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Open AccessArticle

An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions

1
Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes, Aguascalientes 20290, Mexico
2
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 854; https://doi.org/10.3390/s19040854
Received: 17 December 2018 / Revised: 30 January 2019 / Accepted: 5 February 2019 / Published: 19 February 2019
(This article belongs to the Section Sensor Networks)
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. View Full-Text
Keywords: artificial organic networks; artificial hydrocarbon networks; distributed services architecture; failure detection; internet-of-things; machine learning; weather web services; sensor networks artificial organic networks; artificial hydrocarbon networks; distributed services architecture; failure detection; internet-of-things; machine learning; weather web services; sensor networks
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MDPI and ACS Style

Gutiérrez, S.; Ponce, H. An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions. Sensors 2019, 19, 854.

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