Optimized energy efficient clustering in WSNs through modified zebra optimization

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.