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Mathematical Problems in Engineering
Volume 2015, Article ID 187095, 9 pages
Research Article

WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

1School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2Xi’an Aeronautical University, Xi’an 710077, China

Received 2 March 2015; Revised 30 April 2015; Accepted 3 May 2015

Academic Editor: Ming-Hung Hsu

Copyright © 2015 Zhouzhou Liu and Fubao Wang. 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.


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.