An Intelligent Node Localization Algorithm for Heterogeneous Wireless Sensor Network Based Object Detection and Tracking System

Abstract

Abstract Node localization is the process of determining the location of sensor nodes in the area of operation. To determine the location of the moving object in a heterogeneous wireless sensor network accurately, it is important to know the location of sensor nodes that sense the presence of the object in their vicinity area. Sensor nodes equipped with GPS facility can know their exact location with reference to some point in space. But this makes the network system expensive. So, alternate method of determination of exact location of the nodes are always inevitable. But unfortunately, no such error free method is proposed till date. Hence, it is an open research problem for the researchers. In this paper, we propose an intelligent algorithm based on swarm intelligence for node localization problem. The proposed algorithm is a hybrid swarm intelligence algorithm in which the location estimation error resulted in DV-Hop algorithm is corrected using hybrid Particle Swarm Optimization (PSO)-Grey Wolf Optimization (GWO) algorithm with Poor-for-Change strategy. The performance of the algorithm is evaluated and compared with existing DV-Hop using PSO and GWO based node localization algorithm. It is found that the object localization error is less in case of proposed model in comparison to the above said models.