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Low-Power Failure Detection for Environmental Monitoring Based on IoT
Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513096/ https://www.ncbi.nlm.nih.gov/pubmed/34640809 http://dx.doi.org/10.3390/s21196489 |
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author | Liu, Jiaxi Gao, Weizhong Dong, Jian Wu, Na Ding, Fei |
author_facet | Liu, Jiaxi Gao, Weizhong Dong, Jian Wu, Na Ding, Fei |
author_sort | Liu, Jiaxi |
collection | PubMed |
description | Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms. |
format | Online Article Text |
id | pubmed-8513096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85130962021-10-14 Low-Power Failure Detection for Environmental Monitoring Based on IoT Liu, Jiaxi Gao, Weizhong Dong, Jian Wu, Na Ding, Fei Sensors (Basel) Article Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms. MDPI 2021-09-28 /pmc/articles/PMC8513096/ /pubmed/34640809 http://dx.doi.org/10.3390/s21196489 Text en © 2021 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 Liu, Jiaxi Gao, Weizhong Dong, Jian Wu, Na Ding, Fei Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title | Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title_full | Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title_fullStr | Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title_full_unstemmed | Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title_short | Low-Power Failure Detection for Environmental Monitoring Based on IoT |
title_sort | low-power failure detection for environmental monitoring based on iot |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513096/ https://www.ncbi.nlm.nih.gov/pubmed/34640809 http://dx.doi.org/10.3390/s21196489 |
work_keys_str_mv | AT liujiaxi lowpowerfailuredetectionforenvironmentalmonitoringbasedoniot AT gaoweizhong lowpowerfailuredetectionforenvironmentalmonitoringbasedoniot AT dongjian lowpowerfailuredetectionforenvironmentalmonitoringbasedoniot AT wuna lowpowerfailuredetectionforenvironmentalmonitoringbasedoniot AT dingfei lowpowerfailuredetectionforenvironmentalmonitoringbasedoniot |