標題: Exploring group moving pattern for an energy-constrained object tracking sensor network
作者: Tsai, Hsiao-Ping
Yang, De-Nian
Peng, Wen-Chih
Chen, Ming-Syan
交大名義發表
National Chiao Tung University
關鍵字: OTSN;grouping;data aggregation;prediction
公開日期: 2007
摘要: 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 predict ion-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.
URI: http://hdl.handle.net/11536/136523
ISBN: 978-3-540-71700-3
ISSN: 0302-9743
期刊: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS
Volume: 4426
起始頁: 825
結束頁: +
顯示於類別:會議論文