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Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the appl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571096/ https://www.ncbi.nlm.nih.gov/pubmed/32957643 http://dx.doi.org/10.3390/s20185327 |
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author | Liu, Wei Xia, Yu Zheng, Daqing Xie, Jian Luo, Rong Hu, Shunren |
author_facet | Liu, Wei Xia, Yu Zheng, Daqing Xie, Jian Luo, Rong Hu, Shunren |
author_sort | Liu, Wei |
collection | PubMed |
description | Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice. |
format | Online Article Text |
id | pubmed-7571096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75710962020-10-28 Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks Liu, Wei Xia, Yu Zheng, Daqing Xie, Jian Luo, Rong Hu, Shunren Sensors (Basel) Article Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice. MDPI 2020-09-17 /pmc/articles/PMC7571096/ /pubmed/32957643 http://dx.doi.org/10.3390/s20185327 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Wei Xia, Yu Zheng, Daqing Xie, Jian Luo, Rong Hu, Shunren Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title | Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title_full | Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title_fullStr | Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title_full_unstemmed | Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title_short | Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks |
title_sort | environmental impacts on hardware-based link quality estimators in wireless sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571096/ https://www.ncbi.nlm.nih.gov/pubmed/32957643 http://dx.doi.org/10.3390/s20185327 |
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