Abstract—It is imperative to reduce load of the underground
cable channel in coming Mining of Things for thousands of
sensors. Gas data, for example, acts an essential role in mass
time-continuous data sets of coalmine monitoring systems, we
propose a multi-scale 1-bit compressive sensing algorithm in
this paper to effectively compress data according the statistical
properties ‘regular pattern’ of gas data sequence. The
algorithm divides input into non-uniform intervals according
to the prior attention of the gas monitoring information, then
signal decision threshold and the compressed scales depend on
the different attention in order to achieve a large scales of
compression ratio on redundant data and as much as possible
to maintain the sensitive information, comparing with the
traditional 1-bit compressive sensing which brings overload
quantization distortion during uniform quantization.
Satisfactory results obtained by simulation and actual field
applications show, which provides a useful reference to similar
real monitoring data compression acquisition with compressed
sampling.
Index Terms—Mining of things (MoT), multi-scale 1-bit
compressed sensing (MS 1-bitCS), coalmine safety.
Xu Yonggang and Hua Gang are with School of Information &
Electronic Engineering, China University of Mining and Technology,
Xuzhou, Jiangsu, 221008, China (e-mail: feilongxyg@163.com,
ghua3323@163.com).
Zhang Yi is with Sience & Thechnology Center, Huaibei Coalmine Grp.
Ltd., Huaibei, Anhui, China (e-mail: zy@hbcoal.com).
Cite:Xu Yonggang, Zhang Yi, and Hua Gang, "Multi-Scale 1-Bit Compressed Sensing Algorithm and Its Application in Coalmine Gas Monitoring System," International Journal of Machine Learning and Computing vol.3, no. 5, pp. 408-412, 2013.