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Predictive Model for Detecting MQ2 Gases Using Fuzzy Logic on IoT Devices

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Engineering Applications of Neural Networks (EANN 2016)

Abstract

This paper shows the design, implementation and analysis of a fuzzy system for monitoring and alert generation for gas detection in enclosed spaces, which can be very useful either at home or industrial environments. Furthermore, this could be a useful application in the fields of Home Automation which may be developed by integrating devices and technologies of The Internet of Things. Such application consists of the provision of sensors, which constantly receive signals on gases in the environment. Subsequently, the information is analyzed by a fuzzy system that determines when to generate alert notifications, identifying the times when levels are high, either by incendiary or high pollution situations. The prototype consists of connecting an MQ-2 sensor with a Raspberry Pi, which receives the information provided and analyses it by fuzzy logic, thus determining in which cases it is necessary to alarm at sensitive events, generating alert emails and historical data.

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Correspondence to Catalina Hernández .

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© 2016 Springer International Publishing Switzerland

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Hernández, C., Villagrán, S., Gaona, P. (2016). Predictive Model for Detecting MQ2 Gases Using Fuzzy Logic on IoT Devices. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-44188-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44187-0

  • Online ISBN: 978-3-319-44188-7

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