Title

Data Gathering Based on Regionalized Compressive Sensing in WSN

Document Type

Article

Publication Date

8-1-2017

Abstract

On the basis of traditional transmission methods in wireless sensor networks, a vital problem is that a center region near the sink will be created and the sensors in it require vast energy to receive or relay data packages from other sensors. In this case, their own energy will rapidly be exhausted. Recently, compressive sensing (CS) has been employed to reduce the number of data transmissions and extend the lifetime of networks. This paper proposes an energy-efficient data gathering approach based on regionalized compressive sensing (RCS). Firstly, the network is randomly divided into smaller regions without the requirement for any specific relationships or features among sensors in the same block. Subsequently, a center sensor for each region is elected and is utilized to receive original readings of all other sensors in its region. Furthermore, CS is implemented on each center sensor in individual region respectively. The regional measurement matrix is given to generate regionalized samplings. Finally, the regional measurements in every region are transported to the sink for reconstruction. To further guarantee the viability of our approach, two issues related to practical applications are considered: (1) how to design an available regional measurement matrix, and (2) when the sampling procedure should be terminated. Experimental results on a real-world dataset indicate that the proposed RCS outperforms the existing methods in terms of efficiency, and the feasibility of the two issues in practice is also verified.

DOI

10.11897/SP.J.1016.2017.01933

Publication Title

Jisuanji Xuebao/Chinese Journal of Computers

Volume Number

40

Issue Number

8

First Page

1933

Last Page

1945

ISSN

02544164

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