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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Raghuveer Singh Dhaka,
  • Manju Khurana,
  • Sonu Lamba,
  • Arpit Kumar Sharma

摘要

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.