Details

Title Fog Data Analytics for IoT Applications : Next Generation Process Model with State of the Art Technologies
Author Sudeep Tanwar (Editor)
Binding Paperback
Condition New
Pages 497
Volumes 1
Language ENG
Publisher Springer
Publication date 2021-08-26
Illustrated Yes
Features Illustrated
Bookseller's Inventory # ria9789811560460_inp
ISBN 9789811560460 / 9811560463
Weight 1.58 lbs (0.72 kg)
Dimensions 9.21 x 6.14 x 1.04 in (23.39 x 15.60 x 2.64 cm)
Category Technology & Industrial Arts
Quantity available 516

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From the publisher

This book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDAin IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy.

From the rear cover

This book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDAin IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy.

About the author

Dr. Sudeep Tanwar is an Associate Professor at the Computer Engineering Department at the Institute of Technology of Nirma University, India, and was a Visiting Professor at Jan Wyzykowski University in Polkowice, Poland, and the University of Pitesti, Romani. He received his Ph.D. in Wireless Sensor Networks from the Faculty of Engineering and Technology, Mewar University, India, in 2016. He has received three best research paper awards, including two from top-tier international conferences (IEEE-ICC and IEEE-GLOBECOM). His current interests include routings issues in WSN, blockchain technology, smart grid, and fog computing. He has authored/edited six books: Routing in Heterogeneous Wireless Sensor Networks (ISBN: 978-3-330-02892-0), Big Data Analytics (ISBN: 978-93-83992-25-8), Mobile Computing (ISBN: 978-93-83992-25-6), Energy Conservation for IoT Devices: Concepts, Paradigms and Solutions (ISBN: 978-981-13-7398-5), and Multimedia Big Data Computing for IoT Applications: Concepts, Paradigms and Solutions (ISBN: 978-981-13-8759-3). He is an Associate Editor of the Security and Privacy Journal and is a member of IAENG, ISTE, and CSTA.

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