Battery-Free Sensor Networks for Sustainable Next-Generation IoT Connectivity

G.S. Karthick

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English

IGI Global
08 April 2025

Electronic devices & materials; Computer hardware; Computer networking & communications

Battery-free sensor networks emerge as a pivotal technology for enabling sustainable, next-generation Internet of Things (IoT) connectivity. These networks leverage energy harvesting techniques, such as solar, thermal, or radiofrequency (RF) energy, to power sensors and devices, eliminating the need for traditional batteries. This reduces the environmental impact of battery disposal while extending the operational lifetime of IoT devices, making them more reliable and cost-effective. By harnessing energy sources, battery-free sensor networks hold the potential to revolutionize applications in smart cities, industrial monitoring, healthcare, and agriculture, contributing to the development of energy-efficient, self-sustaining IoT systems. Battery-Free Sensor Networks for Sustainable Next-Generation IoT Connectivity explores contemporary developments in battery-free sensor networks and their pivotal role in advancing sustainable connectivity within the next-generation IoT landscape. It delves into the latest advancements, challenges, and applications of battery-free sensor technologies, offering insights into their design principles, energy harvesting techniques, communication protocols, and deployment strategies. This book covers topics such as healthcare monitoring, sensor technology, and sustainability, and is a useful resource for engineers, scientists, environmentalists, business owners, academicians, researchers, and security professionals.

Edited by: G.S. Karthick
Imprint: IGI Global
Dimensions: Height: 279mm, Width: 216mm, Spine: 27mm
Weight: 1.415kg
ISBN: 9798369376003
Pages: 570
Publication Date: 08 April 2025
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Hardback
Publisher's Status: Active

G. S. Karthick is currently working as an Assistant Professor in the Department of Software Systems, PSG College of Arts & Science, Coimbatore, India. Previously, he worked as a Research Fellow under the DST-ICPS Project from April 2019 to March 2022 in the Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, India. Also, he worked as an Assistant Professor in the Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India. Previously, he worked as an Assistant Professor in the Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India. He earned his Ph.D. in Computer Science from Bharathiar University (University Department) in February 2023. He topped the Post-Graduation with Gold Medal in 2017 from Bharathiar University, Coimbatore, India. He topped Under-Graduation with an exemplary performance from SNR Sons College, Coimbatore, India, and also aced three subjects to receive gold medals in 2015. He has qualified UGC-NET Computer Science and Applications in December 2020. Further, he has visited many institutions and universities as a resource person at various events. He has received an Indian Patent Grant for his innovative contribution to the field of Wireless Networks and the Internet of Things. He has published research papers in international journals and presented papers at International and National Conferences. He has published many book chapters and in addition, he has edited one book and authored two books. He is an active Editorial Member of IJHIS, IJERTCS, and IJEHMC. Also, he is an active reviewer in high-standard journals of Elsevier, IEEE, Springer, IGI Journals, and many peer-reviewed journals. He has received many awards and rewards for his achievements in academic and research careers. His areas of specialization are Machine Learning, the Internet of Things, Wireless Sensor Networks, and Analysis of Algorithms. His current research interests focus on the recent developments in the healthcare internet of things and machine learning algorithms.