OGWO-CH: Hybrid Opposition-Based Learning with Gray Wolf Optimization Based Clustering Technique in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs battery-powered, and the energy drainage significant issue. The clustering approach holds an imperative part boosting lifespan WSNs. This gathers into clusters selects cluster heads (CHs). CHs accumulate information from members transfer data to base station (BS). Yet, most challenging task select optimal thereby enhance network lifetime. article introduces head selection framework using hybrid opposition-based learning with gray wolf optimization algorithm. technique dynamically trades off between exploitation exploration search strategies selecting best CHs. In addition, four different metrics such as consumption, minimal distance, node centrality degree utilized. proposed mechanism enhances efficiency by algorithm experimented on MATLAB (2018a) validated performance energy, alive nodes, BS position, packet delivery ratio. obtained results exhibit better outcome terms more per round, maximum number packets BS, improved residual enhanced At last, has achieved lifetime ≈20%, ≈30% ≈45% compared grey (GWO), Artificial bee colony (ABC) Low-energy adaptive hierarchy (LEACH) techniques.