Exploring group moving pattern for an energy-constrained object tracking sensor network

Hsiao Ping Tsai*, De Nian Yang, Wen-Chih Peng, Ming Syan Chen

*Corresponding author for this work

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

5 Scopus citations

Abstract

In this paper, we investigate and utilize the characteristic of the group movement of objects to achieve energy conservation in the inherently resource-constrained wireless object tracking sensor network (OTSN). We propose a novel mining algorithm that consists of a global mining and a local mining to leverage the group moving pattern. We use the VMM model together with Probabilistic Suffix Tree (PST) in learning the moving patterns, as well as Highly Connected Component (HCS) that is a clustering algorithm based on graph connectivity for moving pattern clustering in our mining algorithm. Based on the mined out group relationship and the group moving patterns, a hierarchically prediction-based query algorithm and a group data aggregation algorithm are proposed. Our experiment results show that the energy consumption in terms of the communication cost for our system is better than that of the conventional query/update based OTSN, especially in the case that on-tracking objects have the group moving characteristics.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
Pages825-832
Number of pages8
DOIs
StatePublished - 1 Dec 2007
Event11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China
Duration: 22 May 200725 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4426 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
CountryChina
CityNanjing
Period22/05/0725/05/07

Keywords

  • Data aggregation
  • Grouping
  • OTSN
  • Prediction

Fingerprint Dive into the research topics of 'Exploring group moving pattern for an energy-constrained object tracking sensor network'. Together they form a unique fingerprint.

Cite this