Networked Sensing Systems
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With today’s advancements in wireless and mobile connectivity, Internet of Things (IoT) sensor technologies, and digital innovation, sustainability principles are increasingly reinforcing one another. To transition to more resource-efficient solutions, use resources responsibly, and streamline operations, businesses must embrace digital transformation. Potential application areas include energy management, air pollution monitoring, fleet management, water management, and agriculture. Simultaneously, the expansion of IoT deployments and their integration into the contexts of 5G and emerging 6G mobile networking necessitate that the solutions themselves be green and sustainable. This includes incorporating energy- and environmentally-conscious technical solutions for communications.
By offering previously unattainable solutions, networked sensing can contribute to a more sustainable society by enabling the collection of data from heterogeneous sources in unique and novel ways. Additionally, the networking-based solutions themselves must be sustainable and environmentally friendly. For example, optimizing network architecture and relocating network equipment to strategic locations can significantly reduce energy waste. These goals drive the search for improved sensing technologies, emphasizing energy-efficient mobile sensing devices.
The goal of Networked Sensing Systems is to present and highlight the latest developments in sustainable networked sensing systems across a variety of contexts, all united by the aim of enhancing human well-being and combating climate change. Regardless of the area of expertise, this work seeks to offer practical solutions to the major challenges of building a sustainable smart society 5.0. This book serves as a platform to discuss networked sensing systems for a sustainable society, focusing on systems and applications based on mobile computing and wireless networks, while adopting multidisciplinary approaches that emphasize the human element in addressing these challenges.
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Rajesh Kumar Dhanaraj, PhD, is a distinguished professor at Symbiosis International University in Pune, India. He has authored and edited over 50 books on cutting-edge technologies and holds 21 patents. He has contributed more than 100 articles and papers to esteemed refereed journals and international conferences, along with chapters for several influential books. He has shared his expertise with the academic community through numerous tech talks on disruptive technologies. Dr. Dhanaraj is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the Computer Science Teachers Association and the International Association of Engineers.
Malathy Sathyamoorthy, PhD, is an assistant professor in the Department of Information Technology at KPR Institute of Engineering and Technology, Tamil Nadu, India. She is a life member of the Indian Society for Technical Education and the International Association of Engineers. Dr. Sathyamoorthy has published extensively, including more than 25 research papers in SCI, Scopus, and ESCI-indexed journals, 22 papers in international conferences, two patents, one book, and eight book chapters.
Balasubramaniam S., PhD, is a postdoctoral researcher in the Department of Applied Data Science at Noroff University College, Kristiansand, Norway, with over 15 years of experience in teaching, research, and industry. He has published over 20 research papers in SCI and Scopus-indexed journals, contributed chapters to internationally published books, and has been granted one Australian patent, one Indian patent, and three Indian patent publications. He has also presented papers at conferences and organized numerous conferences, symposiums, and seminars.
Seifedine Kadry, PhD, is a professor of data science at Noroff University College, Norway. He is also an Accreditation Board for Engineering and Technology (ABET) program evaluator for computing engineering technology. Dr. Kadry is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). His current research interests include data science, education through technology, system prognostics, stochastic systems, and probability and reliability analysis.
