Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization

Abstract

Prime goal of WSN deployment in large specific area aims to sense the environment and execute the defined application with the help of essential location information of the devices. Through localization technique, location information is assigned to the unknown devices within the area of interest. Due to its definite solution capabilities with fast convergence rate, bio inspired application become popular to solve numerous applications in the field of wireless sensor network (WSN) applications. In this paper, a newly developed meta- heuristic algorithm based on the social behavior of chickens named as chicken swarm optimization (CSO) is proposed to solve the WSN node localization problem. Two performance metrics which are node precision and computation time are investigated using three different bio inspired algorithms that are particle swarm optimization (PSO), binary particle swarm optimization (BPSO) and penguin search optimization algorithm (PeSOA) respectively. Results are demonstrated using simulation graph where CSO performs more precise accuracy having a ratio of 55% over PSO and BPSO and 10% over PeSOA. For computation time, proposed algorithm performs a computation time that is shorter by 30% than PeSOA as well as 50 and 40% than PSO and BPSO, respectively.