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Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical...

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Autores principales: Xu, Ming, Liu, Wei, Xu, Jinwei, Xia, Yu, Mao, Jing, Xu, Cheng, Hu, Shunren, Huang, Daqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838954/
https://www.ncbi.nlm.nih.gov/pubmed/35161954
http://dx.doi.org/10.3390/s22031212
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author Xu, Ming
Liu, Wei
Xu, Jinwei
Xia, Yu
Mao, Jing
Xu, Cheng
Hu, Shunren
Huang, Daqing
author_facet Xu, Ming
Liu, Wei
Xu, Jinwei
Xia, Yu
Mao, Jing
Xu, Cheng
Hu, Shunren
Huang, Daqing
author_sort Xu, Ming
collection PubMed
description One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.
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spelling pubmed-88389542022-02-13 Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links Xu, Ming Liu, Wei Xu, Jinwei Xia, Yu Mao, Jing Xu, Cheng Hu, Shunren Huang, Daqing Sensors (Basel) Article One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations. MDPI 2022-02-05 /pmc/articles/PMC8838954/ /pubmed/35161954 http://dx.doi.org/10.3390/s22031212 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Ming
Liu, Wei
Xu, Jinwei
Xia, Yu
Mao, Jing
Xu, Cheng
Hu, Shunren
Huang, Daqing
Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title_full Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title_fullStr Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title_full_unstemmed Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title_short Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links
title_sort recurrent neural network based link quality prediction for fluctuating low power wireless links
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838954/
https://www.ncbi.nlm.nih.gov/pubmed/35161954
http://dx.doi.org/10.3390/s22031212
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