Secure Tracking in Sensor Networks

Target tracking is a canonical issue in sensor networks research. However, tracking security has gained little or no attention. Once a sensor node is compromised, it will be able to inject false location information into the network, and those nodes receiving such information will suffer greatly in terms of tracking precision. This paper, to the best of our knowledge, is the first to explore the topic of security in the context of Bayesian tracking for sensor networks. We propose to activate more than one nodes at each time step, and use a relaxation labeling algorithm to detect malicious nodes whose reports are then removed. Simulations based on both linear and nonlinear motion models demonstrate that out algorithm works better than simply averaging over the results based on the redundant sets of nodes.