Node Localization in Wireless Sensor Network by Ant Lion Optimization

Node location is a critical demand in some popularity of Wireless Sensor Network (WSN) applications. This paper proposes a node location identification in WSN based on a combined Ant Lion Optimizer (ALO) with a typical model of localization. The fitness function is modeled mathematically based on estimating distances of the WSN nodes. The updating solutions of the population are figured out for position correcting to improve the node positioning accuracy. The effects of parameters like node density and communicating range is verified in the experiments to evaluate the performance of the proposed method in terms of concerning average localization error and success ratio. Compared to the results of the test with Cuckoo Search (CS) and Particle Swarm Optimization (PSO) shows that the proposed approach effectively offers a better competitor in finding location accuracy.