Energy–lifetime aware cluster head selection in wireless sensor networks using an adaptive hybrid GWO–PSO framework

Network lifetime and energy efficiency are two areas where wireless sensor networks fall short. Optimizing energy-lifetime trade-offs by intelligent node selection and adaptive optimization, this work offers a hybrid GWO-PSO routing and clustering system. Several protocols are evaluated based on efficiency, residual energy, network lifetime, throughput, and energy consumption. These protocols include EOAMRCL, GWO-ABC, GWO-PSO, PSO-DE, AEOWSNC, and QPSO-Fuzzy. It outperforms conventional methods in simulations when using the Hybrid GWO-PSO. It significantly reduces energy consumption compared to LEACH (41–45%), PSO-DE (28–32%), and QPSO-Fuzzy (20–25%). In comparison to LEACH, PSO-DE, and QPSO-Fuzzy, the method improves network lifespan by 110–130%, 45–60%, and 15–20%, respectively. Its efficiency, measured in lifetime per joule, is twice that of LEACH and 30–50% greater than that of earlier hybrid approaches. Reduced energy variance, balanced distribution of cluster-head loads, and stabilization of convergence behavior are the three ways this method increases throughput and dependability. Energy reduction and longevity are always optimized by the Hybrid GWO-PSO, according to the Pareto-based trade-off analysis. Based on these findings, the suggested system is suitable for large-scale, long-term WSN deployments since it is scalable, energy-efficient, and robust.