systems as sensors and actuators are incorporated into a wide variety of physical objects – from highways to pacemakers to cattle to running shoes to factories – and then connected to the Internet via a range of wired and wireless networks. This is the Internet of Things (IoT) and it is already generating massive volumes of data. The result is that much richer information can be collected (in real time) and used by automated systems to provide actionable insight and to respond to changing contexts with appropriate intelligent actions. IoT has rapidly moved from the conceptual phase to widespread use in real-world applications in recent years. The IoT will deliver significant innovation in many different areas, including future cities, transport, health and social care, manufacturing, and agriculture. Sensors can now be deployed at low cost to instrument the world to a far greater extent than has been possible before. There is increasing recognition of the potential value in opening up data resources so that they can be exploited more fully. At the highest level, many of the IoT applications being considered appear similar – involving the collection of information from a range of sensors and other sources, interpreting this in a specific context, and then making better decisions that improve a behaviour or a process. For instance, smart watches or other types of wearable sensing devices are able to drive improvements in our behaviour toward a healthier daily routine. Merchandise tracking sensors can lead to a better understanding of supply chains and deliver optimization of costs and minimization of carbon footprints. IoT has a unique potential for automating and improving man-made systems and behaviours by enabling unprecedented understanding and insight. For example, IoT data enabled a recent comprehensive global study across 111 countries on the impact of physical activity variation and the built environment on health [1]. The IoT has been a recurrent theme among commentators since the term was coined in the late 1990s. The concept has evolved from early work on Radio Frequency Identifier (RFID) technology which represented a hardware related break-through that aimed to connect everyday objects to a network. This perhaps constituted the first wave of the IoT, which then developed beyond the initial hardware world innovation, and focused increasingly The Internet of Things: From Data to Insight, First Edition. Edited by John Davies and Carolina Fortuna. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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1 Introduction

ENVIRONMENT

SENSORS

CONNECTIVITY

COMPUTATION

APPLICATION

Light Sensor

Wi-Fi

Data Analytics

Smart Street Lighting

Bin Usage

MESH

Data Access

Waste Management

Vehicle Temeletry

UNB

Interoperability

Tracking Assets

Soil Moisture

5G

Edge Computing

Smart Parking

Parking Sensor

WPAN

Cloud Computing

Driver Assist

RFID Trace

LORA

Privacy / Security / Trust

Figure 1.1

The IoT ecosystem.

on developing new types of sensors and sensing materials, as well as on developing new communication technologies and protocols. As a result, a wide variety of new communication technologies emerged in the early years of the twenty-first century which were able to support the ubiquitous deployment of a wide variety of sensors. We refer to this as the second wave of IoT. In the last decade, the focus of IoT has shifted to data collection,

1.1 Stakeholders in IoT Ecosystems

processing and security aspects and this period is termed the third wave of IoT. This book focuses primarily on this most recent wave and covers all key aspects including data management, processing, and analytics as well as security, privacy and trust as depicted in Figure 1.1. Real-world examples are given that show the application of IoT technologies in a number of different sectors.

1.1

Stakeholders in IoT Ecosystems

A number of different actors typically participate in any deployment of IoT technology and we will refer to this set of stakeholders and the relationships between them as the IoT ecosystem. Such stakeholders may play one or more different roles. These include sensor providers, connectivity providers, information providers, application developers, analytics service providers, platform providers, and end users of information and applications. Information providers in IoT ecosystems are often owners of sensor deployments. The primary purpose of their sensors may be for their own use but they may choose to make some of their data available to others, either on a commercial basis, to meet their obligations (particularly for public sector organizations), or for the general good. Various data processing platforms may also be information providers, even if they are not directly associated with “Things.” We refer to these as derived information providers; while not being the primary source of any information, they create value by combining data from multiple sources, transforming or applying various analytical techniques. These additive data sources could include: contextual (e.g. geographical, administrative) information; notifications of events such as traffic incidents and sporting fixtures; or, perhaps, rare events such as anomalies in a production process. In efficient IoT ecosystems, information providers should be able to easily publish their services or data resources and advertise their availability via an easily accessible catalogue so that potential users can independently discover and assess their utility. This scenario is perhaps similar to the app stores that are commonplace today to make applications easily available. It is important to note that making data available should not imply relinquishing ownership rights; consequently, information providers also need the ability to define access controls, together with terms and conditions for use of the data they publish. Platform providers have a key enabling role in the IoT ecosystem. They do not directly provide information or build dedicated services or applications but support stakeholders in other roles by providing a set of functionalities that all can use. This allows other participants in the ecosystem to focus on their own core activities and helps to accelerate innovation in the ecosystem. Platform providers may provide computing and storage infrastructure, as well as analytics services, which could include artificial intelligence (AI) capabilities such as summarization, enrichment, and reasoning. Each platform provider will use specialist hardware and software tools and offer generalpurpose frameworks that an end user can exploit to define their own workflows. For instance, an edge or cloud provider offers on demand compute and storage resources that can be configured and modified on demand by users. Certain platform providers, typically application domain experts, offer a more complete service, including consultancy services, to support end users who may not have the necessary systems, data science, or analytics expertise.

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1 Introduction

Application developers produce applications that process the available data within a specific context to produce actionable insight for end users. Application developers should be able to discover what data and platform resources are available to them, what the key features and costs of each resource are, and assess which ones meet the needs of the applications that they want to build. This includes both the information content of the resources and practical considerations for the resources, such as dependability (accuracy, availability, etc.), conditions of use, or commercial considerations. End users participate in the ecosystem by using the information and applications that are made available to them by other stakeholders. The end users can be private persons or institutional decision makers. As the ultimate beneficiaries of the functionality provided by the other stakeholders, it is important that their experience is positive and the ecosystem delivers real value for them. An IoT ecosystem will not be sustainable without the trust of its end users. For individual end users, participation in the ecosystem is generally via an application. Often this application will make use of information that is generated through their use of the application, for example the user’s location is often used as a data source by applications on mobile phones. The situation where the individual is an information provider needs to be addressed with care, particularly where personally identifiable or potentially sensitive information may be involved. Open engagement with end users that ensures they are properly informed and understand that they are included in the ecosystem is essential.

1.2 Human and IoT Sensing, Reasoning, and Actuation: An Analogy Along with IoT, artificial intelligence (AI) comprises an increasingly pervasive and important set of technologies. Recent years have seen significant advances in AI in a number of areas [2]. IoT and AI are inevitably interconnected, given the vast volumes of rich data generated by IoT and the ever-increasing capability of AI systems to analyze, extract insight, and make decisions from that data. Thus, any discussion of the role and impact of AI would be incomplete without consideration of the link to IoT and in this volume a number of chapters are included that discuss the role of AI in IoT systems. The vision of the IoT is that digital systems can be given the ability to sense, process, and extract useful information and actionable insight from the world and respond to the environment accordingly (typically via actuation). From an AI and robotics perspective, we can make an analogy with human sensing/actuating capabilities and the five human senses that receive inputs from the external environment. These stimuli are sent to the brain via the nervous system, and finally the brain processes the stimuli as depicted in Figure 1.2. The result is typically information generation and in some cases is also the initiation of action: the brain transmits commands to muscles, which then trigger motion or speech, or another appropriate response. By analogy, the “things” in the IoT are the sense organs, which detect the stimuli. Devices featuring microphones detect sound; ones featuring gas sensors are able to detect gases such as volatile organic compounds; ones featuring cameras are able to record images or videos; ones featuring accelerometers are able to record motion and vibrations; and so on.

1.3 Replicability and Re-use in IoT

Figure 1.2

Human versus IoT: the sense, process, and actuate analogy.

The sensed data can then be processed locally on the devices (“edge processing” in the terminology of IoT) or sent via wireless or wired technology to data platforms (processing and storage engines, such as the Information Exchange depicted in Figure 1.1). This model is similar to how stimuli from sensory organs are sent via the nerves to the brain. These processing and storage engines then process the received information and generate actionable insight or other types of knowledge. In more advanced applications, systems can also initiate an action such as adjusting a setting in a heating system, sending a tweet, or actuating hardware controlling an industrial process. One key distinction between the IoT and the way in which humans process and react to sense data is that, while in the case of the humans the sensors and processors are co-located, in the case of IoT the system is typically distributed as shown in Figure 1.2. This analogy has inspired researchers and enthusiasts for decades, but, in spite of some reports in the media, relevant sensorics, robotics, and AI technologies are still far from achieving human capabilities.

1.3

Replicability and Re-use in IoT

There are two important classes of sensor-based IoT applications – those that aim to monitor and respond to time-sensitive conditions and those that collect data over a longer period of time for analysis of a longitudinal dataset. In either case, much of the time and effort involved can be spent on activities that are generic. It is advantageous to build new applications in an environment where these generic problems have already been solved by others, with robust solutions available to all. A key technical aspect of IoT is the need to work at a very large scale (many devices, large volumes of data, and with ever-increased scope for automation). We are also seeing increasing potential to share information much more widely. These needs are being driven by ever-decreasing component costs and device miniaturization. As explained above, an IoT ecosystem consists of a number of independent stakeholders, all sharing a common interest in particular kinds of information and obtaining benefit from participation in the ecosystem. This could be as a commercial provider of information or analytic services, as an application developer or as an end user, for example. Use of shared services and facilities generally involves a compromise – typically giving up some level of direct control in return for reduced costs. In the case of today’s global

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1 Introduction

communications networks (including the Internet), the case for common services is very strong. Cloud computing and storage are also becoming widely accepted, although there are still many situations where private infrastructure is preferred. The potential for an IoT ecosystem to stimulate and enable innovation is clear, but all participants need to have confidence in the value proposition and be convinced that it meets their needs. If this is not the case then a sustainable ecosystem will not be possible. Areas of concern for participants will include security and trust, respect for personal and commercial rights, dependability, performance, the ability to comply with legal and regulatory obligations, and cost. Predictability, simplicity, and flexibility are additional important characteristics.

1.4 Overview In this book, we address the entire vertical technology stack of IoT, with special emphasis on the data aggregation, processing, management, analysis, and exploitation aspects. Importantly, we also discuss recent developments in distributed trust, security, and privacy options. Currently we are at a critical point in the development of IoT. While there is a clear need for sensor-based, data-driven decision making with the potential for significant commercial and societal benefits, there are also increasing data misuse concerns that malicious users could abuse, with such a system causing unintended actions and destabilizing normal operation. This book is comprised of two main parts. Firstly, it brings together a description of the full technology stack of IoT with, as mentioned, a focus on the data-driven aspects. These include data modeling, processing, and security. There is also discussion on the critical related aspects of connectivity, privacy, and trust. The second part of the book explains how this technology is being applied in practice and the benefits that it is delivering by providing a number of chapters describing specific applications across a number of industry sectors. The first part of the book can be seen as comprising three subparts. The first subpart, formed of Chapters 2 and 3, introduces data collection (connectivity) and computational infrastructure. The second subpart, comprised of Chapters 4 to 8 discusses the various aspects of data processing. Finally, Chapters 9 to 11 discuss security, trust, and privacy challenges as related to IoT. More specifically, Chapter 2 analyzes connectivity options for the IoT, with particular focus on dedicated low-power wide-area network and cellular technologies. Enabling low-power communications is perhaps the most important challenge for IoT devices, which are often battery-operated. Chapter 3 introduces emerging edge computing architectures and technologies. Topics include data computation close to the network edge as well as the challenge of the efficient management of large numbers of devices through their lifecycle. Chapter 4 discusses IoT data platforms and the need for data interoperability, so that data coming from various IoT systems can be more easily integrated for developing informed decision-making systems and thereby maximizing the value of IoT data. Chapter 5 focuses on architectures and emerging technologies that enable the processing of streaming data. IoT deployments in several application areas, especially for fault detection in critical systems, should produce real-time insights for alerting and decision making, meaning

References

that specialized data stream processing systems are often required. Chapter 6 describes the important role of computer vision in IoT, particularly in drone-operated scenarios using relatively lightweight computation. Chapter 7 introduces structured knowledge representation and reasoning technologies for IoT. This chapter can be seen as showing the suitability of symbolic AI applied to IoT. Chapter 8 then overviews the role of humans in crowdsourcing IoT data collection as well as data annotation and labeling for AI algorithms. Chapter 9 discusses security challenges in an IoT world and provides general guidelines for preventing undesired events. Chapter 10 considers distributed ledger technology, also known as blockchain, as a possible trust enabler in an IoT ecosystem. Chapter 11 reviews data privacy standards, regulations, and technologies that are relevant for particular types of IoT-generated data such as in the healthcare domain. The second part of the book comprises three chapters focused on the application of IoT technologies in selected application areas: healthcare, energy, and air quality and road transportation. Chapter 12 shows the important role of IoT data representation, interoperability, and privacy in integrated digital infrastructures for hospitals. Chapter 13 shows the application of IoT technology and the need for trust and for real-time processing systems in emerging smart grid energy systems. Finally, Chapter 14 discusses the role of IoT in optimizing road transportation for improved air quality. We conclude by discussing the future outlook for IoT and related technologies.

References 1 Althoff, T., Sosiˇc, R., Hicks, J.L. et al. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature 547 (7663): 336. 2 Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Image net classification with deep convolutional neural networks. Advances in Neural Information Processing Systems.

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2 Connecting Devices: Access Networks Paul Putland British Telecommunications plc, Ipswich, UK

2.1

Introduction

In this chapter, we discuss the set of network access technologies used for the Internet of Things (IoT). We describe the scope of “things” that can be connected and the wide range of requirements placed on the networks to support the many different types of device. We describe how existing networking technologies can support some of these devices and connectivity requirements, and then look in detail at some of the new network technologies and capabilities that are being developed specifically to support IoT. The connectivity of IoT devices is achieved through the access network – that part of the network which is concerned with moving data to and from a sensor or edge device into a backhaul network, often via a gateway. The number and complexity of devices that can be connected is enormous, covering a range of potential use cases and scenarios, and to understand the range of requirements for access networks, there is a need to understand the scope of the term “Thing.” Due to the wide range of devices and sensors that can be used in IoT solutions, it is not possible to connect all potential devices using a single networking technology. Instead, a range of technology and networking options are available to meet particular requirements – there is no single “best” solution for all scenarios. In order to understand the technical choices in any particular environment, it is useful to understand the key determining factors. We will examine the key requirements that determine the choice of network, briefly look at how a range of existing network capabilities can meet a large proportion of these needs, but then explore in more detail some of the newer networking technologies that are being developed specifically to support the uses that cases demand from the IoT. Traditional networks tend to fall into two main classes: voice and data. Voice services require low latency and data rates, whereas data-based communications can have a range of requirements on latency and data rates. IoT networks are inherently data networks, but their requirements vary from traditional multimedia communications for which current data networks have been principally designed. Furthermore, the scale of IoT in terms of the number of connected devices is significantly greater than that for human-operated devices

The Internet of Things: From Data to Insight, First Edition. Edited by John Davies and Carolina Fortuna. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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(such as phones) for which traditional networks were originally designed. When designing or selecting an appropriate connectivity technology for IoT, the key considerations are: • Data rate. How much data needs to be transmitted and how often? Data rate requirements can range from real-time video streaming from high-definition cameras, through to smart parking sensors, which may only transmit 10 times a day or less, with each message only being a small number of bits. • Power availability. In the video scenario above, the camera requires a continuous power feed, both for its own operation but also to support the network connection, whereas a smart parking sensor that has been buried in the road surface would have to run off a battery for several years. Although these are two examples at opposite ends of the power requirements spectrum, they demonstrate that access to power is crucial for highthroughput devices, whereas devices that are required to run off batteries for several years will have lower bandwidth and throughput capabilities, and thus require different networking technology to support this. • Range. Use cases vary from short distance, such as homes, offices, cars, and factories, through to long ranges for wider area city networks, deep in-building penetration, or rural use cases such as agriculture. • Cost. Low-cost modules and networks are required for several use cases in order to support the business models to make deployment and operation of devices and sensors commercially viable, so becomes a key factor in real-world deployments. Starting from the new type of data delivery infrastructure required by IoT and these four key considerations, we analyze well-established and traditional connectivity technologies as well as emerging IoT-specific technologies. We discuss and illustrate the design trade-offs with a focus on the emerging technologies dedicated to IoT requirements.

2.2 Overview of Access Networks Several existing network capabilities, covering both fixed and wireless technologies, support a number of the requirements for IoT networks. Short-range, energy-efficient, and typically a high data rate can be delivered through (wireless) personal area networks (WPAN), which include technologies such as Bluetooth and ZigBee. A mid-range high data rate can be delivered via a (wireless) local area network (WLAN), which typically means Wi-Fi, or by using Ethernet over a fixed network. Long-range, energy-efficient, and low data rates can be delivered by new low-power wide area networks (LP-WANs), while long range flexible data rates can be delivered through cellular networks, as depicted in Figure 2.1.

2.2.1 Existing Technologies Are Able to Cover a Number of IoT Scenarios Short- and mid-range wireless networks, such as Zigbee [1]/IEEE 802.15.4 [2], Bluetooth [3], and Wi-Fi [4]/IEEE 802.11 [5] are suitable for scenarios where the device is close to the receiving gateway, such as wearable devices, homes, offices, cars, and factories. Some short-range solutions, such as Zigbee, are also capable of meshing to form longer range networks, and have a master controller every so often that is linked back to other networks.

2.2 Overview of Access Networks

Battery Life

Data Rate

LPWAN

year

1 KB

month

1 MB W(PAN) W(LAN) 100 MB

CELLULAR

day

Range 1m

Figure 2.1

1 km

10 km

Range, battery life, and data rate for network technologies.

In both cases, once the data reaches a router/hub, then the data is transferred to a backhaul network. Mesh networks, some of them proprietary using closed protocols, can also be used for IoT applications, but because each node must continuously scan for incoming messages and then re-transmit to the next node, they are power hungry and can only be used where there is power available for each node. Some smart street lighting solutions use mesh networks, as there is power available at each lamp post, and lamp posts are reasonably close together. Cellular (2G/3G/4G) networks [6] standardized by the 3GPP [7] provide mid- to longrange capabilities. Initially they have been designed for voice communications, subsequently enabled for data communications, while the next generation (5G) of cellular networks are enabling very diverse application requirements, including those for IoT. Satellite communications [8] are increasingly being used to connect remote devices, such as monitoring in the polar regions or in other remote areas such as oceans, and are widely used for location services via GPS. Being rather expensive and battery consuming, such networks are typically used for critical or very special applications. Wired networks are also used for IoT. For instance, a CCTV camera can be considered to be an IoT device, in that data could be sent to a central platform and then accessed by applications and analytics software. Other examples where sensors are connected by fixed wires are in the retail environment, where sensors may track movements of people through a store and to measure dwell times. However, there are two areas where the network requirements cannot be delivered by existing technologies and have led to the creation of new technologies, specifically designed for the IoT, and the rest of this chapter will focus on these. The first of these is designed to support devices that support long-range, low data rate, low-power devices, while the second area is for very low latency/high-throughput scenarios, such as autonomous vehicles interacting with the environment, or for human/machine interaction, such as remote surgery.

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2.3 Low-Power Wide Area Network (LPWAN) As the name suggests, Low-Power Wide Area Network (LPWAN) technologies [9, 10] are required for devices that need long-range and low-power consumption in the end device, although they are also associated with low data rates, and use cases where there are a large number of devices, such as dense deployments in cities or buildings. Technologies such as Bluetooth, Wi-Fi, and Zigbee do not provide the range required, and standard cellular machine to machine (M2M) networks are too expensive for many of the use cases and consume too much power to allow battery operation for multiple years. There are several competing technologies in the LPWAN space and we will quickly look at some of the key choices to be made. The first criteria is the choice of spectrum frequency, whether to use licensed or unlicensed spectrum, and how to use it. Licensed spectrum is owned by mobile operators, and the technology is very heavily standards based. New standards can take a significant time to appear, so while new standards to support LPWANs were being developed by the relevant standards bodies for the mobile operators, the initial forays into IoT networks were in the unlicensed spectrum, which also has the cost advantage of being free. The range of a wireless network is predominantly driven by two factors: the output power of the transmitter and the frequency of the radio spectrum used. For a fixed output power, lower frequencies travel further, so in general LPWANs tend to operate at lower frequencies, typically less than 1 GHz. For comparison, Wi-Fi operates at 2.4 and 5 GHz. From a commercial perspective, one of the key considerations to enable ecosystems to grow and then for vendors to sell equipment into scale markets is access to spectrum across large geographic areas in designated frequency bands. On a global level, such a band is available at 2.4 GHz, and some companies, such as Ingenu [11], use this band, but most solutions are in bands that are less than 1 GHz to assist long-range coverage. In the EU, there is a band available at 868 MHz – the so-called ISM (Industrial, Scientific, Medicine) band – and in the USA, there is a similar band at 915 MHz. Several competing technologies [12] have been developed to take advantage of these spectrum bands, such as Sigfox [12], LoRaWAN [13], and Weightless [14]. Although this spectrum is unlicensed, it is still bound by rules and any system must operate under conditions set by the local regulator. Since it is a shared spectrum, any single device cannot have exclusive access to the network, and the technologies use various approaches to enable fair use of the spectrum. Some protocols use a “listen then talk” approach to avoid collisions, but this has an impact on battery life, while others just transmit and wait for an acknowledgment, and if no acknowledgment is received, the device then resends according to pre-set algorithms. There are also restrictions set by local regulators, typically covering duty cycle and output power. In the UK, these are governed by Ofcom, and include conditions that limit the maximum output power (14 dBm) and duty cycle (the amount of time a device can transmit) to 1% for the 868 MHz band, although there are variations based on exact frequency used and whether the device is a gateway or sensor. The impact of these rules, plus the fact that it is a shared spectrum, limit the use of unlicensed spectrum to those use cases where absolute quality of service is not essential, due to the limits on the number of messages that can be transmitted and the fact that some messages may not get delivered due to collisions or interference. For example, in a large

2.3 Low-Power Wide Area Network (LPWAN)

car park with perhaps 500 spaces, missing the arrival or departure of one or two cars a day does not invalidate the overall service. If the quality of service is a key requirement for a particular use case, then using a licensed spectrum is the preferred option. In terms of power usage, there are several options to conserve battery life. Powering the radio transmitter to send data is the main energy drain, so one way to conserve power is to use an asynchronous protocol, in which the end device does not need to constantly interact with the network so can spend longer periods asleep with the radio powered down. Long range (LoRa) [12, 13] is a technology that uses this approach. Another key factor is the transmit current used to power the radio. Cellular modulation [6, 15] needs a linear transmitter for the modulation, and these require significantly more power than non-linear modulation systems, such as LoRa. The next criterion is the approach taken to maximize range, given the constraints on power output and choice of spectrum. In general, this is achieved by lowering the data rate. Link budget is a function of the propagation loss and receiver sensitivity, and LPWAN technologies generally have higher receiver sensitivities of around −130 dBm compared with the −90 to −110 dBm for existing cellular technologies. Since decibels are a logarithmic scale, this difference equates to being able to detect a signal ∼ 10 000 times weaker. To achieve high receiver sensitivity, you need a slow modulation rate, as Information Theory [16] states that the energy per bit is the main factor in the probability of a message being received. If you slow the modulation rate by half, at the same output power you are essentially putting twice as much energy into each symbol, thus doubling the receiver sensitivity (increase of 3 dB). It is for this reason that LPWANs have very low data rates compared to normal cellular systems, since receiver sensitivity is a critical factor to obtain a long range. There are also differences in the use of the actual spectrum, such as using an ultra-narrow band or spread spectrum. Ultra-narrow Band (UNB) [17] systems transmit the signal in a small amount of spectrum, usually less than 1 kHz, and are particularly suited for small amounts of data. The link budget increases with the Power Spectral Density (PSD), and in a UNB system the PSD is increased due to the transmit power only using a small amount of spectrum. There is a challenge with UNB systems if the end device is moving fast, as the Doppler Effect can induce frequency variations that are relatively large compared to the signal bandwidth, which makes detection and demodulation of the signal more difficult. Weightless and Sigfox are examples of UNB systems. Spread spectrum [18, 19] is a technique in which the signal is transmitted on a bandwidth that is much bigger than the original frequency, typically using ∼125 kHz of spectrum. The primary reasons for this are to decrease interference and increase security. The original signal is only detectable when all the signals across all the bandwidths have been processed, which both increases security and leads to processing gain. Processing gain is defined as the ratio of the bandwidth used to send the signal to the original signal bandwidth frequency. In some LPWAN systems it is possible to have signal to noise ratios that are negative, meaning that the signal is below the noise floor, but the processing gain at the receiver means that the signal can be detected if you are explicitly looking for it. LoRa is an example of a system that uses a chirp [20] spread spectrum. The amount of spectrum used also affects noise. Noise is randomly spread throughout any spectrum used, which gives an advantage to UNB systems as the noise is effectively lower than it is for wider band technologies and these UNB systems operate on positive

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signal to noise ratios. However, the higher noise floor associated with spread spectrum systems that use larger amounts of spectrum are offset by the processing gain and can work with negative signal to noise ratios. Spread spectrum systems are less efficient in terms of the use of spectrum as they need more bandwidth to transmit the signal, which may cause issues when the goal is to maximize the amount of data sent in any given spectrum. Systems using licensed spectrum, where the cost of the spectrum may run into hundreds or thousands of million pounds, are optimized for spectrum utilization, which has cost and power implications on the end device. By comparison, some LPWAN systems using unlicensed spectrum have optimized the end device for lowest cost and longer battery life, at the expense of spectrum utilization. There are several LPWAN networking technologies; some of the key ones are discussed in the following subsections.

2.3.1 Long-Range (LoRa) Low-Power Wide Area Network LoRaWAN [12, 13] is based on the LoRa protocol from Semtech [21], which operates at the physical layer. LoRaWAN covers the higher-level protocols that enable interoperation between different vendors of device modems, gateways, and Network and Application Servers, and is specified by the LoRa Alliance [16], which is a non-profit association set up by various organizations to standardize and drive the global adoption of LoRaWAN. LoRa itself is a spread spectrum technology, using chirped FM modulation. LoRaWAN supports different spreading factors and different bandwidths depending on requirements and coverage. Higher spreading factors provide the largest range (best receiver sensitivity), but at the cost of lower data rates and longer time on air (lower battery life). There are also different programmable bandwidths available in the specification, the trade-off in this case is that a narrower bandwidth has increased sensitivity, but needs a longer time on air to transmit the data, and hence reduces battery life. LoRaWAN has three device classes, named A, B, and C. C is for devices that have continuous power, but most devices are class A, which are battery powered. LoRaWAN is an asynchronous protocol, meaning the end devices control when they send data and when they can sleep, providing extended battery life over other technologies that are synchronous and where the device regularly has to connect to the network even when no data are being sent.

2.3.2 Sigfox Low-Power Wide Area Network Sigfox [12, 13] is another LPWAN solution, in this case named after the company that owns the technology. Sigfox takes an approach that is based on using a narrowband (200 kHz) spectrum channel, which provides long range and low noise, but only at a very low data rate of 100 bits per second. As such, Sigfox is particularly suited to very low data messages, as the uplink message is limited to a payload of 12 bytes and the device will be on air for an average of two seconds to transmit an entire frame (26 bytes in total). Sigfox uses a mixture of DBPSK and GFSK modulation, and each message is 100 Hz wide. Sigfox relies on a frequency hopping algorithm for message delivery, as each device sends each message three times on three different frequencies.

2.4 Cellular Technologies LoRa

Sigfox

Weightless

Ingenu

12.5 KHz

1 MHz

100 b/s

100 Kb/s

150 kb/s

1 to 4 km

1 to 6 km

1 to 4 km

1 to 3 km

Up to 15 km

Up to 30 km

Up to 15 km

Up to 10 km

Bandwidth

Various, between 125 KHz 200 KHz and 500 KHz

Approximate peak uplink rates

50 kb/s

Approximate Range

Rural Urban

Figure 2.2

2.3.3

Illustrative performance data for unlicensed LPWAN technologies.

Weightless Low-Power Wide Area Network

Another LPWAN technology is Weightless [15] and actually consists of three protocols. The original is Weightless-W, which leveraged TV Whitespace, which was developed by a company called Neul. That was then supplemented with Weightless-N, an unlicensed spectrum narrowband protocol based on technology from NWave. Work has also started on a variation called Weightless-P, based on technology donated by M2COMM. It is also worth mentioning Ingenu as another provider in this space. Unlike others, it operates in the 2.4 GHz band so can operate globally without any changes on a regional basis, unlike the other companies mentioned. Some of the characteristics of the main LPWAN technologies operating in unlicensed spectrum are summarized in the table in Figure 2.2.

2.4

Cellular Technologies

The other main network technologies for IoT are offered by from mobile network operators using a licensed spectrum and are based on industry-wide standards (3GPP [7]/GSMA [22]). There are several standards that can be used to support IoT devices: • Extended Coverage – GSM (EC-GSM). This is an IoT-optimized GSM network, which can be deployed in existing GSM networks. This is deployed via a software upgrade within existing 2G, 3G, and 4G networks, but it is expected that other technologies, such as those discussed in the remainder of this section, will dominate in the majority of geographical areas. • Narrow Band Internet of Things (NB-IoT, also called LTE-M2). NB-IoT is based on a subset of the LTE standards, using OFDM modulation for the downlink and SC-FDMA for uplink. NB-IoT is designed to lower the power consumption of devices, and increase system capacity, spectrum efficiency, and range, having a 20-dB improvement in link budget versus GSM. As the name suggests, the technology works in very narrow spectrum bands (200 kHz), and co-exists with 2G, 3G, and 4G mobile networks by operating in guard bands or dedicated spectrum. It draws upon the features of mobile networks, such as security, authentication networks, data integrity, and mobile equipment identification. NB-IoT is a synchronous protocol, with devices regularly synchronizing to the cellular network. This regular connection consumes power and so reduces battery life, so while still offering very long battery life is not quite as battery efficient as asynchronous

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NB-loT (also called Cat-NB1)

Cat-M1 (also called LTE-M or enhanced Machine Type Communications (eMTC)

Deployment options

In-band GSM

LTE in band or guard band

In-band LTE

Bandwidth

200 MHz

180 MHz

1.4 MHz

Uplink

20 Kb/s

20 Kb/s

375 Kb/s

Downlink

20 Kb/s

20 Kb/s

300 Kb/s

Approximate Peak rates

Figure 2.3

Illustrative performance data for licensed LPWAN technologies.

protocols such as LoRa. However, NB-IoT does have advantages over LoRa for use cases where low latency or higher data throughput are key requirements. • LTE-M (also called LTE-M1 or CAT-M1). This is essentially the second generation of LTE chips built for IoT applications (after Cat-0) and has higher throughput and lower battery life than NB-IoT, but the chipset is likely to cost more. The maximum system bandwidth is 1.4 MHz (as opposed to Cat-0’s 20 MHz) and has specific use cases for LPWAN applications where there is a need for higher bandwidth and/or the ability to support voice calls. An example of this may be in a lift, where there is a requirement to move from a data link for the alarm and then a requirement to support a voice call. LT-M is compatible with existing LTE networks, meaning rollout is mainly software upgrades, so relatively cheap (Figure 2.3).

2.4.1 Emerging 5G Cellular Technology The other new technology that is starting to appear is 5G,1 a term that encompasses the next (fifth) generation of cellular networks. There are several technical advances occurring to enable 5G networks: • The security module evolving to be an embedded SIM (eSIM), which is a SIM card directly connected (soldered) to a circuit board in a device during manufacture. For devices where there is no need to replace the SIM card, this is cheaper than installing a SIM card later, and improves reliability by avoiding the need for a connector and improves security as the eSIM can be provisioned remotely and the mobile operator changed without the need to physically change a SIM card. • The need for additional frequency bands to support higher bandwidths. • Migration from dedicated networking hardware to Network Function Virtualization (NFV), which means network capability can increasingly be delivered by software running on generic hardware • Placing some functionality closer to the edge of the network, to reduce latency for delay-critical services. The ambition for 5G is that by using these new technologies, 5G networks will be able to be rapidly deployed or modified to support a range of different use cases, since the key 1 http://www.3gpp.org/release-15.

2.4 Cellular Technologies

features of the network will be able to be configured by changing software on general purpose servers, rather than using dedicated hardware. An example of this is network slicing, in which virtualization technologies are used to create logical network slices to support multiple different requirements without the need to change the underlying physical infrastructure – in effect, creating “multiple networks” from one physical deployment. These different network slices can then be dedicated to support use cases that have different Service Level Agreement (SLA), latency, security, or reliability requirements to other services running over the same common physical infrastructure. Some of the key use cases enabled by 5G networks are: • Enhanced Mobile Broadband (eMBB). Provision of higher data rates to mobile devices to support uses such as ultra-high definition video, virtual reality, interactive gaming. • Ultra-reliable Low-Latency Communications (URLLC). This is for mission critical use cases where quality of service and low latency are essential, such as industrial automation, remote surgery, traffic safety and control, and autonomous vehicles. • Massive Machine Type Communications (mMTC). This is to support the huge numbers of devices that will be connected in the future, including smart homes and buildings, smart agriculture, and asset tracking. From an IoT perspective, the latter two are of interest. URLLC use cases include those where very low network latency (