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WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

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  • Zhouzhou Liu
  • Fubao Wang

Abstract

For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS) used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR) algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.

Suggested Citation

  • Zhouzhou Liu & Fubao Wang, 2015. "WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, June.
    • Handle: RePEc:hin:jnlmpe:187095
    DOI: 10.1155/2015/187095
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