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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Bibliographic Details
Published in:International journal of distributed sensor networks Vol. 2015; no. 6; p. 795353
Main Authors: Zhou, Bin, Jin, Hai, Zheng, Ran
Format: Journal Article
Language:English
Published: London, England Hindawi Publishing Corporation 01.01.2015
SAGE Publications
Sage Publications Ltd. (UK)
John Wiley & Sons, Inc
Wiley
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1550-1329
1550-1477
1550-1477
DOI:10.1155/2015/795353