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Energy-Efficient Routing in Wireless Sensor Networks: A Meta-heuristic and Artificial Intelligence-based Approach: A Comprehensive Review

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Abstract

Artificial Intelligence (AI) realizes Wireless Sensor Networks (WSNs) as a dynamic environment of battery-powered sensor nodes. These nodes are carefully built to monitor and record several environmental factors over a large area. The above nodes, which perform many activities in our everyday lives, may be considered the digital equivalents of our sensory faculties. Recent advances in network connection and computing capability have expanded WSN applications. Data capture and transmission to a distant server, frequently in an isolated place, are WSNs’ main purpose. These networks might be subterranean, underwater, terrestrial, or multimodal. They are utilized in industrial automation, traffic management, medical device monitoring, and other fields. Despite market growth, WSNs have several hurdles. Energy efficiency, storage and processing resource restrictions, bandwidth, error rates, scalability, and survival in hostile climates must be considered. This circumstance has made extending the longevity of these networks a major issue. Energy saving is a major difficulty in many vocations. Several bio-inspired algorithms have been developed to find the best route from member nodes to the sink node. These methods aim to reduce energy use and extend network life. This article investigates WSN routing and clustering, concentrating on optimization methods. We aim to give a comprehensive and insightful evaluation of WSN research, with an emphasis on AI integration. This study honors the development of clever methods to overcome WSNs’ various obstacles. The above issues will affect sensor-based connection in our increasing global environment, and our research shows our commitment to understanding and resolving them.

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Priyadarshi, R. Energy-Efficient Routing in Wireless Sensor Networks: A Meta-heuristic and Artificial Intelligence-based Approach: A Comprehensive Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-023-10039-6

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