On Localized Prediction for Power Efficient Object Tracking in Sensor Networks

  • Authors:
  • Yingqi Xu;Wang-Chien Lee

  • Affiliations:
  • -;-

  • Venue:
  • ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
  • Year:
  • 2003

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Abstract

Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-e.cient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and by reducing the amount of long-range transmissions. Performance evaluation, based on mathematical analysis, shows that localized prediction can significantly reduce the power consumption in object tracking sensor networks.