Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes
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-f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/11/1776 |
| _version_ | 1798042540331499520 |
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| author | Thu L. N. Nguyen Yoan Shin Jin Young Kim Dong In Kim |
| author_facet | Thu L. N. Nguyen Yoan Shin Jin Young Kim Dong In Kim |
| author_sort | Thu L. N. Nguyen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:36:51Z |
| format | Article |
| id | doaj.art-864a4a19605c4eb2ab378d4330a8e20d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:36:51Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-864a4a19605c4eb2ab378d4330a8e20d2022-12-22T03:59:11ZengMDPI AGEnergies1996-10732017-11-011011177610.3390/en10111776en10111776Applicability of Compressive Sensing for Wireless Energy Harvesting NodesThu L. N. Nguyen0Yoan Shin1Jin Young Kim2Dong In Kim3School of Electronic Engineering, Soongsil University, Seoul 06978, KoreaSchool of Electronic Engineering, Soongsil University, Seoul 06978, KoreaDepartment of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, KoreaCollege of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, KoreaThis 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.https://www.mdpi.com/1996-1073/10/11/1776compressive sensingenergy harvestingsparse random projection |
| spellingShingle | Thu L. N. Nguyen Yoan Shin Jin Young Kim Dong In Kim Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes Energies compressive sensing energy harvesting sparse random projection |
| title | Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes |
| title_full | Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes |
| title_fullStr | Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes |
| title_full_unstemmed | Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes |
| title_short | Applicability of Compressive Sensing for Wireless Energy Harvesting Nodes |
| title_sort | applicability of compressive sensing for wireless energy harvesting nodes |
| topic | compressive sensing energy harvesting sparse random projection |
| url | https://www.mdpi.com/1996-1073/10/11/1776 |
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