Abstract
Localization is one of the challenges in achieving reliable communication in Wireless Sensor Networks (WSN). Estimating a sensor's node's position is known as Localization. Nonlinear version of Kalman filtering is known as the Extended Kalman Filter which deals with the case governed by the nonlinear stochastic differential equations, Extended kalman filter is nonlinear filter having their own problem of consistency. In this paper proposed efficient localization algorithm that enables sensor nodes to estimate their location with high accuracy. The purpose of this paper is to develop the particle swarm optimization assisted Extended Kalman Filter (PSO-EKF) for Localization in WSN. Performance evaluation for the PSO-EKF as compared to the conventional KF could be better for time critical applications.
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