We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP). Even for the simplest model considered in [1], the curse of dimensionality renders the problem intractable. We formulate this problem with a suitable state-action space in the framework of POMDP and develop a reinforcement learning algorithm utilising the Upper Confidence Tree Search (UCT) method to mitigate the state-action space explosion. Through simulations, we illustrate that our algorithm scales well with the increasing state and action space.
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