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WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs
Sun, Wei1; Lu, Wei1; Li, Qiyue1; Chen, Liangfeng2; Mu, Daoming1; Yuan, Xiaojing3
2017
Source PublicationIEEE ACCESS
Volume5Pages:12788-12797
AbstractWireless 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.
SubtypeArticle
KeywordSmart Grids Wireless Sensor Networks Quality Of Service Link Quality Estimation Wavelet Neural Network Radio Link Reliability
WOS HeadingsScience & Technology ; Technology
Funding OrganizationNational Natural Science Foundation of China(51307041) ; National Natural Science Foundation of China(51307041) ; China Scholarship Council(201606695038) ; China Scholarship Council(201606695038) ; National Natural Science Foundation of China(51307041) ; National Natural Science Foundation of China(51307041) ; China Scholarship Council(201606695038) ; China Scholarship Council(201606695038)
DOI10.1109/ACCESS.2017.2723360
WOS KeywordPROTOCOL
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(51307041) ; National Natural Science Foundation of China(51307041) ; China Scholarship Council(201606695038) ; China Scholarship Council(201606695038) ; National Natural Science Foundation of China(51307041) ; National Natural Science Foundation of China(51307041) ; China Scholarship Council(201606695038) ; China Scholarship Council(201606695038)
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000406432300058
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/33602
Collection信息中心
Affiliation1.Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Anhui, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230000, Peoples R China
3.Univ Houston, Coll Technol, Houston, TX 77004 USA
Recommended Citation
GB/T 7714
Sun, Wei,Lu, Wei,Li, Qiyue,et al. WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs[J]. IEEE ACCESS,2017,5:12788-12797.
APA Sun, Wei,Lu, Wei,Li, Qiyue,Chen, Liangfeng,Mu, Daoming,&Yuan, Xiaojing.(2017).WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs.IEEE ACCESS,5,12788-12797.
MLA Sun, Wei,et al."WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs".IEEE ACCESS 5(2017):12788-12797.
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