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MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAINThe 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 IoTAudienceThe 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.

Machine Learning Approaches for Convergence of IoT and Blockchain

Preface xi1 Blockchain and Internet of Things Across Industries 1Ananya Rakhra, Raghav Gupta and Akansha Singh1.1 Introduction 11.2 Insight About Industry 31.2.1 Agriculture Industry 51.2.2 Manufacturing Industry 51.2.3 Food Production Industry 61.2.4 Healthcare Industry 71.2.5 Military 71.2.6 IT Industry 81.3 What is Blockchain? 81.4 What is IoT? 111.5 Combining IoT and Blockchain 141.5.1 Agriculture Industry 151.5.2 Manufacturing Industry 171.5.3 Food Processing Industry 181.5.4 Healthcare Industry 201.5.5 Military 211.5.6 Information Technology Industry 241.6 Observing Economic Growth and Technology's Impact 251.7 Applications of IoT and Blockchain Beyond Industries 281.8 Conclusion 32References 332 Layered Safety Model for IoT Services Through Blockchain 35Anju Malik and Bharti Sharma2.1 Introduction 362.1.1 IoT Factors Impacting Security 382.2 IoT Applications 392.3 IoT Model With Communication Parameters 402.3.1 RFID (Radio Frequency Identification) 402.3.2 WSH (Wireless Sensor Network) 402.3.3 Middleware (Software and Hardware) 402.3.4 Computing Service (Cloud) 412.3.5 IoT Software 412.4 Security and Privacy in IoT Services 412.5 Blockchain Usages in IoT 442.6 Blockchain Model With Cryptography 442.6.1 Variations of Blockchain 452.7 Solution to IoT Through Blockchain 462.8 Conclusion 50References 513 Internet of Things Security Using AI and Blockchain 57Raghav Gupta, Ananya Rakhra and Akansha Singh3.1 Introduction 583.2 IoT and Its Application 593.3 Most Popular IoT and Their Uses 613.4 Use of IoT in Security 633.5 What is AI? 643.6 Applications of AI 653.7 AI and Security 663.8 Advantages of AI 683.9 Timeline of Blockchain 693.10 Types of Blockchain 703.11 Working of Blockchain 723.12 Advantages of Blockchain Technology 743.13 Using Blockchain Technology With IoT 743.14 IoT Security Using AI and Blockchain 763.15 AI Integrated IoT Home Monitoring System 783.16 Smart Homes With the Concept of Blockchain and AI 793.17 Smart Sensors 813.18 Authentication Using Blockchain 823.19 Banking Transactions Using Blockchain 833.20 Security Camera 843.21 Other Ways to Fight Cyber Attacks 853.22 Statistics on Cyber Attacks 883.23 Conclusion 90References 904 Amalgamation of IoT, ML, and Blockchain in the Healthcare Regime 93Pratik Kumar, Piyush Yadav, Rajeev Agrawal and Krishna Kant Singh4.1 Introduction 934.2 What is Internet of Things? 954.2.1 Internet of Medical Things 974.2.2 Challenges of the IoMT 974.2.3 Use of IoT in Alzheimer Disease 994.3 Machine Learning 1004.3.1 Case 1: Multilayer Perceptron Network 1014.3.2 Case 2: Vector Support Machine 1024.3.3 Applications of the Deep Learning in the Healthcare Sector 1034.4 Role of the Blockchain in the Healthcare Field 1044.4.1 What is Blockchain Technology? 1044.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain 1054.5 Conclusion 106References 1065 Application of Machine Learning and IoT for Smart Cities 109Nilanjana Pradhan, Ajay Shankar Singh, Shrddha Sagar, Akansha Singh and Ahmed A. Elngar5.1 Functionality of Image Analytics 1105.2 Issues Related to Security and Privacy in IoT 1125.3 Machine Learning Algorithms and Blockchain Methodologies 1145.3.1 Intrusion Detection System 1165.3.2 Deep Learning and Machine Learning Models 1185.3.3 Artificial Neural Networks 1185.3.4 Hybrid Approaches 1195.3.5 Review and Taxonomy of Machine Learning 1205.4 Machine Learning Open Source Tools for Big Data 1215.5 Approaches and Challenges of Machine Learning Algorithms in Big Data 1235.6 Conclusion 127References 1276 Machine Learning Applications for IoT Healthcare 129Neha Agarwal, Pushpa Singh, Narendra Singh, Krishna Kant Singh and Rohit Jain6.1 Introduction 1306.2 Machine Learning 1306.2.1 Types of Machine Learning Techniques 1316.2.1.1 Unsupervised Learning 1316.2.1.2 Supervised Learning 1316.2.1.3 Semi-Supervised Learning 1326.2.1.4 Reinforcement Learning 1326.2.2 Applications of Machine Learning 1326.2.2.1 Prognosis 1326.2.2.2 Diagnosis 1346.3 IoT in Healthcare 1356.3.1 IoT Architecture for Healthcare System 1356.3.1.1 Physical and Data Link Layer 1366.3.1.2 Network Layer 1376.3.1.3 Transport Layer 1376.3.1.4 Application Layer 1376.4 Machine Learning and IoT 1386.4.1 Application of ML and IoT in Healthcare 1386.4.1.1 Smart Diagnostic Care 1386.4.1.2 Medical Staff and Inventory Tracking 1396.4.1.3 Personal Care 1396.4.1.4 Healthcare Monitoring Device 1396.4.1.5 Chronic Disease Management 1396.5 Conclusion 140References 1407 Blockchain for Vehicular Ad Hoc Network and Intelligent Transportation System: A Comprehensive Study 145Raghav Sharma, Anirudhi Thanvi, Shatakshi Singh, Manish Kumar and Sunil Kumar Jangir7.1 Introduction 1467.2 Related Work 1497.3 Connected Vehicles and Intelligent Transportation System 1527.3.1 VANET 1537.3.2 Blockchain Technology and VANET 1537.4 An ITS-Oriented Blockchain Model 1557.5 Need of Blockchain 1567.5.1 Food Track and Trace 1597.5.2 Electric Vehicle Recharging 1607.5.3 Smart City and Smart Vehicles 1617.6 Implementation of Blockchain Supported Intelligent Vehicles 1647.7 Conclusion 1657.8 Future Scope 166References 1678 Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT 175S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balázs Gulyás and Ajay Kumar H.8.1 Introduction 1768.2 Pre-Processing 1788.2.1 Principle of Diffusion Filtering 1788.3 Improved FCM Based on Crow Search Optimization 1838.4 Prediction-Based Lossless Compression Model 1848.5 Results and Discussion 1888.6 Conclusion 202Acknowledgment 202References 2039 Innovative Ideas to Build Smart Cities with the Help of Machine and Deep Learning and IoT 205ShylajaVinaykumar Karatangi, Reshu Agarwal, Krishna Kant Singh and Ivan Izonin9.1 Introduction 2069.2 Related Work 2079.3 What Makes Smart Cities Smart? 2089.3.1 Intense Traffic Management 2089.3.2 Smart Parking 2099.3.3 Smart Waste Administration 2109.3.4 Smart Policing 2119.3.5 Shrewd Lighting 2119.3.6 Smart Power 2119.4 In Healthcare System 2129.5 In Homes 2139.6 In Aviation 2139.7 In Solving Social Problems 2139.8 Uses of AI-People 2149.8.1 Google Maps 2149.8.2 Ridesharing 2149.8.3 Voice-to-Text 2159.8.4 Individual Assistant 2159.9 Difficulties and Profit 2159.10 Innovations in Smart Cities 2169.11 Beyond Humans Focus 2179.12 Illustrative Arrangement 2179.13 Smart Cities with No Differentiation 2189.14 Smart City and AI 2199.15 Further Associated Technologies 2219.15.1 Model Identification 2219.15.2 Picture Recognition 2219.15.3 IoT 2229.15.4 Big Data 2239.15.5 Deep Learning 2239.16 Challenges and Issues 2249.16.1 Profound Learning Models 2249.16.2 Deep Learning Paradigms 2259.16.3 Confidentiality 2269.16.4 Information Synthesis 2269.16.5 Distributed Intelligence 2279.16.6 Restrictions of Deep Learning 2289.17 Conclusion and Future Scope 228References 229Index 233