By Topic

Mining Region-Based Movement Patterns for Energy-Efficient Object Tracking in Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Vincent S. Tseng ; Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan ; Ming Hua Hsieh ; Kawuu W. Lin

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important issue in OTSNs is the energy saving strategy for object tracking and most existing solutions are based on statistical methods. In this paper, we propose a data mining-based approach for energy-efficient object tracking in OTSNs. First, a data mining methodology named RM-mine is proposed for discovering the region-based movement patterns of moving objects in an OTSN. Moreover, we also propose the corresponding prediction strategies for tracking objects in energy-efficient way. Through empirical evaluations on various simulation conditions, RM-mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.

Published in:

2008 Eighth International Conference on Intelligent Systems Design and Applications  (Volume:3 )

Date of Conference:

26-28 Nov. 2008