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

The increasing demand for machine-to-machine communication has established Internet of Things (IoT)-enabled wireless sensor networks (WSNs) as a fundamental component of contemporary wireless systems. Numerous IoT-driven applications necessitate WSNs to function with optimal energy efficiency and dependable communication performance. Effective cooperation between devices deployed across numerous network layers is required to achieve these goals. Clustering has shown to be effective in improving key performance metrics of WSNs. However, there are major challenges with existing methods, including limited cluster head (CH) lifetime and inadequate cluster quality. These constraints highlight the need for an advanced routing method that ensures efficient CH selection while concurrently improving cluster quality. The optimal CH selection problem in WSNs is addressed in this work using a recently developed adaptive hybrid optimization technique which is a hybrid algorithm of the whale optimization algorithm (WOA), the INFO algorithm, the fussion–fission optimization (FuFiO) and naked mole rat algorithm (NMRA), known as the WIFN algorithm. In comparison to the existing solutions like LEACH, SEP-E, HCR, ERP, SAERP, DRESEP, SEECP, DESTERP, HSSTERP and FESTERP, the suggested WIFN-based clustering protocol performs better, achieving a longer network lifetime in terms of stability period (time period till the death of the first node from the initialization of the network) and consuming less energy. These results validate the suitability of WIFN methodology for creating effective IoT-supported WSNs.