On localized prediction for power efficient object tracking in sensor networks

Yingqi Xu, Wang-chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

105 Scopus citations

Abstract

Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient 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.

Original languageEnglish (US)
Title of host publicationProceedings - 23rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages434-439
Number of pages6
ISBN (Electronic)0769519210, 9780769519210
DOIs
StatePublished - Jan 1 2003
Event23rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2003 - Providence, United States
Duration: May 19 2003May 22 2003

Publication series

NameProceedings - 23rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2003

Other

Other23rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2003
Country/TerritoryUnited States
CityProvidence
Period5/19/035/22/03

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

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