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Edge Computing: A Guide to Achieving Sustainable Living
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Author(s): Manvi Vijay Gawande (Pimpri Chinchwad College of Engineering and Research, India), Sanika Shrikant Hange (Pimpri Chinchwad College of Engineering and Research, India), Mukesh Mukundraj Tandale (Pimpri Chinchwad College of Engineering and Research, India)and Archana Kollu (Pimpri Chinchwad College of Engineering and Research, India)
Copyright: 2025
Pages: 36
Source title:
Machine Learning for Environmental Monitoring in Wireless Sensor Networks
Source Author(s)/Editor(s): Parikshit N. Mahalle (Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, India), Dattatray G. Takale (Vishwakarma Institute of Information Technology, India), Sachin Sakhare (Vishwakarma Institute of Information Technology, India)and Ganesh B. Regulwar (Vardhaman College of Engineering, India)
DOI: 10.4018/979-8-3693-3940-4.ch005
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Abstract
Data processing near the network edge solves decentralized cloud infrastructures. Without time-consuming transfers to centralized data centers, edge computing processes and analyzes data locally. Network processing at the edge minimizes latency when transferring or sharing data with remote servers. Reduced data center processing improves real-time application speed, latency, and energy savings. Edge computing makes AI and IoT devices work without cloud load. Energy is saved by offloading computation to edge devices. Edge computing selectively processes and consolidates data at the network edge and sends only relevant data to the cloud. Strategic data transmission conserves network capacity and benefits the environment. Edge data storage eliminates big data centers and reduces their environmental effect from construction, maintenance, and cooling. Automotive, healthcare, smart cities, and industrial automation benefit from edge computing architectures.
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