An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions
Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/854 |
| _version_ | 1811298824041267200 |
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| author | Sebastián Gutiérrez Hiram Ponce |
| author_facet | Sebastián Gutiérrez Hiram Ponce |
| author_sort | Sebastián Gutiérrez |
| collection | DOAJ |
| description | Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately. |
| first_indexed | 2024-04-13T06:26:00Z |
| format | Article |
| id | doaj.art-ed8a2ef0dd744b748ea8079c6d4e954d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:26:00Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ed8a2ef0dd744b748ea8079c6d4e954d2022-12-22T02:58:26ZengMDPI AGSensors1424-82202019-02-0119485410.3390/s19040854s19040854An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate ConditionsSebastián Gutiérrez0Hiram Ponce1Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes, Aguascalientes 20290, MexicoFacultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, MexicoWireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.https://www.mdpi.com/1424-8220/19/4/854artificial organic networksartificial hydrocarbon networksdistributed services architecturefailure detectioninternet-of-thingsmachine learningweather web servicessensor networks |
| spellingShingle | Sebastián Gutiérrez Hiram Ponce An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions Sensors artificial organic networks artificial hydrocarbon networks distributed services architecture failure detection internet-of-things machine learning weather web services sensor networks |
| title | An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions |
| title_full | An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions |
| title_fullStr | An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions |
| title_full_unstemmed | An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions |
| title_short | An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions |
| title_sort | intelligent failure detection on a wireless sensor network for indoor climate conditions |
| topic | artificial organic networks artificial hydrocarbon networks distributed services architecture failure detection internet-of-things machine learning weather web services sensor networks |
| url | https://www.mdpi.com/1424-8220/19/4/854 |
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