Sensor scheduling for energy-efficient target tracking in sensor networks

George K. Atia, Venugopal V. Veeravalli, Jason A. Fuemmeler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5893952
Pages (from-to)4923-4927
Number of pages5
JournalIEEE Transactions on Signal Processing
Volume59
Issue number10
DOIs
StatePublished - Oct 2011

Keywords

  • Dynamic programming
  • Markov models
  • POMDP
  • sensor networks
  • target tracking

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Sensor scheduling for energy-efficient target tracking in sensor networks'. Together they form a unique fingerprint.

Cite this