WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs
Wireless sensor networks (WSNs) are currently being used for monitoring and control in smart grids. To ensure the quality of service (QoS) requirements of smart grid applications, WSNs need to provide specific reliability guarantees. Real-time link quality estimation (LQE) is essential for improving...
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| Format: | Article |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7968255/ |
| _version_ | 1818645272726601728 |
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| author | Wei Sun Wei Lu Qiyue Li Liangfeng Chen Daoming Mu Xiaojing Yuan |
| author_facet | Wei Sun Wei Lu Qiyue Li Liangfeng Chen Daoming Mu Xiaojing Yuan |
| author_sort | Wei Sun |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) are currently being used for monitoring and control in smart grids. To ensure the quality of service (QoS) requirements of smart grid applications, WSNs need to provide specific reliability guarantees. Real-time link quality estimation (LQE) is essential for improving the reliability of WSN protocols. However, many state-of-the-art LQE methods produce numerical estimates that are suitable neither for describing the dynamic random features of radio links nor for determining whether the reliability satisfies the requirements of smart grid communication standards. This paper proposes a wavelet-neural-network-based LQE (WNN-LQE) algorithm that closes the gap between the QoS requirements of smart grids and the features of radio links by estimating the probability-guaranteed limits on the packet reception ratio (PRR). In our algorithm, the signal-to-noise ratio (SNR) is used as the link quality metric. The SNR is approximately decomposed into two components: a time-varying nonlinear part and a non-stationary random part. Each component is separately processed before it is input into the WNN model. The probability-guaranteed limits on the SNR are obtained from the WNN-LQE algorithm and are then transformed into estimated limits on the PRR via the mapping function between the SNR and PRR. Comparative experimental results are presented to demonstrate the validity and effectiveness of the proposed LQE algorithm. |
| first_indexed | 2024-12-17T00:28:07Z |
| format | Article |
| id | doaj.art-5268c9f9936a4e2091891ca6eccdc34e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:28:07Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5268c9f9936a4e2091891ca6eccdc34e2022-12-21T22:10:25ZengIEEEIEEE Access2169-35362017-01-015127881279710.1109/ACCESS.2017.27233607968255WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNsWei Sun0https://orcid.org/0000-0003-4075-0597Wei Lu1Qiyue Li2https://orcid.org/0000-0002-9399-8759Liangfeng Chen3Daoming Mu4Xiaojing Yuan5School 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, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, ChinaCollege of Technology, University of Houston, Houston, TX, USAWireless sensor networks (WSNs) are currently being used for monitoring and control in smart grids. To ensure the quality of service (QoS) requirements of smart grid applications, WSNs need to provide specific reliability guarantees. Real-time link quality estimation (LQE) is essential for improving the reliability of WSN protocols. However, many state-of-the-art LQE methods produce numerical estimates that are suitable neither for describing the dynamic random features of radio links nor for determining whether the reliability satisfies the requirements of smart grid communication standards. This paper proposes a wavelet-neural-network-based LQE (WNN-LQE) algorithm that closes the gap between the QoS requirements of smart grids and the features of radio links by estimating the probability-guaranteed limits on the packet reception ratio (PRR). In our algorithm, the signal-to-noise ratio (SNR) is used as the link quality metric. The SNR is approximately decomposed into two components: a time-varying nonlinear part and a non-stationary random part. Each component is separately processed before it is input into the WNN model. The probability-guaranteed limits on the SNR are obtained from the WNN-LQE algorithm and are then transformed into estimated limits on the PRR via the mapping function between the SNR and PRR. Comparative experimental results are presented to demonstrate the validity and effectiveness of the proposed LQE algorithm.https://ieeexplore.ieee.org/document/7968255/Smart gridswireless sensor networksquality of servicelink quality estimationwavelet neural networkradio link reliability |
| spellingShingle | Wei Sun Wei Lu Qiyue Li Liangfeng Chen Daoming Mu Xiaojing Yuan WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs IEEE Access Smart grids wireless sensor networks quality of service link quality estimation wavelet neural network radio link reliability |
| title | WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs |
| title_full | WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs |
| title_fullStr | WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs |
| title_full_unstemmed | WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs |
| title_short | WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs |
| title_sort | wnn lqe wavelet neural network based link quality estimation for smart grid wsns |
| topic | Smart grids wireless sensor networks quality of service link quality estimation wavelet neural network radio link reliability |
| url | https://ieeexplore.ieee.org/document/7968255/ |
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