Multi-objective Sand Cat Swarm Optimization Algorithm for Cluster Head and Routing Path Selection in WSN

Abstract

A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements that are utilized for sensing, tracking, and data collection in a variety of situations. Sensor energy is considered a significant resource due to the difficulty in providing battery a backup to sensors when they are deployed in an uncontrolled or wild area. Integrating clustering & routing strategies are the optimal techniques for saving sensor node energy. Cluster heads (CHs) are selected carefully for load balancing throughout the clustering process. Therefore, multi-objective sand cat swarm optimization (MSCSO) algorithm is proposed to maximize the efficiency of energy in WSN. The optimum cluster heads from the network are selected and the path through the cluster head is identified by utilizing MSCSO. The proposed MSCSO decreases the usage of energy of nodes when maximizing the transmission of data in the WSN. The performances of MSCSO are evaluated based on alive nodes, energy consumption, throughput, and life expectancy for 100 nodes and attained better performance than other methods like low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and threshold-distributed energy efficient clustering (T-DEEC). The MSCSO is compared with existing methods such as fractional artificial lion (FAL) algorithm, taylor-spotted hyena optimization (Taylor-SHO) algorithm, energy centric multi objective salp swarm algorithm (ECMOSSA) and whale based tunicate swarm algorithmaquila optimizer (WTSA-AO) and shows that the MSCSO algorithm improves the performance by reducing energy consumption of 0.0208J, 0.0350J and 0.04045J for 200, 400 and 600 rounds respectively.