  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 state-of-the-art algorithm in the
literature, the curse of dimensionality renders the problem intractable. In
this paper, we formulate the Intrusion Detection (ID) problem with a suitable
state-action space in the framework of POMDP and develop a Reinforcement
Learning (RL) algorithm utilizing the Upper Confidence Tree Search (UCT) method
to solve the ID problem. Through simulations, we show that our algorithm
performs and scales well with the increasing state and action spaces.
