Convergence of Blockchain, AI, and IoT : Concepts and Challenges book cover
1st Edition

Convergence of Blockchain, AI, and IoT
Concepts and Challenges




ISBN 9780367532642
Published December 23, 2021 by CRC Press
206 Pages 71 B/W Illustrations

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Book Description

Convergence of Blockchain, AI, and IoT: Concepts and Challenges discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy.

The book provides an overview of the clinical scientific research enabling smart diagnosis equipment through AI. It presents the role these technologies play in augmented reality and blockchain, covers digital currency managed with bitcoin, and discusses deep learning and how it can enhance human thoughts and behaviors.

Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

Table of Contents

Chapter 1. Affiliation of Blockchain, IoT and Big Data: Demystified. Chapter 2. A Decentralized Privacy-Preserving Blockchain for IoT and Big Data in Health Care Applications. Chapter 3.Amalgamation of Blockchain, IoT, and Big Data by Using Distributed Hyperledger Framework. Chapter 4. Blockchain and Big Data for Decentralized Management of IoT-Driven Healthcare Devices. Chapter 5.Visualizing Process Data with Reliability Data in Era of Big Data and I(4.0). Chapter 6. Tracking or Maintenance: Tracking IoT-Based Devices Parts from Manufacture to End of Life by Leveraging Big Data in Blockchain Environment. Chapter 7. Jaya Process Optimization in IoT Applications. Chapter 8. Data Security Essentials for the Convergence of Blockchain, AI, and IoT. Chapter 9. Convergence of Blockchain and Artificial Intelligence in IoT for the Smart City. Chapter 10. Blockchain for Supply Chain in Pharmaceutical Industry: An Approach to CounterFeit Drug Detection.

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Editor(s)

Biography

Ms. Indrakumari is working as an Assistant Professor, School of Computing Science and Engineering, Galgotias University, NCR Delhi, India. She has completed M.Tech in Computer and Information Technology from Manonmaniam Sundaranar University, Tirunelveli. Her main thrust areas are Big Data, Internet of Things, Data Mining, Data warehousing and its visualization tools like Tableau, Qlikview. Dr. R. Lakshmana Kumar is working as an Assistant professor of Computer Applications and currently also leads the technical training team in Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu. His PhD is from Anna University, Chennai and his Research is on Semantic Web Services and part of his PhD work was funded by South Korea. He is a global chapter Lead for MLCS [Machine Learning for Cyber Security] for the Coimbatore chapter. He is currently allied with company-specific training of Infosys Campus Connect, Oracle WDP and Palo Alto Networks. He holds the International certification on SCJP (Sun Certificated Java Programmer) and SCJWCD (Sun Certificate Java Web Component Developer). He works with programming languages like Java, Python and PHP. He is involved in research and has expertise in distributed computing. He holds the Data Science certification from John Hopkins University and the Amazon Cloud Architect certification from Amazon Web Services. He has published more than 25 papers in various international journals. Dr. B. Balamurugan completed Ph.D at VIT University, Vellore and currently works as a Professor in Galgotias University, Greater Noida, Uttar Pradesh. He has 15 years of teaching experience in the field of computer science. His area of interest lies in the field of Internet of Things, Big data, Networking. He has published more than 100 international journals papers and contributed book chapters. Dr. Vijanth Sagayan Asirvadam studied at the University of Putra, Malaysia for the Bachelor Science (Hon) majoring in Statistics and graduated in April 1997 before leaving for Queen’s University Belfast to do his Masters where he received his Master’s Science degree in Engineering Computation. He has worked as a Lecturer in a private higher institution and as a System Engineer in Multimedia University, Malaysia. He later joined the Intelligent Systems and Control Research Group at Queen’s University Belfast in November 1999 where he completed his Doctorate (Ph.D.) research in Online and Constructive Neural Learning methods. He is currently a Lecturer of Information Science and Technology at Multimedia University, Malaysia. His research interests include neural network and statistical learning for black-box modelling, model validation and data mining.