A Storage Management for Mining Object Moving Patterns in Object Tracking Sensor Networks

Authors:
Chih-Chieh Hung;Wen-Chih Peng
Affiliations:
National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

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Abstract

One promising application of sensor networks is object tracking. Because the movements of the tracked objects usually show repeating patterns, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. To ensure the quality of moving patterns, we develop a storage management to facilitate mining object moving patterns. Specifically, we explore load-balance feature to store more moving data for mining moving patterns. Once a storage of a cluster head is occupied by moving data, we devise a replacement strategy to replace the less informative patterns. Simulation results show that HTM with storage management is able not only to increase the accuracy of predition but also to save more energy in tracking objects.