Research Article
Data Compression of Wireless Sensor Network Employing Kalman Filter and QC-LDPC Codes
@INPROCEEDINGS{10.4108/icst.chinacom.2014.256354, author={Jian Zheng and Hongxia Bie and Dijia Xu and Chunyang Lei and Xuekun Zhang}, title={Data Compression of Wireless Sensor Network Employing Kalman Filter and QC-LDPC Codes}, proceedings={9th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={1}, keywords={data compression; kalman filter; quasi-cyclic low-density parity-check codes; the moving average model; the linear regression model;}, doi={10.4108/icst.chinacom.2014.256354} }- Jian Zheng
Hongxia Bie
Dijia Xu
Chunyang Lei
Xuekun Zhang
Year: 2015
Data Compression of Wireless Sensor Network Employing Kalman Filter and QC-LDPC Codes
CHINACOM
IEEE
DOI: 10.4108/icst.chinacom.2014.256354
Abstract
Considering the fact that the wireless sensor networks (WSNs) need to maintain a long lifetime, there is a great demand to decrease energy dissipation of the sensor. Data compression is an efficient method to solve the problem. This paper proposes a practical and efficient data compression algorithm with high compression and noise-resisted features, in which the quasi-cyclic low-density parity-check (QC-LDPC) codes and the Kalman filters are used to compress the transition data of the sensors and to provide the side information for the joint decoding, respectively. The simulation results prove that the algorithm provides an outstanding performance than the famous syndrome techniques.
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