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Data Analytics for Home Air Quality Monitoring

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Future Access Enablers for Ubiquitous and Intelligent Infrastructures (FABULOUS 2019)

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Abstract

Modern air quality monitoring systems are characterised by high complexity and costs. The expensive embedded units such as sensor arrays, processors, power blocks, displays and communication units make them less appropriate for small indoor spaces.

In this paper we demonstrate that two widely available, in private houses, sensors (for Humidity and Temperature) are promising alternative, to the expensive indoor air quality solutions, provided with intelligent data processing tools. Our findings suggest that neural network based data analytics system can learn to discriminate unusual indoor gases from normal home air components based only on temperature and humidity measurements.

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Acknowledgements

This study has been done during the traineeship program of PhD student Petya Mihaylova in University of Aveiro funded by ERASMUS+EU programme for education, training, youth and sport, supported by technical University of Sofia, Bulgaria.

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Authors and Affiliations

  1. Department of Electronics, Telecommunications and Informatics, University of Aveiro, Avero, Portugal

    Petia Georgieva

  2. English Language Faculty of Engineering, Technical University of Sofia, Sofia, Bulgaria

    Petya Mihaylova

  3. Faculty of Telecommunications, Technical University of Sofia, Sofia, Bulgaria

    Agata Manolova

Authors
  1. Petya Mihaylova
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  2. Agata Manolova
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  3. Petia Georgieva
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Corresponding author

Correspondence to Agata Manolova .

Editor information

Editors and Affiliations

  1. Department of Communication Networks, Technical University of Sofia, Sofia, Bulgaria

    Vladimir Poulkov

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mihaylova, P., Manolova, A., Georgieva, P. (2019). Data Analytics for Home Air Quality Monitoring. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-23976-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23975-6

  • Online ISBN: 978-3-030-23976-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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