Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation

As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

[1]  Abraham Lempel,et al.  A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.

[2]  Naima Kaabouch,et al.  Compressive sensing: Performance comparison of sparse recovery algorithms , 2018, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[3]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[4]  Mukesh Singhal,et al.  Barrier coverage of WSNs with the imperialist competitive algorithm , 2017, The Journal of Supercomputing.

[5]  Zilong Zou,et al.  Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring , 2015, IEEE Sensors Journal.

[6]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[7]  Sungwon Lee EE-Systems.  Compressed Sensing and Routing in Multi-Hop Networks , 2007 .

[8]  R. Nowak,et al.  Compressed Sensing for Networked Data , 2008, IEEE Signal Processing Magazine.

[9]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[10]  Liang Xiao,et al.  Sparse Representations of Images by a Multi-component Gabor Perception Dictionary: Sparse Representations of Images by a Multi-component Gabor Perception Dictionary , 2009 .

[11]  Chen Hong,et al.  An Algorithm of Data Aggregation Based on Data Compression for Sensor Networks , 2006 .

[12]  V. S. Saroja,et al.  A survey on compressive sensing , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[13]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[14]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[15]  Shutao Li,et al.  A Survey on Compressive Sensing: A Survey on Compressive Sensing , 2009 .

[16]  Xiaodai Dong,et al.  Multiple Access and Data Reconstruction in Wireless Sensor Networks Based on Compressed Sensing , 2013, IEEE Transactions on Wireless Communications.

[17]  Naima Kaabouch,et al.  A performance comparison of measurement matrices in compressive sensing , 2018, Int. J. Commun. Syst..

[18]  Sun Yu.  Sparse Representations of Images by a Multi-component Gabor Perception Dictionary , 2008 .

[19]  François Ingelrest,et al.  SensorScope: Out-of-the-Box Environmental Monitoring , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[20]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[21]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[22]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[23]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[24]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[25]  Rajkumar Buyya,et al.  FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments , 2017, J. Parallel Distributed Comput..

[26]  Toshiyuki Tanaka,et al.  A User's Guide to Compressed Sensing for Communications Systems , 2013, IEICE Trans. Commun..