Intelligent node identification and dynamic clustering for underwater acoustic sensor networks

Underwater Acoustic Sensor Networks (UWSNs) are used to facilitate a variety of applications, such as ocean observation and offshore exploration. However, they are typically characterized by a number of inherent issues, i.e., high delay in transmission, high energy consumption, complicated deployment process, long propagation delay, and high signal attenuation. Moreover, the occurrence of void areas along the network routing path has the potential to severely impair overall performance. To solve these problems, particularly the routing void one, it is important to select a best forwarder node. The suggested solution starts by utilizing the Silhouette Score-based Taylor Fuzzy C-Means (S2-TFCM) clustering algorithm to efficiently cluster sensor nodes. Then, Cluster Head (CH) selection is done based on a new Binary Fire Hawks Optimization (BFHO) algorithm, based on significant parameters like residual energy, node degree, priority factor, and sink node distance. Lastly, an Evolutionary Game Theory-based model is utilized to determine the most appropriate forwarder node, taking into consideration factors like depth difference. Simulation outcomes reveal that the given method improves energy efficiency by 18.7%, improves throughput by 14.2%, and extends network lifetime by 21.5% compared to current schemes such as EGRC and BEEC, thereby proving effective for energy-efficient and reliable routing in UWSNs.