Enhancing Network Lifetime and Communication Efficiency in Wireless Sensor Networks Using a Hybrid Lévy–Starfish Clustering Optimization Algorithm

In the wireless sensor network, different spatially distributed sensors are used to sense, integrate, and transfer data for further evaluations. Several traditional approaches struggle with a few major difficulties like inefficient data routing and static cluster head selection. Therefore, this paper proposes a novel optimization approach termed the Lévy flight–boosted starfish optimization model for an improved low‐energy adaptive clustering protocol. This model integrated the Lévy flight mechanism and the starfish optimization algorithm for effective global search ability and cluster head selection. The output of experiments conducted revealed that the proposed model attained robust performance compared to existing models with a throughput of 4 Mbps and a packet delivery rate of 98.6%. Overall, the proposed model exhibits significant efficiency in enhancing the performance of wireless sensor networks, making a model optimal for long‐term and large‐scale applications.