A Parallel High Speed Lossless Data Compression Algorithm in Large-Scale Wireless Sensor Network. (11th June 2015)

• Record Type: Journal Article
• Authors: Zhou, Bin; Jin, Hai; Zheng, Ran
• Other Names: Ma Maode Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-06-11
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/795353
• Languages: English
• ISSNs: 1550-1329

Abstract

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.