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Title	Energy-Efficient Deep Learning Approaches In Iot, Fog, And Green Blockchain Revolution
ID_Doc	23471
Authors	Sathio A.A.; Rind M.M.; Awan S.A.; Junejo A.R.
Year	2025
Published	Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution
DOI	http://dx.doi.org/10.4018/979-8-3373-0300-0
Abstract	The convergence of Internet of Things (loT), fog computing, and blockchain technology can be used to revolutionize energy efficiency and sustainability. The implementation of deep learning (DL) techniques may optimize the energy consumption of these interconnected systems. Thus, they can be used to create green, energy-efficient solutions for various industries, including smart cities, healthcare, finance, and industrial loT (lloT). Focusing on the energy efficiency and environmental impact of these technologies, they provide valuable insights into creating sustainable and scalable systems. Energy-Efficient Deep Learning Approaches in loT, Fog, and Green Blockchain Revolution bridges the knowledge gap between traditional loT and blockchain research and the emerging need for energy-efficient and green technologies. It influences future research directions, encourages collaboration across disciplines, and inspires innovations that prioritize sustainability. Covering topics such as software-defined networking (SDN), ecosystem conservation, and monitoring systems, this book is an excellent resource for computer scientists, policymakers, technologists, industry practitioners, engineers, environmentalists, sustainability advocates, professionals, researchers, scholars, academicians, and more. © 2025 by IGI Global Scientific Publishing. All rights reserved.
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