Mobility Assisted Distributed Tracking in Hybrid Sensor Networks

In this paper, we propose a new mobility assisted tracking (MAT) algorithm for tracking a single target in a hybrid sensor network consisting of both static and mobile nodes. The network is assumed to be partitioned into clusters and cluster heads are formed from a set of high capacity static nodes. One cluster head is selected to perform the tracking task using particle filters at a given time based on the observations received from the nodes belonging to the corresponding cluster. We exploit the node mobility in the hybrid sensor network to dynamically maintain a certain coverage level at the predicted target location at each time. In the proposed MAT algorithm mobile nodes are directed to move towards the predicted target position at each time step if the predicted target position is not covered to the desired coverage level by static nodes. Simulation results show that with the proposed MAT algorithm, an improved performance closer to the PCRLB is achieved with a relatively small number of mobile nodes in the network compared to the scenario when all nodes are static. The proposed scheme is also robust against static node as well as cluster head failures.