A storage management for mining object moving patterns in object tracking sensor networks

Chih Chieh Hung, Wen Chih Peng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One promising application of sensor networks is object tracking. Because the movements of the tracked objects usually show repeating patterns, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. To ensure the quality of moving patterns, we develop a storage management to facilitate mining object moving patterns. Specifically, we explore load-balance feature to store more moving data for mining moving patterns. Once a storage of a cluster head is occupied by moving data, we devise a replacement strategy to replace the less informative patterns. Simulation results show that HTM with storage management is able not only to increase the accuracy of predition but also to save more energy in tracking objects.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)
PublisherIEEE Computer Society
Pages252-256
Number of pages5
ISBN (Print)0769527493, 9780769527499
DOIs
StatePublished - 1 Jan 2006
Event2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Publication series

NameProceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)

Conference

Conference2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
CountryChina
CityHong Kong
Period18/12/0622/12/06

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