Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements

WANG Kai; LIU Yulin; WAN Qun; JING Xiaojun

doi:10.1049/cje.2015.04.027

Volume 24 Issue 2

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WANG Kai, LIU Yulin, WAN Qun, et al., “Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 388-392, 2015, doi: 10.1049/cje.2015.04.027

Citation:

WANG Kai, LIU Yulin, WAN Qun, et al., “Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 388-392, 2015, doi: 10.1049/cje.2015.04.027

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Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements

doi: 10.1049/cje.2015.04.027

1.
Chongqing Communication Institute, Chongqing 400035, China;
2.
China Electronic System Engineering Company, Shenyang 110005, China;
3.
University of Electronic Science and Technology of China, Chengdu 610054, China;
4.
Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: This work is supported by the Program for New Century Excellent Talents in University (No.NCET-11-0873), the Program for Innovative Research Team in University of Chongqing (No.KJTD201343), the Key Project of Chongqing Natural Science Foundation (No.CSTC2011BA2016), and the Program for Fundamental and Advanced Research of Chongqing (No.CSTC2013JCYJA40045).

Publish Date: 2015-04-10

Abstract

Abstract

In Wireless sensor networks (WSNs), missed measurements may be caused by the sensor malfunction and interruption of communication between sensor nodes. The feasibility of exact recovery of WSNs data with missed measurements is analyzed in the framework of compressed sensing. A new incomplete measurement model was developed and the data reconstruction algorithm was proposed. The required number of the missing measurements and the sparsity condition of network data are found for exact compressed sensing ofWSNs data. Theoretical derivation shows that aWSNs data of length N with no more than M/(log(N/M)+1) nonzero coefficients can be exactly recovered with M Gaussian measurements, provided that fraction of the missed measurements is less than a quarter of the Restricted isometry property (RIP) constant squared. Simulation results validate the theoretical results.
- Wireless sensor networks,
- Compressed sensing,
- Missed measurements,
- Data reconstruction

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References(16)

References

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