Salp Swarm Optimization Algorithm Based Cluster Head Selection in Wireless Sensor Network

Abstract

Sensors are the integral part of the upcoming technologies like wireless sensor networks (WSNs), internet of things (IOTs) and adhoc networks etc.  Due to the great expansion of sensor technology in various application areas such as health monitoring, domestic automation systems, defense operations, environment monitoring, heavy data streaming applications and surveillance systems etc., the sensors become attractive research area. Sensor nodes (SNs) are restricted to source restraints like fixed energy availability, lifetime stability of the network, data packets failure, computation time and throughput of the network. The quality functioning of wireless sensor network (WSN) depends on these resource constraints and can be improved by utilizing various clustering and routing methods while designing WSN. Cluster Head (CH) plays important role in clustering and can be elected using various optimization algorithms. In this article, a Salp Swarm Optimization Algorithm (SSOA) is presented for CH selection in WSN. CH is elected dynamically for hieratical clustering technique and performance of the network is optimized using SSOA. The performance of the network is evaluated using SSOA and is simulated in MATLAB and compared with the performance of the Ant-lion Optimization (ALO) and Genetic Algorithm (GA) for the presented network scenario. The quality of the service of the network is evaluated in terms of the network lifetime, computation time, energy consumption, throughput and packet loss. The research contributes longer network lifetime, lesser energy consumption, minimum packet loss, less execution time for CH election and higher amount of throughput. The simulated results performs better than existing ALO & GA  89% higher network lifetime, 3% CH selection time computation,  60% better throughput, only 3% packet loss and 3% energy consumption as compared to ALO & GA.