Energy Efficient Cluster Head Selection Using Fish Swarm Optimization Algorithm (EECHS-FSOA) In Wireless Sensor Network (WSN)

Wireless Sensor Network (WSN) with reliability and network lifetime plays a significant role. Cluster Head Selection (CHS) has made clustering more energy efficient, application is still challenging. This paper uses an EECHS-FSOA model to solve $\mathbf{C H}$ selection problems and maximize network lifetime. Fitness Function (FF) is derived by the clustering algorithm using numerical metrics such as residual energy, distance to neighbors, and distance to Base Station (BS).By incorporating the fitness value for energy usage, the FF improves energy efficiency. FSOA classifies four operators as “search“ and “movement,“ with the latter being utilized for CHS. These operators are Movement, Food, Instinctive collective movement, and Non-Instinctive collective movement. The result with the fewest hop routing is selected as the best, and the new CH position is selected based on the current fitness value. The EECHS algorithm increases the network lifetime of the WSN. Additionally, by restricting node energy usage during data packet transmission, the total no. of packets received by BS is improved. The proposed system appears to be taking advantage of throughput, Residual Energy (RE), dead node count, and live node count. It is simulated using MATrix LABoratory (MATLAB R2018a).