Investigating the Impact of Kalman Filter to Minimize the Localization Error in Wireless Sensor Networks

Wireless Sensor Networks (WSN) comprise sensors that are spatially distributed, collecting information and reporting the data back to the control module. Knowing the sensor (node) positions is necessary for most applications, and the sensor positions are estimated using Localization algorithms. This paper proposes a strategy that uses the Kalman Filter (KF) approach with the proposed range free centroid localization algorithm to increase the accuracy of any unknown node's predicted position in a network. The parameters, namely nodes, network size, communication range, and deployment of the network, have been varied, and the performance of the proposed system comprising the range free centroid localization algorithm with Kalman filter is studied. Combining the Kalman filter with the proposed range free centroid localization algorithm enhances the methodology and increases the success rate in the presence of measurement errors. The simulation results demonstrate that the suggested technique enhances the location accuracy of unknown nodes.