Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
This addresses the trade-off between tracking accuracy and energy consumption in sensor networks, but it is incremental as it builds on existing POMDP frameworks with a new RL method.
The paper tackles the problem of tracking an intruder in energy-efficient sensor networks by developing a reinforcement learning algorithm based on Upper Confidence Tree Search to optimize sensor scheduling, showing through simulations that it performs and scales well with increasing state and action spaces.
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.