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

Sensors
Sebastián Gutiérrez, Hiram Ponce

Abstract

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.

References

Oct 8, 2008·Emotion·Jaap J A DenissenMarcel A G van Aken
Apr 25, 2014·Sensors·Pedro Magaña-EspinozaAldo Medina-Cass
Jan 8, 2015·Journal of Computational Chemistry·Hiram PonceArturo Molina
Feb 11, 2015·Sensors·Zhihua ZhuYoshio Inoue
Apr 18, 2015·Sensors·Shah Ahsanul HaqueSyed Mahfuzul Aziz
Oct 30, 2016·Sensors·Hiram PonceMaría de Lourdes Martínez-Villaseñor
Oct 28, 2018·Sensors·Peio Lopez-IturriFrancisco Falcone

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Citations

Jul 3, 2021·Sensors·Juan A López-MoralesAntonio F Skarmeta

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Software Mentioned

DSA Architecture )
DGLux5®program
EnOcean®protocol
Raspberry Pi
Yahoo Weather API

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