Secure multiple target tracking based on clustering intersection points of measurement circles in wireless sensor networks

The problem of tracking multiple targets simultaneously using a wireless sensor network is studied in this paper. We introduce a new algorithm, based only on the received signal power measurements, to estimate the location of multiple indistinguishable targets. For each node, a circle centered at the location of the node with a radius equal to the estimated distance between the node and the nearest target is drawn. The intersection points of all these measurement circles are calculated and clustered using a density-based clustering algorithm. The centroid of each generated cluster can be a candidate location, corresponding to a target. In order to choose the best candidate locations, we introduce a new robust criterion, which is capable of dealing with the problem of malicious nodes. Besides, the selected candidate is given to the Gauss–Newton iterative search method, which can increase the accuracy of tracking. We also propose three different approaches for reducing the effect of malicious nodes on the accuracy of tracking. Furthermore, a scheme is proposed for identifying the malicious nodes. We demonstrate the robustness and accuracy of our proposed tracking algorithm via simulation results and compare our results with the Multi-resolution search algorithm and the Expectation–Maximization algorithm.