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Optimization approaches to dynamic routing of measurements and models in a sensor network object tracking problem

Inter-sensor communication often comprises a significant portion of energy expenditures in a sensor network as compared to sensing and computation. We discuss an integrated approach to dynamically routing measurements and models in a sensor network. Specifically, we examine the problem of tracking objects within a region wherein the responsibility for combining measurements and updating a posterior state distribution is assigned to a single sensor at any given time step. The so called leader node may change over time. Sensor nodes communicate for two reasons: firstly, measurements of target state are transmitted from sensors to the current leader node for incorporation into the state estimate model; secondly, the state model is transmitted between sensors when the leader node changes. The trade-off between these two types of communication is of primary importance to dynamic selection of the leader node. We propose an algorithm based on a dynamic programming roll-out formulation of the minimum cost problem. We obtain a cost function which can be efficiently minimized by simplifying the problem to that of an open loop feedback controller which is an upper bound to the optimal cost. We present empirical results which compare methods previously proposed in the literature to the algorithm presented here.