Clustering object moving patterns for prediction-based object tracking sensor networks

Chih Chieh Hung*, Wen-Chih Peng

*Corresponding author for this work

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

11 Scopus citations

Abstract

Prior works have shown that probabilistic suffix trees (PST) could predict accurately the moving behaviors of objects for prediction-based object tracking sensor networks. However, maintaining PSTs for objects incurs a considerable amount of storage spaces for resource-constrained sensor nodes. In this paper, we derive a distance function between two PSTs and propose an algorithm to determine the similarity between them. By the distance between PSTs, we propose a clustering algorithm to partition objects with similar moving behaviors into groups. Furthermore, for each group, one PST is selected to predict movements of objects within one group. Experimental results show that our proposed approaches not only effectively reduce the storage cost but also provide good prediction accuracy.

Original languageEnglish
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1633-1636
Number of pages4
DOIs
StatePublished - 1 Dec 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2 Nov 20096 Nov 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CountryChina
CityHong Kong
Period2/11/096/11/09

Keywords

  • Clustering
  • Group mobility
  • Object tracking

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    Hung, C. C., & Peng, W-C. (2009). Clustering object moving patterns for prediction-based object tracking sensor networks. In ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 (pp. 1633-1636). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1645953.1646191