A Parallel High Speed Lossless Data Compression Algorithm in Large-Scale Wireless Sensor Network

In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by the energy and bandwidth, a large-scale wireless sensor network displays the disadvantages of fusing the data collected by the sensor nodes a...

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Vydáno v:	International journal of distributed sensor networks Ročník 2015; číslo 6; s. 795353
Hlavní autoři:	Zhou, Bin, Jin, Hai, Zheng, Ran
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London, England Hindawi Publishing Corporation 01.01.2015 SAGE Publications Sage Publications Ltd. (UK) John Wiley & Sons, Inc Wiley
Témata:	Algorithms Compressing Computer science Data compression Engineering research Environmental monitoring High speed Internet Matching Methods Microelectromechanical systems Product introduction R&D Remote sensors Research & development Sensors Wavelet transforms Wireless networks Wireless sensor networks
ISSN:	1550-1329, 1550-1477, 1550-1477
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Abstract	In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by the energy and bandwidth, a large-scale wireless sensor network displays the disadvantages of fusing the data collected by the sensor nodes and compressing them at the sensor nodes. Thus the goals of reduction of bandwidth and a high speed of data processing should be achieved at the second-level sink nodes. Traditional compression technology is unable to appropriately meet the demands of processing massive sensor data with a high compression rate and low energy cost. In this paper, Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm, making use of the CUDA framework at the second-level sink node is presented. The core idea of PMLZSS algorithm is parallel matrix matching. PMLZSS algorithm divides the data compression files into multiple compressed dictionary window strings and prereading window strings along the vertical and horizontal axes of the matrices, respectively. All of the matrices are parallel matched in the different thread blocks. Compared with LZSS and BZIP2 on the traditional serial CPU platforms, the compression speed of PMLZSS increases about 16 times while, for BZIP2, the compression speed increases about 12 times when the basic compression rate unchanged.
AbstractList	In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by the energy and bandwidth, a large-scale wireless sensor network displays the disadvantages of fusing the data collected by the sensor nodes and compressing them at the sensor nodes. Thus the goals of reduction of bandwidth and a high speed of data processing should be achieved at the second-level sink nodes. Traditional compression technology is unable to appropriately meet the demands of processing massive sensor data with a high compression rate and low energy cost. In this paper, Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm, making use of the CUDA framework at the second-level sink node is presented. The core idea of PMLZSS algorithm is parallel matrix matching. PMLZSS algorithm divides the data compression files into multiple compressed dictionary window strings and prereading window strings along the vertical and horizontal axes of the matrices, respectively. All of the matrices are parallel matched in the different thread blocks. Compared with LZSS and BZIP2 on the traditional serial CPU platforms, the compression speed of PMLZSS increases about 16 times while, for BZIP2, the compression speed increases about 12 times when the basic compression rate unchanged.
Audience	Academic
Author	Zhou, Bin Zheng, Ran Jin, Hai
Author_xml	– sequence: 1 givenname: Bin surname: Zhou fullname: Zhou, Bin organization: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China hust.edu.cn – sequence: 2 givenname: Hai surname: Jin fullname: Jin, Hai organization: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China hust.edu.cn – sequence: 3 givenname: Ran surname: Zheng fullname: Zheng, Ran organization: Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China hust.edu.cn
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Cites_doi	10.3724/sp.j.1001.2010.03518 10.1007/s11036-011-0302-x 10.1109/icacia.2009.5361111 10.1109/saint.2008.71 10.1109/snpa.2003.1203366 10.1109/inpar.2012.6339599 10.1109/contel.2007.381840 10.1109/waina.2009.192 10.1109/icde.2003.1260811 10.1109/TPDS.2012.216 10.1166/sl.2013.2671 10.1109/aero.2012.6187255 10.1109/sasp.2011.5941091 10.14778/1920841.1920927 10.1109/dcc.2011.60 10.1007/978-3-540-76858-6_70 10.1109/TIT.1977.1055714 10.1155/2012/728521 10.1109/79.985684 10.1007/s10586-005-6186-4 10.1145/1127777.1127824
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Copyright	Copyright © 2015 Bin Zhou et al. 2015 Bin Zhou et al. COPYRIGHT 2015 Sage Publications Ltd. (UK) Copyright © 2015 Bin Zhou et al. Bin Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright_xml	– notice: Copyright © 2015 Bin Zhou et al. – notice: 2015 Bin Zhou et al. – notice: COPYRIGHT 2015 Sage Publications Ltd. (UK) – notice: Copyright © 2015 Bin Zhou et al. Bin Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet	In large-scale wireless sensor networks, massive sensor data generated by a large number of sensor nodes call for being stored and disposed. Though limited by...
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SubjectTerms	Algorithms Compressing Computer science Data compression Engineering research Environmental monitoring High speed Internet Matching Methods Microelectromechanical systems Product introduction R&D Remote sensors Research & development Sensors Wavelet transforms Wireless networks Wireless sensor networks
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Title	A Parallel High Speed Lossless Data Compression Algorithm in Large-Scale Wireless Sensor Network
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