Approximate Dynamic Programming for Communication-Constrained Sensor Network Management

TLDR

Resource management in distributed sensor networks is challenging because the value of information must be balanced against energy costs, especially communication, and ignoring this tradeoff can drastically shorten network lifetime, while existing methods are indirect and typically consider only a single time step. We propose an approximate dynamic programming approach for object tracking that integrates the value of information with transmission costs over a rolling time horizon. The tradeoff is formulated as a dynamic program and solved using a linearization of the sensor model about a nominal trajectory to jointly determine leader node and sensor subset selections. Simulations show the algorithm achieves estimation performance comparable to the most informative sensor selection method while reducing communication cost to a fraction of that method.

Abstract

Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental tradeoff between the value of information contained in a distributed set of measurements versus the energy costs of acquiring measurements, fusing them into the conditional probability density function (pdf) and transmitting the updated conditional pdf. Communications is commonly the highest contributor among these costs, typically by orders of magnitude. Failure to consider this tradeoff 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 that integrates the value of information and the cost of transmitting data over a rolling time horizon. We formulate this tradeoff 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.