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This book provides current state of the art along with an insight of recent research trends and open issues, challenges, and future research direction for the academician, analyzer, researcher, writers, and authors. It also provides an opportunity to exchange knowledge in the field of IoT-enabled smart systems, Industry 4.0, networking, cyber-physical system, computing paradigms, and security with various tools and methods used for industry-oriented intelligent-based IoT applications. This advanced research edited book focuses on emerging and advancing technology-federated machine learning, blockchain, and artificial intelligence for real-time Internet of things (IoT) application to solve the real-world problems and make the life of human more conformable. The objective of the proposed book is to develop privacy preserving model for IoT applications and improve the security, privacy, reliability, and sustainability of the systems. This book motivates and enhances the quality of research and commercialization in the fields of AI, Industry 4.0, federated learning, and blockchain.
Dr. Mohit Kumar is an assistant professor in the Department of Information Technology at Dr. B R Ambedkar National Institute of Technology, Jalandhar, India. He received his Ph.D. degree from Indian Institute of Technology Roorkee in the field of cloud computing in 2018, and M.Tech. degree in Computer Science and Engineering from ABV-Indian Institute of Information Technology Gwalior, India, in 2013. He has received his B.Tech. degree in Computer Science and Engineering from MJP Rohilkhand University Bareilly, 2009. His research topics cover the areas of cloud computing, fog/ edge computing, Internet of things, federated learning, blockchain, and artificial intelligence. He has published more than 40 research articles in reputed journals, IEEE transactions and international conferences. He has been a session chair and keynote speaker of many international conferences, webinars, FDP, and STC in India. He has guided six M.Tech. theses and guiding 5 Ph.D. scholars.
Dr. Anand Nayyar received Ph.D. (Computer Science) from Desh Bhagat University in 2017 in the area of wireless sensor networks, swarm intelligence, and network simulation. He is acting as an associate editor for Wireless Networks (Springer), Computer Communications (Elsevier), International Journal of Sensor Networks (IJSNET) (Inderscience), Frontiers in Computer Science, PeerJ Computer Science, Human Centric Computing and Information Sciences (HCIS), Tech Science Press-CSSE, IASC, IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI, IJGC, and IJSIR. He has reviewed more than 2500+ articles for diverse Web of Science and Scopus Indexed Journals. He is currently researching in the area of wireless sensor networks, Internet of things, swarm intelligence, cloud computing, artificial intelligence, drones, blockchain, cyber-security, healthcare informatics, big data, and wireless communications.
Dr. Ashutosh Kumar Singh obtained B.Tech. in Information Technology from Uttar Pradesh Technical University Lucknow (now AKTU Lucknow), India, in 2011, M.Tech. degree in Computer Science and Engineering from Indian Institute of Information Technology and Management Gwalior, India, in 2014, and Ph.D. in CSE from Motilal Nehru National Institute of Technology Allahabad, India. Now, Dr. Singh is currently working as an assistant professor in the Department of Computer Science and Engineering, United College of Engineering and Research Allahabad, India. Till date, Dr. Singh has published 12 publications in reputed journals (including 6 SCI, 10 Scopus Indexed) and conferences (12 International Scopus Indexed Conferences), reviewers of various reputed journals including IEEE transactions, conferences, and books, guided 2 M.Tech. theses, currently guiding 03 M.Tech. theses. Dr. Singh has membership of IEEE and ACM. His research interests include network optimization, software-defined networking, machine learning, IoT, and edge computing.
Dr. Yanhui Guo received the B.S. degree in automatic control from Zhengzhou University, China, the M.S. degree in pattern recognition and intelligence system from the Harbin Institute of Technology, China, and the Ph.D. degree from the Department of Computer Science, Utah State University, USA. He was a research fellow with the Department of Radiology, University of Michigan, and also an assistant professor with St. Thomas University. He is currently an assistant professor with the Department of Computer Science, University of Illinois, at Springfield. He has published more than 70 journal articles and 20 top conference articles and completed more than ten grant funded research projects. His research areas include computer vision, machine learning, big data analytics, computer-aided detection/diagnosis, and computer assist surgery. He worked as an associate editor of different international journals and Reviewer for top journals and conferences.
Convergence of AI, Federated learning and Blockchain.- AI for expert system.- Natural Language processing for industry 4.0.- Design and development of autonomous systems.- Decision-Making Techniques for Analytical Systems.- Smart Computational Techniques for IoT applications.- Federated Learning for advanced healthcare.- Federated learning for privacy preserving.- Secure computation for IoT applications.- Analysis and Implementation of Security using Federated Systems.- Development of Federated Learning Recommendation Systems.- Development of Intelligent Productive Federated Learning Systems.- AI and Federated Learning for Sustainable Development Systems Industry 4.0.- Role of Blockchain Techniques for Sustainable Development.- Blockchain with smart contacts for real-time applications.- Trustworthy AI in Cybersecurity.- Case study based upon smart cities, home and agriculture using AI and Blockchain.- Sustainable Development using Blockchain Technology.- Traceability and Transparency issues in Transactions using Blockchain Techniques.- Emerging Trends and future directions of AI assisted Blockchain and Federated Learning for Sustainable Development Systems.
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