Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784650250212868096 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8838954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xuming recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT liuwei recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT xujinwei recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT xiayu recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT maojing recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT xucheng recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT hushunren recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks AT huangdaqing recurrentneuralnetworkbasedlinkqualitypredictionforfluctuatinglowpowerwirelesslinks |