Machine learning approaches for convergence of IoT and blockchain / edited by Krishna Kant Singh, Akansha Singh, Sanjay K. Sharma.

Format:

Book

Contributor:

Singh, Akansha, editor.

Singh, Krishna Kant, editor.

Sharma, Sanjay, Dr., editor.

Language:

English

Subjects (All):

Machine learning.

Internet of things.

Blockchains (Databases).

Physical Description:

1 online resource (256 pages)

Place of Publication:

Hoboken, New Jersey : Wiley-Scrivener, [2021]

Summary:

MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAIN 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.

Contents:

Cover

Half-Title Page

Series Page

Title Page

Copyright Page

Contents

Preface

1 Blockchain and Internet of Things Across Industries

1.1 Introduction

1.2 Insight About Industry

1.2.1 Agriculture Industry

1.2.2 Manufacturing Industry

1.2.3 Food Production Industry

1.2.4 Healthcare Industry

1.2.5 Military

1.2.6 IT Industry

1.3 What is Blockchain?

1.4 What is IoT?

1.5 Combining IoT and Blockchain

1.5.1 Agriculture Industry

1.5.2 Manufacturing Industry

1.5.3 Food Processing Industry

1.5.4 Healthcare Industry

1.5.5 Military

1.5.6 Information Technology Industry

1.6 Observing Economic Growth and Technology's Impact

1.7 Applications of IoT and Blockchain Beyond Industries

1.8 Conclusion

References

2 Layered Safety Model for IoT Services Through Blockchain

2.1 Introduction

2.1.1 IoT Factors Impacting Security

2.2 IoT Applications

2.3 IoT Model With Communication Parameters

2.3.1 RFID (Radio Frequency Identification)

2.3.2 WSH (Wireless Sensor Network)

2.3.3 Middleware (Software and Hardware)

2.3.4 Computing Service (Cloud)

2.3.5 IoT Software

2.4 Security and Privacy in IoT Services

2.5 Blockchain Usages in IoT

2.6 Blockchain Model With Cryptography

2.6.1 Variations of Blockchain

2.7 Solution to IoT Through Blockchain

2.8 Conclusion

3 Internet of Things Security Using AI and Blockchain

3.1 Introduction

3.2 IoT and Its Application

3.3 Most Popular IoT and Their Uses

3.4 Use of IoT in Security

3.5 What is AI?

3.6 Applications of AI

3.7 AI and Security

3.8 Advantages of AI

3.9 Timeline of Blockchain

3.10 Types of Blockchain

3.11 Working of Blockchain

3.12 Advantages of Blockchain Technology

3.13 Using Blockchain Technology With IoT

3.14 IoT Security Using AI and Blockchain.

3.15 AI Integrated IoT Home Monitoring System

3.16 Smart Homes With the Concept of Blockchain and AI

3.17 Smart Sensors

3.18 Authentication Using Blockchain

3.19 Banking Transactions Using Blockchain

3.20 Security Camera

3.21 Other Ways to Fight Cyber Attacks

3.22 Statistics on Cyber Attacks

3.23 Conclusion

4 Amalgamation of IoT, ML, and Blockchain in the Healthcare Regime

4.1 Introduction

4.2 What is Internet of Things?

4.2.1 Internet of Medical Things

4.2.2 Challenges of the IoMT

4.2.3 Use of IoT in Alzheimer Disease

4.3 Machine Learning

4.3.1 Case 1: Multilayer Perceptron Network

4.3.2 Case 2: Vector Support Machine

4.3.3 Applications of the Deep Learning in the Healthcare Sector

4.4 Role of the Blockchain in the Healthcare Field

4.4.1 What is Blockchain Technology?

4.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain

4.5 Conclusion

5 Application of Machine Learning and IoT for Smart Cities

5.1 Functionality of Image Analytics

5.2 Issues Related to Security and Privacy in IoT

5.3 Machine Learning Algorithms and Blockchain Methodologies

5.3.1 Intrusion Detection System

5.3.2 Deep Learning and Machine Learning Models

5.3.3 Artificial Neural Networks

5.3.4 Hybrid Approaches

5.3.5 Review and Taxonomy of Machine Learning

5.4 Machine Learning Open Source Tools for Big Data

5.5 Approaches and Challenges of Machine Learning Algorithms in Big Data

5.6 Conclusion

6 Machine Learning Applications for IoT Healthcare

6.1 Introduction

6.2 Machine Learning

6.2.1 Types of Machine Learning Techniques

6.2.2 Applications of Machine Learning

6.3 IoT in Healthcare

6.3.1 IoT Architecture for Healthcare System

6.4 Machine Learning and IoT.

6.4.1 Application of ML and IoT in Healthcare

6.5 Conclusion

7 Blockchain for Vehicular Ad Hoc Network and Intelligent Transportation System: A Comprehensive Study

7.1 Introduction

7.2 Related Work

7.3 Connected Vehicles and Intelligent Transportation System

7.3.1 VANET

7.3.2 Blockchain Technology and VANET

7.4 An ITS-Oriented Blockchain Model

7.5 Need of Blockchain

7.5.1 Food Track and Trace

7.5.2 Electric Vehicle Recharging

7.5.3 Smart City and Smart Vehicles

7.6 Implementation of Blockchain Supported Intelligent Vehicles

7.7 Conclusion

7.8 Future Scope

8 Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

8.1 Introduction

8.2 Pre-Processing

8.2.1 Principle of Diffusion Filtering

8.3 Improved FCM Based on Crow Search Optimization

8.4 Prediction-Based Lossless Compression Model

8.5 Results and Discussion

8.6 Conclusion

Acknowledgment

9 Innovative Ideas to Build Smart Cities with the Help of Machine and Deep Learning and IoT

9.1 Introduction

9.2 Related Work

9.3 What Makes Smart Cities Smart?

9.3.1 Intense Traffic Management

9.3.2 Smart Parking

9.3.3 Smart Waste Administration

9.3.4 Smart Policing

9.3.5 Shrewd Lighting

9.3.6 Smart Power

9.4 In Healthcare System

9.5 In Homes

9.6 In Aviation

9.7 In Solving Social Problems

9.8 Uses of AI-People

9.8.1 Google Maps

9.8.2 Ridesharing

9.8.3 Voice-to-Text

9.8.4 Individual Assistant

9.9 Difficulties and Profit

9.10 Innovations in Smart Cities

9.11 Beyond Humans Focus

9.12 Illustrative Arrangement

9.13 Smart Cities with No Differentiation

9.14 Smart City and AI

9.15 Further Associated Technologies

9.15.1 Model Identification

9.15.2 Picture Recognition.

9.15.3 IoT

9.15.4 Big Data

9.15.5 Deep Learning

9.16 Challenges and Issues

9.16.1 Profound Learning Models

9.16.2 Deep Learning Paradigms

9.16.3 Confidentiality

9.16.4 Information Synthesis

9.16.5 Distributed Intelligence

9.16.6 Restrictions of Deep Learning

9.17 Conclusion and Future Scope

Index

EULA.

Notes:

Description based on print version record.

ISBN:

9781119761877

1119761875

9781119761884

1119761883

9781119761808

1119761808

OCLC:

1263027374