A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
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Taking advantage of the delay tolerance for objects tracking sensor networks, we propose delay-tolerant trajectory compression (DTTC) technique, an efficient and accurate algorithm for in-network data compression. In DTTC, each cluster head compresses the movement trajectory of a moving object by a compression function and reports only the compression parameters, which drastically reduces the total amount of data communications required for tracking operations. DTTC supports a broad class of movement trajectories using two techniques, DC-compression and SWcompression, which are designed to minimize the total number of segments to be compressed. Furthermore, we propose an efficient trajectory segmentation scheme, which helps both compression techniques to compress movement trajectory more accurately at less computation cost. An extensive simulation has been conducted to compare DTTC with competing prediction-based tracking technique, DPR [14]. Simulation results show that DTTC exhibits superior performance in terms of accuracy, communication cost and computation cost and soundly outperforms DPR with all types of movement trajectories.