Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes

Thu L. N. Nguyen, Yoan Shin, Jin Young Kim and Dong In Kim
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Thu L. N. Nguyen: School of Electronic Engineering, Soongsil University, Seoul 06978, Korea
Yoan Shin: School of Electronic Engineering, Soongsil University, Seoul 06978, Korea
Jin Young Kim: Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea
Dong In Kim: College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea

Energies, 2017, vol. 10, issue 11, 1-15

Abstract: This paper proposes an approach toward solving an issue pertaining to measuring compressible data in large-scale energy-harvesting wireless sensor networks with channel fading. We consider a scenario in which N sensors observe hidden phenomenon values, transmit their observations using amplify-and-forward protocol over fading channels to a fusion center (FC), and the FC needs to choose a number of sensors to collect data and recover them according to the desired approximation error using the compressive sensing. In order to reduce the communication cost, sparse random matrices are exploited in the pre-processing procedure. We first investigate the sparse representation for sensors with regard to recovery accuracy. Then, we present the construction of sparse random projection matrices based on the fact that the energy consumption can vary across the energy harvesting sensor nodes. The key ingredient is the sparsity level of the random projection, which can greatly reduce the communication costs. The corresponding number of measurements is chosen according to the desired approximation error. Analysis and simulation results validate the potential of the proposed approach.

Keywords: compressive sensing; energy harvesting; sparse random projection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
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