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This paper addresses information-based sensing point selection from a set ofpossible sensing locations, which determines a set of measurement pointsmaximizing the mutual information between the sensor measurements and thevariables of interest. A potential game approach has been applied to addressingdistributed implementation of decision making for cooperative sensor planning.When a sensor network involves a large number of sensing agents, the localutility function for a sensing agent is hard to compute, because the localutility function depends on the other agents' decisions while each sensingagent is inherently faced with limitations in both its communication andcomputational capabilities. Accordingly, a local utility function for eachagent should be approximated to accommodate limitations in informationgathering and processing. We propose an approximation method for a localutility function using only a portion of the decisions of other agents. Thepart of the decisions that each agent considers is called the neighboring setfor the agent. The error induced by the approximation is also analyzed, and tokeep the error small we propose a neighbor selection algorithm that chooses theneighbor set for each agent in a greedy way. The selection algorithm is basedon the correlation information between one agent's measurement selection andthe other agents' selections. Futhermore, we show that a game with anapproximate local utility function has an $\epsilon$-equilibrium and the set ofthe equilibria include the Nash equilibrium of the original potential game. Wedemonstrate the validity of our approximation method through two numericalexamples on simplified weather forecasting and multi-target tracking.

Efficient sensor network planning method using approximate potential game