TY - GEN
T1 - DTTC
T2 - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
AU - Xu, Yingqi
AU - Lee, Wang Chien
PY - 2006/12/15
Y1 - 2006/12/15
N2 - 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 SW-compression, 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.
AB - 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 SW-compression, 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.
UR - http://www.scopus.com/inward/record.url?scp=33845417175&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845417175&partnerID=8YFLogxK
U2 - 10.1109/SUTC.2006.1636210
DO - 10.1109/SUTC.2006.1636210
M3 - Conference contribution
AN - SCOPUS:33845417175
SN - 0769525539
SN - 9780769525532
T3 - Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
SP - 436
EP - 443
BT - Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
Y2 - 5 June 2006 through 7 June 2006
ER -