Mobility profiling using Markov chains for tree-based object tracking in wireless sensor networks

Li-Hsing Yen*, Chia Cheng Yang

*Corresponding author for this work

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

13 Scopus citations

Abstract

Object tracking in wireless sensor networks is to track moving objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object's move. Existing tree construction algorithms all require a mobility profile that is obtained based on historical statistics. In this paper, we propose an analytic estimate of such mobility profile based on Markov-chain model. This estimate replaces otherwise experimental process that collects statistical data. Simulation results confirm the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
Pages220-225
Number of pages6
DOIs
StatePublished - 15 Dec 2006
EventIEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Taichung, Taiwan
Duration: 5 Jun 20067 Jun 2006

Publication series

NameProceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Volume2006 I

Conference

ConferenceIEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
CountryTaiwan
CityTaichung
Period5/06/067/06/06

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    Yen, L-H., & Yang, C. C. (2006). Mobility profiling using Markov chains for tree-based object tracking in wireless sensor networks. In Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006 (pp. 220-225). [1636277] (Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing; Vol. 2006 I). https://doi.org/10.1109/SUTC.2006.92