WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing

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 i...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-9
Main Authors: Liu, Zhouzhou, Wang, Fubao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Chaos theory
Compressing
Compression ratio
Data compression
Detection
Energy consumption
Engineering
Linear programming
Microseisms
Perception
Reconstruction
Sensors
Signal processing
Signal reconstruction
Sparsity
Wireless communications
Wireless networks
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/187095