Cluster Head Selection for Energy Balancing in Wireless Sensor Networks Using Modified Salp Swarm Optimization

– In today’s realm, Wireless Sensor Network (WSN) has been emerged as a prominent research topic due to the advances in the design of small and low cost sensors for an extensive sort of applications. A battery powers the sensor nodes that make up the WSNs. The restricted quantity of electricity available within WSN nodes is considered as one of the important research issues. Researchers have offered a variety of proposals from various angles to maximize the use of energy resources. Clustering nodes has shown to be one of the most effective ways for WSNs to save energy. The traditional Salp Swarm Algorithm (SSA) has a slow convergence rate and local optima stagnation, and thus produces disappointing results on higher-dimensional issues. Convergence inefficiency is caused by SSA's lack of exploration and exploitation. Improvements to the original population update method are made in this study, and a Modified Salp Swarm Algorithm (MSSA) is provided for achieving energy stability and sustaining network life time through effective cluster head selection throughout the clustering process. Furthermore, the performance of MSSA is validated and equated to other start-of-the art optimization algorithms under different WSN deployments. The suggested model outperforms competing algorithms in terms of sustained operation time, longevity of the network, and total energy consumption, as shown by the simulation results.