Machine Learning Approaches for Convergence of IoT and Blockchain
Description
The unique aspect of this book is that its focus is the convergence of machine learning, IoT, and blockchain in a single publication.
Blockchain technology and the Internet of Things (IoT) are two of the most impactful trends to have emerged in the field of machine learning. Although there are a number of books available solely on the subjects of machine learning, IoT and blockchain technology, no such book has been available which focuses on machine learning techniques for IoT and blockchain convergence until now. Thus, this book is unique in terms of the topics it covers. Designed as an essential guide for all academicians, researchers, and those in industry who are working in related fields, this book will provide insights into the convergence of blockchain technology and the IoT with machine learning.
Highlights of the book include:
* Examines many industries such as agriculture, manufacturing, food production, healthcare, the military, and IT
* Security of the Internet of Things using blockchain and AI
* Developing smart cities and transportation systems using machine learning and IoT
Audience
The target audience of this book is professionals and researchers (artificial intelligence specialists, systems engineers, information technologists) in the fields of machine learning, IoT, and blockchain technology.
More details
Other editions
Additional editions
Persons
Krishna Kant Singh is an associate professor in the Faculty of Engineering & Technology, Jain (Deemed-to-be University), Bengaluru, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals.
Akansha Singh is an associate professor in the Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural networks and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes mobile computing, artificial intelligence, machine learning, digital image processing.
Sanjay Kumar Sharma PhD is professor and Head in the Department of Electronics and Communication Engineering at KIET Group of Institutions. Dr. Sanjay Sharma has a total of 24 years of teaching and research experience. He has more than 45 publications in journals and international conferences.
Content
Preface xi
1 Blockchain and Internet of Things Across Industries 1
Ananya Rakhra, Raghav Gupta and Akansha Singh
1.1 Introduction 1
1.2 Insight About Industry 3
1.2.1 Agriculture Industry 5
1.2.2 Manufacturing Industry 5
1.2.3 Food Production Industry 6
1.2.4 Healthcare Industry 7
1.2.5 Military 7
1.2.6 IT Industry 8
1.3 What is Blockchain? 8
1.4 What is IoT? 11
1.5 Combining IoT and Blockchain 14
1.5.1 Agriculture Industry 15
1.5.2 Manufacturing Industry 17
1.5.3 Food Processing Industry 18
1.5.4 Healthcare Industry 20
1.5.5 Military 21
1.5.6 Information Technology Industry 24
1.6 Observing Economic Growth and Technology's Impact 25
1.7 Applications of IoT and Blockchain Beyond Industries 28
1.8 Conclusion 32
References 33
2 Layered Safety Model for IoT Services Through Blockchain 35
Anju Malik and Bharti Sharma
2.1 Introduction 36
2.1.1 IoT Factors Impacting Security 38
2.2 IoT Applications 39
2.3 IoT Model With Communication Parameters 40
2.3.1 RFID (Radio Frequency Identification) 40
2.3.2 WSH (Wireless Sensor Network) 40
2.3.3 Middleware (Software and Hardware) 40
2.3.4 Computing Service (Cloud) 41
2.3.5 IoT Software 41
2.4 Security and Privacy in IoT Services 41
2.5 Blockchain Usages in IoT 44
2.6 Blockchain Model With Cryptography 44
2.6.1 Variations of Blockchain 45
2.7 Solution to IoT Through Blockchain 46
2.8 Conclusion 50
References 51
3 Internet of Things Security Using AI and Blockchain 57
Raghav Gupta, Ananya Rakhra and Akansha Singh
3.1 Introduction 58
3.2 IoT and Its Application 59
3.3 Most Popular IoT and Their Uses 61
3.4 Use of IoT in Security 63
3.5 What is AI? 64
3.6 Applications of AI 65
3.7 AI and Security 66
3.8 Advantages of AI 68
3.9 Timeline of Blockchain 69
3.10 Types of Blockchain 70
3.11 Working of Blockchain 72
3.12 Advantages of Blockchain Technology 74
3.13 Using Blockchain Technology With IoT 74
3.14 IoT Security Using AI and Blockchain 76
3.15 AI Integrated IoT Home Monitoring System 78
3.16 Smart Homes With the Concept of Blockchain and AI 79
3.17 Smart Sensors 81
3.18 Authentication Using Blockchain 82
3.19 Banking Transactions Using Blockchain 83
3.20 Security Camera 84
3.21 Other Ways to Fight Cyber Attacks 85
3.22 Statistics on Cyber Attacks 88
3.23 Conclusion 90
References 90
4 Amalgamation of IoT, ML, and Blockchain in the Healthcare Regime 93
Pratik Kumar, Piyush Yadav, Rajeev Agrawal and Krishna Kant Singh
4.1 Introduction 93
4.2 What is Internet of Things? 95
4.2.1 Internet of Medical Things 97
4.2.2 Challenges of the IoMT 97
4.2.3 Use of IoT in Alzheimer Disease 99
4.3 Machine Learning 100
4.3.1 Case 1: Multilayer Perceptron Network 101
4.3.2 Case 2: Vector Support Machine 102
4.3.3 Applications of the Deep Learning in the Healthcare Sector 103
4.4 Role of the Blockchain in the Healthcare Field 104
4.4.1 What is Blockchain Technology? 104
4.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain 105
4.5 Conclusion 106
References 106
5 Application of Machine Learning and IoT for Smart Cities 109
Nilanjana Pradhan, Ajay Shankar Singh, Shrddha Sagar, Akansha Singh and Ahmed A. Elngar
5.1 Functionality of Image Analytics 110
5.2 Issues Related to Security and Privacy in IoT 112
5.3 Machine Learning Algorithms and Blockchain Methodologies 114
5.3.1 Intrusion Detection System 116
5.3.2 Deep Learning and Machine Learning Models 118
5.3.3 Artificial Neural Networks 118
5.3.4 Hybrid Approaches 119
5.3.5 Review and Taxonomy of Machine Learning 120
5.4 Machine Learning Open Source Tools for Big Data 121
5.5 Approaches and Challenges of Machine Learning Algorithms in Big Data 123
5.6 Conclusion 127
References 127
6 Machine Learning Applications for IoT Healthcare 129
Neha Agarwal, Pushpa Singh, Narendra Singh, Krishna Kant Singh and Rohit Jain
6.1 Introduction 130
6.2 Machine Learning 130
6.2.1 Types of Machine Learning Techniques 131
6.2.1.1 Unsupervised Learning 131
6.2.1.2 Supervised Learning 131
6.2.1.3 Semi-Supervised Learning 132
6.2.1.4 Reinforcement Learning 132
6.2.2 Applications of Machine Learning 132
6.2.2.1 Prognosis 132
6.2.2.2 Diagnosis 134
6.3 IoT in Healthcare 135
6.3.1 IoT Architecture for Healthcare System 135
6.3.1.1 Physical and Data Link Layer 136
6.3.1.2 Network Layer 137
6.3.1.3 Transport Layer 137
6.3.1.4 Application Layer 137
6.4 Machine Learning and IoT 138
6.4.1 Application of ML and IoT in Healthcare 138
6.4.1.1 Smart Diagnostic Care 138
6.4.1.2 Medical Staff and Inventory Tracking 139
6.4.1.3 Personal Care 139
6.4.1.4 Healthcare Monitoring Device 139
6.4.1.5 Chronic Disease Management 139
6.5 Conclusion 140
References 140
7 Blockchain for Vehicular Ad Hoc Network and Intelligent Transportation System: A Comprehensive Study 145
Raghav Sharma, Anirudhi Thanvi, Shatakshi Singh, Manish Kumar and Sunil Kumar Jangir
7.1 Introduction 146
7.2 Related Work 149
7.3 Connected Vehicles and Intelligent Transportation System 152
7.3.1 VANET 153
7.3.2 Blockchain Technology and VANET 153
7.4 An ITS-Oriented Blockchain Model 155
7.5 Need of Blockchain 156
7.5.1 Food Track and Trace 159
7.5.2 Electric Vehicle Recharging 160
7.5.3 Smart City and Smart Vehicles 161
7.6 Implementation of Blockchain Supported Intelligent Vehicles 164
7.7 Conclusion 165
7.8 Future Scope 166
References 167
8 Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT 175
S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balázs Gulyás and Ajay Kumar H.
8.1 Introduction 176
8.2 Pre-Processing 178
8.2.1 Principle of Diffusion Filtering 178
8.3 Improved FCM Based on Crow Search Optimization 183
8.4 Prediction-Based Lossless Compression Model 184
8.5 Results and Discussion 188
8.6 Conclusion 202
Acknowledgment 202
References 203
9 Innovative Ideas to Build Smart Cities with the Help of Machine and Deep Learning and IoT 205
ShylajaVinaykumar Karatangi, Reshu Agarwal, Krishna Kant Singh and Ivan Izonin
9.1 Introduction 206
9.2 Related Work 207
9.3 What Makes Smart Cities Smart? 208
9.3.1 Intense Traffic Management 208
9.3.2 Smart Parking 209
9.3.3 Smart Waste Administration 210
9.3.4 Smart Policing 211
9.3.5 Shrewd Lighting 211
9.3.6 Smart Power 211
9.4 In Healthcare System 212
9.5 In Homes 213
9.6 In Aviation 213
9.7 In Solving Social Problems 213
9.8 Uses of AI-People 214
9.8.1 Google Maps 214
9.8.2 Ridesharing 214
9.8.3 Voice-to-Text 215
9.8.4 Individual Assistant 215
9.9 Difficulties and Profit 215
9.10 Innovations in Smart Cities 216
9.11 Beyond Humans Focus 217
9.12 Illustrative Arrangement 217
9.13 Smart Cities with No Differentiation 218
9.14 Smart City and AI 219
9.15 Further Associated Technologies 221
9.15.1 Model Identification 221
9.15.2 Picture Recognition 221
9.15.3 IoT 222
9.15.4 Big Data 223
9.15.5 Deep Learning 223
9.16 Challenges and Issues 224
9.16.1 Profound Learning Models 224
9.16.2 Deep Learning Paradigms 225
9.16.3 Confidentiality 226
9.16.4 Information Synthesis 226
9.16.5 Distributed Intelligence 227
9.16.6 Restrictions of Deep Learning 228
9.17 Conclusion and Future Scope 228
References 229
Index 233
