Industry 4.0 Interoperability, Analytics, Security, and Case Studies

All over the world, vast research is in progress on the domain of Industry 4.0 and related techniques. Industry 4.0 is expected to have a very high impact on labor markets, global value chains, education, health, environment, and many social economic aspects.

Industry 4.0 Interoperability, Analytics, Security, and Case Studies provides a deeper understanding of the drivers and enablers of Industry 4.0. It includes real case studies of various applications related to different fields, such as cyber physical systems (CPS), Internet of Things (IoT), cloud computing, machine learning, virtualization, decentralization, blockchain, fog computing, and many other related areas. Also discussed are interoperability, design, and implementation challenges.

Researchers, academicians, and those working in industry around the globe will find this book of interest.

FEATURES

• Provides an understanding of the drivers and enablers of Industry 4.0
• Includes real case studies of various applications for different fields
• Discusses technologies such as cyber physical systems (CPS), Internet of Things (IoT), cloud computing, machine learning, virtualization, decentralization, blockchain, fog computing, and many other related areas
• Covers design, implementation challenges, and interoperability
• Offers detailed knowledge on Industry 4.0 and its underlying technologies, research challenges, solutions, and case studies

G. Rajesh is an Assistant Professor in the Department of Information Technology at Anna University, Chennai, India. He earned a PhD in trust-based temporal data aggregation for energy-constrained wireless sensor networks at Anna University in 2016. He did postgraduate in computer science engineering at the Government College of Engineering, Tirunelveli under Anna University. He earned an undergraduate degree in information technology at AVC College of Engineering at Anna University. He has 12 years of teaching and research experience. His area of research interest includes wireless sensor networks and its IoT applications, software engineering machine learning, data analysis, and computational optimization. His research contributions include 3 patents from India, 2 authored books, 2 edited books, and many articles in reputed international journals and at conferences. He also is a reviewer at reputed journals and conferences. He is an active member of IEEE, SAEINDIA, the Society of Automotive Engineers, and the International Association of Engineers (IAENG).

X. Mercilin Raajini is an Associate Professor in the Department of Electronics and Communication at Prince Shri Venkatesahwara Padmavathy Engineering College at Anna University, Chennai, India. She earned a PhD in enhanced routing techniques for energy management in wireless sensor networks at Anna University, India in 2016. She completed postgraduate work in power electronics and drives at Government College of Engineering, Tirunelveli at Anna University in 2007. She earned an undergraduate degree in electronics and communication at Noorul Islam College of Engineering, under MS University in 2001. She has 15 years of teaching and research experience. Her area of research interest includes wireless sensor networks and its IoT applications, signal processing, machine learning, and computational optimization. Her research contributions include 2 patents from India, 3 authored books, 2 edited books, and many published articles in international journals and at conferences. She also is a reviewer for reputed journals and conferences. She is an active member of ISTE, SAEINDIA, and the Society of Automotive Engineers.

Hien Dang earned a PhD in computer science in 2010. She is on the faculty at Computer Science and Engineering Faculty, Thuy Loi University, Vietnam. She is also a research scholar at the University of Massachusetts, Boston, USA. She is the author or coauthor of many books and has more than thirty papers in journals and conference contributions. Her research areas include artificial intelligence, machine learning, deep learning, problems about regressive prediction, Big Data analytics, and processing for practical applications.