An improved energy efficient approach for wsn based tracking applications

Tracking systems using a high number of low cost sensor nodes have been proposed for use in diverse applications including civil, military, and wildlife monitoring applications. In tracking applications, each sensor node attempts to send the target's location information to a sink node. Deploying a tracking system with a high number of sensor nodes results in the following limitations: high packet dropping rate, high congestion, transmission delay, and high power-consumption. Data aggregation schemes can reduce the number of messages transmitted over the network, while prediction schemes can decrease the number of activated beacon nodes in the tracking process. Consequently, data aggregation and prediction approaches can reduce the energy consumed during the tracking process. In this paper, we propose and implement an energy efficient approach for WSN-based tracking applications by integrating both a novel data aggregation method with a simple prediction approach. Three metrics are utilized for the evaluation purposes: total number of messages transmitted in the network, overall power-consumption, and the quality of the tracking accuracy. The proposed system is simulated using the NS2 simulation environment.