Energy efficient fast predictor for WSN-based target tracking

Abstract

Power source replacement of the sensor nodes, which are once deployed in the network area, is generally difficult. So, energy saving is one of the most important issues for object tracking in wireless sensor networks. To reduce the consumed energy and prolong the network lifetime, the nodes surrounding the mobile object should be responsible for sensing the target. The number of participant nodes in target tracking can be reduced by an accurate prediction of the object location. In this paper, we present a fast energy efficient with high-accuracy target tracking scheme which is based on location prediction. The missing rate of proposed predictor is very low in comparison with other predictors especially in a random waypoint mobility model in which after pause time, the three main parameters direction, velocity and, acceleration would be changed. The accuracy of predictor has a direct effect on missing rate and so strongly reduces the consumed energy. Additionally, a new node selection criterion is proposed in which minimum nodes surrounding the object are wakened and track the object. Simulation results show that our proposed predictor has low consumed energy and complexity in comparison with Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) predictors.