Swarm Intelligence Based Location Estimation for Wireless Sensor Network

Abstract

Object tracking is a noteworthy application in the field of wireless sensor networks that has attracted major Research attention recently. Most object tracking schemes uses prediction scheme to minimize the energy consumption and to maintain low missing rate in a sensor network. However objects need to be localize, when object was found missing during tracking process. In this article, we proposed a swarm intelligence mechanism, such as particle swarm optimization (PSO) to accurately estimate the location of the missing object, using updated object position and velocity and the extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm against the centroid and weighted centroid methods to evaluate its performance in terms of localization error.