Towards the optimal tracking interval management for target tracking wireless sensor networks

We consider the minimization of power consumption in target tracking wireless sensor networks (WSNs) using dynamic modification of tracking interval. In this context, we first analyze the performance of such networks, using a quantitative mathematical analysis. Then we calculate an upper bound for the achievable improvement in total power consumption, when using an adaptive time interval modification algorithm for tracking moving objects with acceleration. Towards this optimum functionality, we propose a novel adaptive algorithm (AHC) to adapt the tracking interval such that it minimizes power consumption while keeping an acceptable accuracy. Simulation results show that using the proposed algorithm, the network has a very good performance with the added advantage of reducing the power consumption significantly (up to 24% and up to 14%) when compared with two existing adaptive methods (PaM and AEC, respectively). Moreover, according to simulation results, proposed algorithm acts relatively (about 28%) better than mentioned adaptive schemes in achieving the upper bound power conservation.