Efficient target tracking in directional sensor networks with selective target area’s coverage

Wireless sensor networks (WSNs) are employed in a variety of applications. One of the key applications of WSNs, which gained much attention, is the target tracking. Directional sensor networks (DSNs) are a subset of WSNs with some unique characteristics. Since optimizing the tracking system under the energy and coverage constraints in DSNs is of paramount importance, in this paper, we introduce a reliable algorithm for tracking mobile targets using directional WSNs. First, by selecting a minimum set of boundary and borderline sensor nodes, we achieve the desired coverage for an incoming detection. Second, for both deterministic ordered and random node deployments, we propose an efficient mechanism for determining the minimal interior sensor nodes that should be activated. Doing so, the network lifetime can be maximized by the employment of much fewer sensor nodes. Third, we use a geometric method for collecting data using two active sensors at a time. Accordingly, target position is estimated using the extended Kalman filter (EKF). Finally, we compare the proposed algorithm with a genetic algorithm and present the comparative simulation results of the EKF and the random walk. The results demonstrate the effectiveness of our proposed scheme in terms of the energy efficiency, coverage, and tracking accuracy.