A Clustering Approach Based on Genetic Algorithm for Wireless Sensor Network Localization

Exact localization of randomly deployed sensor nodes is critically important in Wireless Sensor Networks (WSNs) setup especially for tracking and monitoring purposes. With this advantage, the preferred approach can be applied to obtain the location of a particular node based on all existing information known in the WSN. In this paper, the normal Genetic Algorithm (GA) will be extended by a clustering approach which contributes to the increase in localization accuracy. The number of clusters constructed in the network helps to improve the accuracy. The selection of the initial cluster center is done in order to lower the intensity of distant points. Simulation results show the proposed approach can reach better results in comparison with the centroid and Distance Vector-Hop (DV-Hop) methods.