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On the prediction of air quality within vehicles using outdoor air pollution: sensors and machine learning algorithms

Authors:

Giovanni Delnevo,

Silvia MirriAuthors Info & Claims

NET4us '22: Proceedings of the ACM SIGCOMM Workshop on Networked Sensing Systems for a Sustainable Society

Pages 14 - 19

https://doi.org/10.1145/3538393.3544934

Published: 22 August 2022 Publication History

Abstract

Environmental conditions within vehicles represent a significant element of the driver's well-being and comfort. In particular, exposure to air pollution has been proven to affect human cognitive performances, hence it could represent a risk to driving safety. Monitoring internal and external environmental data could provide interesting hints, helpful in predicting trends and situations potentially dangerous and/or unease, that should be reported, enhancing the driver's awareness. This paper presents a study we have conducted with the aim of predicting indoor vehicle environmental conditions, thanks to a campaign of data collection. In particular, we have adopted a multi-sensor kit, installed within and outside a vehicle, then we have exploited driving sessions in a urban environment. Different machine learning algorithms have been adopted to test their accuracy in predicting internal conditions, on the basis of external ones, discussing the obtained results.

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Cited By

Ramirez-Espinosa GChiavassa PGiusto EQuer SMontrucchio BRebaudengo M(2024)Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban EnvironmentsIEEE Internet of Things Journal10.1109/JIOT.2024.340562311:17(28409-28420)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3405623
Gandino FChiavassa PFerrero R(2023)Measuring Particulate Matter: An Investigation on Sensor Technology, Particle Size, Monitoring SiteIEEE Access10.1109/ACCESS.2023.331909211(108761-108774)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3319092
Sarcar PGiri ARout M(2023)Secured DV-Hop localization scheme for WSN in environmental monitoringWireless Networks10.1007/s11276-023-03572-630:3(1245-1253)Online publication date: 21-Nov-2023
https://doi.org/10.1007/s11276-023-03572-6

Index Terms

On the prediction of air quality within vehicles using outdoor air pollution: sensors and machine learning algorithms
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

NET4us '22: Proceedings of the ACM SIGCOMM Workshop on Networked Sensing Systems for a Sustainable Society

August 2022

49 pages

ISBN:9781450393928

DOI:10.1145/3538393

Copyright © 2022 ACM.

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Published: 22 August 2022

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August 22, 2022

Amsterdam, Netherlands

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Cited By

Ramirez-Espinosa GChiavassa PGiusto EQuer SMontrucchio BRebaudengo M(2024)Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban EnvironmentsIEEE Internet of Things Journal10.1109/JIOT.2024.340562311:17(28409-28420)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3405623
Gandino FChiavassa PFerrero R(2023)Measuring Particulate Matter: An Investigation on Sensor Technology, Particle Size, Monitoring SiteIEEE Access10.1109/ACCESS.2023.331909211(108761-108774)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3319092
Sarcar PGiri ARout M(2023)Secured DV-Hop localization scheme for WSN in environmental monitoringWireless Networks10.1007/s11276-023-03572-630:3(1245-1253)Online publication date: 21-Nov-2023
https://doi.org/10.1007/s11276-023-03572-6

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