On mining moving patterns for object tracking sensor networks

Wen Chih Peng, Yu Zen Ko, Wang Chien Lee

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

28 Scopus citations

Abstract

In this paper, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. Specifically, we use a variable memory Markov model to exploit the dependencies among object movements. Furthermore, due to the hierarchical nature of HTM, multi-resolution object moving patterns are provided. The proposed HTM is able to accurately predict the movements of objects and thus reduces the energy consumption for object tracking. Simulation results show that HTM not only is able to effectively mine object moving patterns but also save energy in tracking objects.

Original languageEnglish (US)
Title of host publication7th International Conference on Mobile Data Management, 2006. MDM 2006
DOIs
Publication statusPublished - Nov 21 2006
Event7th International Conference on Mobile Data Management, 2006. MDM 2006 - Nara, Japan
Duration: May 10 2006May 12 2006

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2006
ISSN (Print)1551-6245

Other

Other7th International Conference on Mobile Data Management, 2006. MDM 2006
CountryJapan
CityNara
Period5/10/065/12/06

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Peng, W. C., Ko, Y. Z., & Lee, W. C. (2006). On mining moving patterns for object tracking sensor networks. In 7th International Conference on Mobile Data Management, 2006. MDM 2006 [1630577] (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2006). https://doi.org/10.1109/MDM.2006.114