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<p>Addressing the challenges of energy imbalance and the difficulty in optimizing cluster head selection in clustering protocols for wireless sensor networks (WSNs), this paper proposes a clustering protocol based on an improved zebra optimization algorithm (IZOACP). The method systematically solves the NP-hard problem of cluster head selection by integrating the zebra optimization algorithm (ZOA), Gaussian mutation strategy, and opposition-based learning mechanism, while optimizing the clustering process based on four key metrics: node residual energy, network density, intra-cluster distance, and communication delay. To further enhance data transmission efficiency, a dynamic adaptive inter-cluster routing mechanism is designed, which achieves path dynamic balancing based on node distance, residual energy, and load status. Experimental results demonstrate that, compared to the LEACH, DMaOWOA, and ARSH-FATI-CHS protocols, IZOACP significantly outperforms the comparison schemes in key metrics such as network lifespan (improved by 97.56%), throughput (improved by 93.88%), and transmission delay (reduced by 10.12%). These results validate its superiority in energy consumption control, topology stability, and large-scale monitoring scenarios, providing an efficient and reliable clustering optimization framework for WSN information monitoring systems.</p>

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Optimized energy efficient clustering in WSNs through modified zebra optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Yeshen Lan,
  • Mingqi Kan,
  • Bingyu Cao,
  • Chuchu Rao,
  • Mingan Zhou,
  • Peng Zhou

摘要

Addressing the challenges of energy imbalance and the difficulty in optimizing cluster head selection in clustering protocols for wireless sensor networks (WSNs), this paper proposes a clustering protocol based on an improved zebra optimization algorithm (IZOACP). The method systematically solves the NP-hard problem of cluster head selection by integrating the zebra optimization algorithm (ZOA), Gaussian mutation strategy, and opposition-based learning mechanism, while optimizing the clustering process based on four key metrics: node residual energy, network density, intra-cluster distance, and communication delay. To further enhance data transmission efficiency, a dynamic adaptive inter-cluster routing mechanism is designed, which achieves path dynamic balancing based on node distance, residual energy, and load status. Experimental results demonstrate that, compared to the LEACH, DMaOWOA, and ARSH-FATI-CHS protocols, IZOACP significantly outperforms the comparison schemes in key metrics such as network lifespan (improved by 97.56%), throughput (improved by 93.88%), and transmission delay (reduced by 10.12%). These results validate its superiority in energy consumption control, topology stability, and large-scale monitoring scenarios, providing an efficient and reliable clustering optimization framework for WSN information monitoring systems.