WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs | Zendy

Wei Sun | Zendy; Wei Lu | Zendy; Qiyue Li | Zendy; Liangfeng Chen | Zendy; Daoming Mu | Zendy; Xiaojing Yuan | Zendy

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WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs

Author(s) -

Wei Sun,

Wei Lu,

Qiyue Li,

Liangfeng Chen,

Daoming Mu,

Xiaojing Yuan

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2723360

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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