Energy efficient clustering protocol in wireless sensor networks using an adaptive hybrid optimization algorithm

The increasing prevalence of machine-to-machine communication has positioned Internet of Things (IoT)-enabled wireless sensor networks (WSNs) as a critical element in contemporary wireless systems. Numerous IoT applications demand WSNs to operate with optimal energy efficiency and reliable communication. Achieving these objectives necessitates effective cooperation among devices across various network layers. Clustering has proven effective in enhancing key performance metrics of WSNs. However, existing clustering methods face significant challenges, including limited cluster head (CH) lifetime and inadequate cluster quality. These limitations underscore the need for advanced routing mechanisms that ensure efficient CH selection while simultaneously improving cluster quality. This research addresses the optimal CH selection problem in WSNs by proposing an

adaptive hybrid optimization technique, termed the WIFN algorithm. This algorithm integrates the Whale Optimization Algorithm (WOA), the INFO algorithm, the Fussion–Fission Optimization (FuFiO), and the Naked Mole Rat Algorithm (NMRA). Comparative analysis against established solutions such as LEACH, SEP-E, HCR, ERP, SAERP, DRESEP, SEECP, DESTERP, HSSTERP, and FESTERP demonstrates the superior performance of the WIFN-based clustering protocol. The proposed approach significantly improves energy efficiency, extends network lifetime, and enhances overall communication reliability in WSNs.

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