Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks

TL;DR

This paper addresses energy-efficient target tracking in sensor networks by formulating the sensor scheduling problem as a POMDP, exploring models with increasing complexity, and proposing approximate solutions that balance tracking accuracy and energy use.

Contribution

It introduces a POMDP-based framework for sensor scheduling in target tracking, considering various sensing models and developing approximate solutions for complex scenarios.

Findings

Proposed POMDP models for different sensing scenarios.

Developed approximate scheduling algorithms with near-optimal tradeoffs.

Derived lower bounds on energy-tracking performance.

Abstract

In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP), where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing levels of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence the actions of the different sensors, are tightly coupled. Finally, we consider scenarios wherein the target locations and sensors' observations assume values on continuous spaces. Exact solutions are generally intractable even for the simplest models due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques, and in some cases derive lower bounds on the optimal tradeoff curves. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs.