An approximate dynamic programming approach to a communication constrained sensor management problem

Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental trade-off between the value of information contained in a distributed set of measurements versus the energy costs of acquiring measurements, fusing them into a model of uncertainty, and transmitting the resulting model. Communications is commonly the highest contributor among these costs, typically by orders of magnitude. Failure to consider this trade-off can significantly reduce the operational lifetime of a sensor network. While a variety of methods have been proposed that treat a subset of these issues, the approaches are indirect and usually consider at most a single time step. In the context of object tracking with a distributed sensor network we propose an approximate dynamic programming approach which integrates the value of information and the cost of transmitting data over a rolling time horizon. We formulate this trade-off as a dynamic program, and use an approximation based on a linearization of the sensor model about a nominal trajectory to simultaneously find a tractable solution to the leader node selection problem and the sensor subset selection problem. Simulation results demonstrate that the resulting algorithm can provide similar estimation performance to that of the common most informative sensor selection method for a fraction of the communication cost.