Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution. This item is unavailable.
Language: English
Published by IGI Global, 2025
- Softcover
- New

Heritage seller
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
5-star seller
AbeBooks seller since June 11, 1999
Unavailable
Softcover
Condition: New
US$ 345.15
Item description from seller
New Book. Shipped from UK. Established seller since 2000.
Seller Inventory # L2-9798337303017
- Title
- Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution
- Publisher
- IGI Global
- Publication year
- 2025
- Condition
- New
- Binding
- PAP
- Language
- English
- ISBN 13
- 9798337303017
- Item weight
- 1,196 grams
The convergence of Internet of Things (IoT), 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 IoT (IIoT). 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 IoT, Fog, and Green Blockchain Revolution bridges the knowledge gap between traditional IoT 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.
"Synopsis" may belong to another edition of this title.