Hiren Kumar Thakkar & Chinmaya Kumar Dehury 
Predictive Analytics in Cloud, Fog, and Edge Computing 
Perspectives and Practices of Blockchain, IoT, and 5G

This book covers the relationship of recent technologies (such as Blockchain, Io T, and 5G) with the cloud computing as well as fog computing, and mobile edge computing. The relationship will not be limited to only architecture proposal, trends, and technical advancements. However, the book also explores the possibility of predictive analytics in cloud computing with respect to Blockchain, Io T, and 5G. The recent advancements in the internet-supported distributed computing i.e. cloud computing, has made it possible to process the bulk amount of data in a parallel and distributed. This has made it a lucrative technology to process the data generated from technologies such as Blockchain, Io T, and 5G. However, there are several issues a Cloud Service Provider (CSP) encounters, such as Blockchain security in cloud, Io T elasticity and scalability management in cloud, Service Level Agreement (SLA) compliances for 5G, Resource management, Load balancing, and Fault-tolerance. This edited book will discuss the aforementioned issues in connection with Blockchain, Io T, and 5G.

Moreover, the book discusses how the cloud computing is not sufficient and one needs to use fog computing, and edge computing to efficiently process the data generated from Io T, and 5G. Moreover, the book shows how smart city, smart healthcare system, and smart communities are few of the most relevant Io T applications where fog computing plays a significant role. The book discusses the limitation of fog computing and the need for the edge computing to further reduce the network latency to process streaming data from Io T devices.

The book also explores power of predictive analytics of Blockchain, Io T, and 5G data in cloud computing with its sister technologies. Since, the amount of resources increases day-by day, artificial intelligence (AI) tools are becoming more popular due to their capability which can be used in solving wide variety of issues, such as minimize the energy consumption of physical servers, optimize the service cost, improve the quality of experience, increase the service availability, efficiently handle the huge data flow, manages the large number of Io T devices, etc.

Inhaltsverzeichnis

Chapter. 1. Collaboration of Io T and Cloud Computing towards Healthcare Security.- Chapter. 2. Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart Io T Healthcare.- Chapter. 3.The Role of Blockchain in Cloud Computing.- Chapter. 4. Analysis and Prediction of Plant Growth in a Cloud-based Smart Sensor Controlled Environment.- Chapter. 5. Cloud-based Io T controlled System Model for Plant Disease Monitoring.- Chapter. 6.- Design and Usage of a Digital E- Pharmacy Application Framework.- Chapter. 7. Serverless Data Pipelines for Io T data analytics: A cloud vendors perspective and solutions.- Chapter. 8. Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction.- Chapter. 9. Impact of 5G technologies on cloud analytics.- Chapter. 10. Io T based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers.-Chapter. 11. A Workload-aware Data Placement Scheme for Hadoop-enabled Map Reduce Cloud Data Center.- Chapter. 12. 5G Enabled Smart City using Cloud Environment.

Über den Autor

Dr. Hiren Kumar Thakkar has received his M.Tech in Computer Science and Engineering from IIIT Bhubaneswar, India in 2012 and a Ph.D. in Computer Science and Information Engineering from Chang Gung University, Taiwan in 2018. Later, he worked as a postdoctoral researcher in the Department of Occupation Therapy, Motor Behavioral Research Lab (MBRL), Chang Gung University, Taiwan. Currently, he is working as an Assistant Professor in the Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gujarat, India. His research interests include optimization, machine learning, and reinforcement learning. He has published several research papers in peer-reviewed journals such IEEE Transactions in Parallel and Distributed Systems, IEEE Sensors Journal, IEEE Journal on Selected Areas in Communications, Information Processing and Management, Elsevier. He is a member of IEEE.


Dr. Chinmaya Kumar Dehury received a Bachelor’s degree from Sambalpur University, India, in June 2009 and a Master’s degree from Biju Pattnaik University of Technology, India, in June 2013. He received a Ph.D. Degree in the Department of Computer Science and Information Engineering, Chang Gung University, Taiwan. He is currently a Lecturer of Distributed System, member of Mobile & Cloud Lab in the Institute of Computer Science, University of Tartu, Estonia. His research interests include scheduling, resource management and fault tolerance problems of Cloud and fog Computing, the application of artificial intelligence in cloud management, edge intelligence, Internet of Things, and data management frameworks. His research results are published by top-tier journals and transactions such as IEEE TCC, JSAC, TPDS, FGCS, etc. He is a member of IEEE and ACM India. He is also serving as a PC member of several conferences and reviewer to several journals and conferences, such as IEEE TPDS, IEEE JSAC, IEEE TCC, IEEE TNNLS, Wiley Software: Practice and Experience, etc.


Dr. Prasan Kumar Sahoo (Senior Member, IEEE) received the BSc degree in physics (with honors), the MSc degree in mathematics both from Utkal University, Bhubaneswar, India, in 1987 and 1994, respectively, the MTech degree in computer science from the Indian Institute of Technology (IIT), Kharagpur, India, in 2000, the first Ph D degree in mathematics from Utkal University, Bhubaneswar, India, in 2002, and the second Ph D degree in computer science and information engineering from the National Central University, Taiwan, in 2009. He is currently a professor with the Department of Computer Science and Information Engineering, Chang Gung University, Taiwan. He is an adjunct researcher in the Division of Colon and Rectal Surgery, Chang Gung Memorial Hospital, Linkou, Taiwan since 2018. He has worked as an associate professor with the Department of Information Management, Vanung University, Taiwan, from 2007 to 2011. He was a visiting associate professor with the Department of Computer Science, Universite Claude Bernard Lyon 1, Villeurbanne, France. His current research interests include artificial intelligence, big data analytic, cloud computing, and Io T. He is the lead guest editor, special issue of Electronics journal and an editorial board member for the International Journal of Vehicle Information and Communication Systems. He has worked as the program committee member of several IEEE and ACM conferences.


Dr. Bharadwaj Veeravalli (Senior Member, IEEE) received the BSc degree in physics from Madurai-Kamaraj University, India, in 1987, the master’s degree in electrical communication engineering from the Indian Institute of Science, Bangalore, India, in 1991, and the Ph D degree from the Department of Aerospace Engineering, Indian Institute of Science, in 1994. He is currently a tenured associate professor with the Department of Electrical and Computer Engineering, Communications and Information Engineering (CIE) Division, National University of Singapore, Singapore. His main research interests include cloud/grid/cluster computing, which include big data processing, analytics, and resource allocation, scheduling in parallel and distributed systems, cybersecurity, and multimedia computing. He is one of the earliest researchers in the field of divisible load theory. He is currently on the editorial board of the IEEE Transactions on Parallel and Distributed Systems as an associate editor. He is a senior member of the IEEE and the IEEE-CS. He was the recipient of Gold Medals for his bachelor’s degree overall performance and outstanding Ph D thesis (IISc, Bangalore, India), in 1987 and 1994, respectively.