Compressing moving object trajectory in wireless sensor networks

Yingqi Xu, Wang Chien Lee

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Some object tracking applications can tolerate delays in data collection and processing. Taking advantage of the delay tolerance, we propose an efficient and accurate algorithm for in-network data compression, called delay-tolerant trajectory compression (DTTC). In DTTC, a cluster-based infrastructure is built within the network. Each cluster head compresses an object's movement trajectory detected within its cluster by a compression function. Rather than transmitting all sensor readings to the sink node, the cluster head communicates only the compression parameters, which not only provide the sink node expressive yet traceable models about the object movements, but also significantly reduce the total amount of data communication required for tracking operations. DTTC supports a broad class of movement trajectories using two proposed techniques, DC-compression and SW-compression, and an efficient trajectory segmentation scheme, which are designed for improving the trajectory compression accuracy at less computation cost. Moreover, we analyze the underlying cluster-based infrastructure and mathematically derive the optimum cluster size, aiming at minimizing the total communication cost of the DTTC algorithm. An extensive simulation has been conducted to compare DTTC with competing prediction-based tracking technique, DPR [28]. 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.

Original languageEnglish (US)
Pages (from-to)151-174
Number of pages24
JournalInternational Journal of Distributed Sensor Networks
Issue number2
StatePublished - Apr 2007

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Networks and Communications


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