RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid
In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynam...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8951146/ |
| _version_ | 1819171327171362816 |
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| author | Xue Xue Wei Sun Jianping Wang Qiyue Li Guojun Luo Keping Yu |
| author_facet | Xue Xue Wei Sun Jianping Wang Qiyue Li Guojun Luo Keping Yu |
| author_sort | Xue Xue |
| collection | DOAJ |
| description | In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively. |
| first_indexed | 2024-12-22T19:49:31Z |
| format | Article |
| id | doaj.art-b1eed37926ab4bfdad2bdbcb0d334f0b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:49:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1eed37926ab4bfdad2bdbcb0d334f0b2022-12-21T18:14:35ZengIEEEIEEE Access2169-35362020-01-0187829784110.1109/ACCESS.2020.29643198951146RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart GridXue Xue0https://orcid.org/0000-0003-0796-0250Wei Sun1https://orcid.org/0000-0003-4075-0597Jianping Wang2https://orcid.org/0000-0002-7911-1197Qiyue Li3https://orcid.org/0000-0002-9399-8759Guojun Luo4https://orcid.org/0000-0002-0169-3721Keping Yu5https://orcid.org/0000-0001-5735-2507School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaGlobal Information and Telecommunication Institute, Waseda University, Tokyo, JapanIn the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively.https://ieeexplore.ieee.org/document/8951146/Wireless sensor networkslink quality predictionRVFL networkprobability-guaranteed interval boundary |
| spellingShingle | Xue Xue Wei Sun Jianping Wang Qiyue Li Guojun Luo Keping Yu RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid IEEE Access Wireless sensor networks link quality prediction RVFL network probability-guaranteed interval boundary |
| title | RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid |
| title_full | RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid |
| title_fullStr | RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid |
| title_full_unstemmed | RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid |
| title_short | RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid |
| title_sort | rvfl lqp rvfl based link quality prediction of wireless sensor networks in smart grid |
| topic | Wireless sensor networks link quality prediction RVFL network probability-guaranteed interval boundary |
| url | https://ieeexplore.ieee.org/document/8951146/ |
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