Mining temporal moving patterns in object tracking sensor networks

Vincent Shin-Mu Tseng*, Kawuu W. Lin

*Corresponding author for this work

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

17 Scopus citations

Abstract

Advances in wireless communication and microelectronic devices technologies have enabled the development of low-power micro-sensors and the deployment of large scale sensor networks. With the capabilities of pervasive surveillance, sensor networks can be very useful in a lot of commercial and military applications for collecting and processing the environmental data. One of the very interesting research issues is the energy saving in object tracking sensor networks (OTSNs). However, most of the past studies focused only on the aspect of movement behavior analysis or location tracking and did not consider the temporal characteristics, which are very critical in OTSNs. In this paper, we propose a novel data mining method named TMP-Mine with a special data structure named TMP-Tree for discovering temporal moving patterns efficiently. To our best knowledge, this is the first study that explores the issue of discovering temporal moving patterns that contain both movement and time interval simultaneously. Through empirical evaluation on various simulation conditions, TMP-Mine is shown to deliver excellent performance in terms of accuracy, execution efficiency, and scalability.

Original languageEnglish
Title of host publicationProceedings - International Workshop on Ubiquitous Data Management, UDM 2005
EditorsK. Tanaka, Y. Zettsu
Pages105-112
Number of pages8
DOIs
StatePublished - 31 Oct 2005
Event1st International Workshop on Ubiquitous Data Management, UDM 2005 - Tokyo, Japan
Duration: 4 Apr 20054 Apr 2005

Publication series

NameProceedings of the International Workshop on Ubiquitous Data Management , UDM 2005

Conference

Conference1st International Workshop on Ubiquitous Data Management, UDM 2005
CountryJapan
CityTokyo
Period4/04/054/04/05

Keywords

  • Data mining
  • Object tracking
  • Sensor data
  • Sensor networks
  • Temporal moving patterns

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