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International Conference on Computational Science and Its Applications

ICCSA 2017: Computational Science and Its Applications – ICCSA 2017 pp 443–454Cite as

Computational Intelligence for Smart Air Quality Monitors Calibration

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10406))

Abstract

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application.

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Author information

Authors and Affiliations

  1. C.R. Portici, ENEA DTE-FSN-DIN, P. Le Enrico Fermi 1, Portici, 80055, Naples, Italy

    Elena Esposito, Saverio De Vito, Maria Salvato, Grazia Fattoruso & Girolamo Di Francia

Authors
  1. Elena Esposito
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  2. Saverio De Vito
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  3. Maria Salvato
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  4. Grazia Fattoruso
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  5. Girolamo Di Francia
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Corresponding author

Correspondence to Elena Esposito .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Esposito, E., De Vito, S., Salvato, M., Fattoruso, G., Di Francia, G. (2017). Computational Intelligence for Smart Air Quality Monitors Calibration. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_31

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  • DOI: https://doi.org/10.1007/978-3-319-62398-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62397-9

  • Online ISBN: 978-3-319-62398-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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