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Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(1): 83-91
Xiaoxia Song and Yong Li, "Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 83-91, Jan. 2018. doi: 10.1109/JAS.2017.7510448
Citation: Xiaoxia Song and Yong Li, "Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 83-91, Jan. 2018. doi: 10.1109/JAS.2017.7510448
shu

Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix

doi: 10.1109/JAS.2017.7510448

  • Department of Computer Science, Shanxi Datong University, Datong 037009, China

Funds:

the National Natural Science Foundation of China 61307121

ABRP of Datong 2017127

the Ph.D.'s Initiated Research Projects of Datong University 2013-B-17

the Ph.D.'s Initiated Research Projects of Datong University 2015-B-05

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    Abstract
  • A great challenge faced by wireless sensor networks (WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However, it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property (StRIP) via regular low density parity check (RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that StRIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

     

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