Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks

TL;DR

This paper introduces a reinforcement learning approach using Upper Confidence Tree Search to efficiently schedule sensors for intruder tracking, balancing energy consumption and tracking accuracy in sensor networks.

Contribution

It formulates the intruder detection problem as a POMDP and develops a scalable RL algorithm to optimize sensor activation, addressing the curse of dimensionality.

Findings

The RL algorithm performs well in simulations.

It scales effectively with larger state and action spaces.

It balances energy efficiency and tracking accuracy.

Abstract

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.