This book investigates the fundaments, standards, and protocols of Cyber-Physical Systems (CPS) in the industrial transformation environment. It facilitates a fusion of both technologies in the creation of reliable and robust applications.
Cyber-Physical Systems for Industrial Transformation: Fundamentals, Standards, and Protocols explores emerging technologies such as artificial intelligence, data science, blockchain, robotic process automation, virtual reality, edge computing, and 5G technology to highlight current and future opportunities to transition CPS to become more robust and reliable. The book showcases the real-time sensing, processing, and actuation software and discusses fault-tolerant and cybersecurity as well.
This book brings together undergraduates, postgraduates, academics, researchers, and industry individuals that are interested in exploring new ideas, techniques, and tools related to CPS and Industry 4.0.
About the Author: Gunasekaran Manogaran is currently working as a Big Data Scientist in Honward University, Washington DC, United States. He received his PhD from the Vellore Institute of Technology University, India. He received his Bachelor of Engineering and Master of Technology from Anna University and Vellore Institute of Technology University respectively. He has worked as a Research Assistant for a project on spatial data mining funded by Indian Council of Medical Research, Government of India. His current research interests include data mining, big data analytics and soft computing. He is the author/co-author of papers in conferences, book chapters and journals. He got an award for young investigator from India and Southeast Asia by Bill and Melinda Gates Foundation. He is a member of International Society for Infectious Diseases and Machine Intelligence Research labs.
Nour Eldeen M. Khalifa received his B.Sc., M.Sc., and Ph.D. degree in 2006, 2009 and 2013 respectively, all from Cairo University, Faculty of Computers and Artificial Intelligence, Cairo, Egypt. He also had a Professional M.Sc. Degree in Cloud Computing in 2018. He authored/coauthored more than 30 publications and 2 edited books. He had more than 1700 citations. He reviewed several papers for international journals and conferences including (Scientific Reports, IEEE IoT, Neural Computing, Artificial Intelligence Review). Currently, he is an associate professor at Faculty of Computers and Artificial Intelligence, Cairo University. His research interests include wireless sensor networks, cryptography, multimedia, network security, machine, and deep learning.
Mohamed Hamed N. Taha received the B.Sc., M.Sc., and Ph.D. degrees from the Faculty of Computers and Artificial Intelligence, Cairo University, in 2006, 2009, and 2013, respectively. He has been an Associate Professor with the Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, since 2016. He is a Reviewer of the IEEE INTERNET OF THINGS JOURNAL. Deep learning, machine learning, the Internet of Things, wireless sensor networks, and blockchain are his research interests.
Mohamed Loey is currently an Associate Professor in the Faculty of Computers and Artificial Intelligence, Benha University. He is Associate professor seconded to Buraydah Colleges, Saudi Arabia. In 2021, he was a head and coordinator of information technology program in New Cairo Technological University, Egypt. He received the B.Sc., and M.Sc. degrees from the Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University in 2006, 2009, respectively. In 2008, he was an assistant lecturer in Sinai University. He received the PhD. degrees from the Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University in 2017. He is a reviewer in different international journals. He has published over 30 publications with over 1900 citations. He has several publications in reputed and high impact journals published by Elsevier, Springer, and others. His research interests include deep learning, machine learning, computer vision, image processing, big data, and computational neuroscience.
