Wireless Sensor Networks (WSNs) have become a fundamental technology in modern intelligent systems due to their wide applications in environmental monitoring, smart cities, an‎d Internet of Things (IoT) environments. However, limited energy resources of sensor nodes remain a critical challenge, directly affecting network stability an‎d lifetime. Clustering has emerged as an effective strategy to reduce energy consumption by organizing nodes into groups an‎d assigning cluster heads (CHs) responsible for data aggregation an‎d transmission. Nevertheless, optimal cluster formation an‎d cluster head selec‎tion represent a complex optimization problem influenced by multiple factors such as residual energy, node distance, an‎d load balancing. This study investigates clustering optimization in WSNs using a metaheuristic approach based on the Horse Herd Optimization (HOA) algorithm. Inspired by the collective an‎d social behavior of horse herds, the proposed approach aims to enhance cluster head selec‎tion an‎d improve overall network efficiency. The methodology focuses on minimizing energy consumption, balancing node load distribution, an‎d extending network lifetime while maintaining network stability. The research reviews existing clustering techniques an‎d highlights the limitations of traditional methods in large an‎d dynamic networks. By leveraging the global search capability an‎d exploration–exploitation balance of HOA, the proposed framework provides an intelligent solution for efficient network organization. The findings demonstrate the potential of metaheuristic-based clustering strategies in improving energy efficiency an‎d prolonging the operational lifetime of wireless sensor networks

2/18/2026 12:00:00 AM

89720

1404/11/29

Clustering Wireless Sensor Networks: A Horse Herd Optimization Approach

Wireless Sensor Networks , Clustering , Energy Management , Network Lifetime , Metaheuristic Optimization , , Horse Herd Optimization