The Industrial Internet of Things (IIoT) Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Advances in Learning Analytics for Intelligent Cloud-IoT Systems Series Editors: Dr. Souvik Pal and Dr. Dac-Nhuong Le Scope: The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of Cloud-IoT Systems is becoming increasingly essential and intertwined. The capability of an intelligent system depends on various self-decision making algorithms in IoT Devices. IoT based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of Analytics Reasoning and Sense-making in Big Data, which is centered in the Cloud and IoT-enabled environments. The series seeks volumes that are empirical studies, theoretical and numerical analysis, and novel research findings. The series encourages cross-fertilization of highlighting research and knowledge of Data Analytics, Machine Learning, Data Science, and IoT sustainable developments. Please send proposals to: Dr. Souvik Pal Department of Computer Science & Engineering Sister Nivedita University (Techno India Group) Kolkata, India [email protected] Dr. Dac-Nhuong Le Faculty of Information Technology, Haiphong University, Haiphong, Vietnam [email protected] Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected]) The Industrial Internet of Things (IIoT) Intelligent Analytics for Predictive Maintenance Edited by R. Anandan Suseendran Gopalakrishnan Souvik Pal and Noor Zaman This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. 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 other- wise, 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. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley prod- ucts visit us at www.wiley.com. 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Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-76877-7 Cover image: Pixabay.Com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1 Contents Preface xvii 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1 Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes 1.1 Introduction 2 1.2 Relationship Between Artificial Intelligence and IoT 5 1.2.1 AI Concept 6 1.2.2 IoT Concept 10 1.3 IoT Ecosystem 15 1.3.1 Industry 4.0 Concept 18 1.3.2 Industrial Internet of Things 19 1.4 Discussion 21 1.5 Trends 23 1.6 Conclusions 24 References 26 2 Analysis on Security in IoT Devices—An Overview 31 T. Nalini and T. Murali Krishna 2.1 Introduction 32 2.2 Security Properties 33 2.3 Security Challenges of IoT 34 2.3.1 Classification of Security Levels 35 2.3.1.1 At Information Level 36 2.3.1.2 At Access Level 36 2.3.1.3 At Functional Level 36 2.3.2 Classification of IoT Layered Architecture 37 2.3.2.1 Edge Layer 37 2.3.2.2 Access Layer 37 v vi Contents 2.3.2.3 Application Layer 37 2.4 IoT Security Threats 38 2.4.1 Physical Device Threats 39 2.4.1.1 Device-Threats 39 2.4.1.2 Resource Led Constraints 39 2.4.2 Network-Oriented Communication Assaults 39 2.4.2.1 Structure 40 2.4.2.2 Protocol 40 2.4.3 Data-Based Threats 41 2.4.3.1 Confidentiality 41 2.4.3.2 Availability 41 2.4.3.3 Integrity 42 2.5 Assaults in IoT Devices 43 2.5.1 Devices of IoT 43 2.5.2 Gateways and Networking Devices 44 2.5.3 Cloud Servers and Control Devices 45 2.6 Security Analysis of IoT Platforms 46 2.6.1 ARTIK 46 2.6.2 GiGA IoT Makers 47 2.6.3 AWS IoT 47 2.6.4 Azure IoT 47 2.6.5 Google Cloud IoT (GC IoT) 48 2.7 Future Research Approaches 49 2.7.1 Blockchain Technology 51 2.7.2 5G Technology 52 2.7.3 Fog Computing (FC) and Edge Computing (EC) 52 References 54 3 Smart Automation, Smart Energy, and Grid Management Challenges 59 J. Gayathri Monicka and C. Amuthadevi 3.1 Introduction 60 3.2 Internet of Things and Smart Grids 62 3.2.1 Smart Grid in IoT 63 3.2.2 IoT Application 64 3.2.3 Trials and Imminent Investigation Guidelines 66 3.3 Conceptual Model of Smart Grid 67 3.4 Building Computerization 71 3.4.1 Smart Lighting 73 3.4.2 Smart Parking 73 3.4.3 Smart Buildings 74 Contents vii 3.4.4 Smart Grid 75 3.4.5 Integration IoT in SG 77 3.5 Challenges and Solutions 81 3.6 Conclusions 83 References 83 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89 C. Amuthadevi and J. Gayathri Monicka 4.1 Introduction 89 4.1.1 Fundamental Terms in IIoT 91 4.1.1.1 Cloud Computing 92 4.1.1.2 Big Data Analytics 92 4.1.1.3 Fog/Edge Computing 92 4.1.1.4 Internet of Things 93 4.1.1.5 Cyber-Physical-System 94 4.1.1.6 Artificial Intelligence 95 4.1.1.7 Machine Learning 95 4.1.1.8 Machine-to-Machine Communication 99 4.1.2 Intelligent Analytics 99 4.1.3 Predictive Maintenance 100 4.1.4 Disaster Predication and Safety Management 101 4.1.4.1 Natural Disasters 101 4.1.4.2 Disaster Lifecycle 102 4.1.4.3 Disaster Predication 103 4.1.4.4 Safety Management 104 4.1.5 Optimization 105 4.2 Existing Technology and Its Review 106 4.2.1 Survey on Predictive Analysis in Natural Disasters 106 4.2.2 Survey on Safety Management and Recovery 108 4.2.3 Survey on Optimizing Solutions in Natural Disasters 109 4.3 Research Limitation 110 4.3.1 Forward-Looking Strategic Vision (FVS) 110 4.3.2 Availability of Data 111 4.3.3 Load Balancing 111 4.3.4 Energy Saving and Optimization 111 4.3.5 Cost Benefit Analysis 112 4.3.6 Misguidance of Analysis 112 4.4 Finding 113 4.4.1 Data Driven Reasoning 113 4.4.2 Cognitive Ability 113 4.4.3 Edge Intelligence 113 viii Contents 4.4.4 Effect of ML Algorithms and Optimization 114 4.4.5 Security 114 4.5 Conclusion and Future Research 114 4.5.1 Conclusion 114 4.5.2 Future Research 114 References 115 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119 Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar 5.1 Introduction 120 5.2 Fuzzy Logic 121 5.2.1 Fuzzy Sets 121 5.2.2 Fuzzy Logic Basics 122 5.2.3 Fuzzy Logic and Power System 122 5.2.4 Fuzzy Logic—Automatic Generation Control 123 5.2.5 Fuzzy Microgrid Wind 123 5.3 Genetic Algorithm 123 5.3.1 Important Aspects of Genetic Algorithm 124 5.3.2 Standard Genetic Algorithm 126 5.3.3 Genetic Algorithm and Its Application 127 5.3.4 Power System and Genetic Algorithm 127 5.3.5 Economic Dispatch Using Genetic Algorithm 128 5.4 Artificial Neural Network 128 5.4.1 The Biological Neuron 129 5.4.2 A Formal Definition of Neural Network 130 5.4.3 Neural Network Models 131 5.4.4 Rosenblatt’s Perceptron 131 5.4.5 Feedforward and Recurrent Networks 132 5.4.6 Back Propagation Algorithm 133 5.4.7 Forward Propagation 133 5.4.8 Algorithm 134 5.4.9 Recurrent Network 135 5.4.10 Examples of Neural Networks 136 5.4.10.1 AND Operation 136 5.4.10.2 OR Operation 137 5.4.10.3 XOR Operation 137 5.4.11 Key Components of an Artificial Neuron Network 138 5.4.12 Neural Network Training 141 5.4.13 Training Types 142 5.4.13.1 Supervised Training 142 5.4.13.2 Unsupervised Training 142 Contents ix 5.4.14 Learning Rates 142 5.4.15 Learning Laws 143 5.4.16 Restructured Power System 144 5.4.17 Advantages of Precise Forecasting of the Price 145 5.5 Conclusion 145 References 146 6 Recent Advances in Wearable Antennas: A Survey 149 Harvinder Kaur and Paras Chawla 6.1 Introduction 150 6.2 Types of Antennas 153 6.2.1 Description of Wearable Antennas 153 6.2.1.1 Microstrip Patch Antenna 153 6.2.1.2 Substrate Integrated Waveguide Antenna 153 6.2.1.3 Planar Inverted-F Antenna 153 6.2.1.4 Monopole Antenna 153 6.2.1.5 Metasurface Loaded Antenna 154 6.3 Design of Wearable Antennas 154 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design 154 6.3.1.1 Conducting Coating on Substrate 154 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 157 6.3.1.3 Partial Ground Plane 158 6.3.2 Logo Antennas 159 6.3.3 Embroidered Antenna 159 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap 160 6.3.5 Wearable Reconfigurable Antenna 161 6.4 Textile Antennas 162 6.5 Comparison of Wearable Antenna Designs 168 6.6 Fractal Antennas 168 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 171 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 172 6.6.3 Double-Fractal Layer Wearable Antenna 172 6.6.4 Development of Embroidered Sierpinski Carpet Antenna 172 6.7 Future Challenges of Wearable Antenna Designs 174 6.8 Conclusion 174 References 175