Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

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dc.contributor.authorTariq, Hasan
dc.contributor.authorAbdaoui, Abderrazak
dc.contributor.authorTouati, Farid
dc.contributor.authorAl Hitmi, Mohammad Abdullah
dc.contributor.authorCrescini, Damiano
dc.contributor.authorMnaouer, Adel Ben
dc.date.accessioned2021-02-07T12:37:18Z
dc.date.available2021-02-07T12:37:18Z
dc.date.copyright© 2020
dc.date.issued2020-12
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Advances in Science, Technology and Engineering Systems (2020), available online at: https://doi.org/10.25046/aj0506198en_US
dc.description.abstractEnvironmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University). © 2020 ASTES Publishers. All rights reserved.en_US
dc.description.sponsorshipQatar National Research Fund - QNRF Qatar Foundation - QFen_US
dc.identifier.citationTariq, H., Abdaoui, A., Touati, F., Hitmi, M.A.A., Crescini, D. & Mnaouer, A.B. (2020). Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform. Advances in Science, Technology and Engineering Systems Journal, 5(6), pp. 1666-1673. https://doi.org/10.25046/aj0506198en_US
dc.identifier.issn24156698
dc.identifier.urihttps://doi.org/10.25046/aj0506198
dc.identifier.urihttp://hdl.handle.net/20.500.12519/327
dc.language.isoenen_US
dc.publisherASTES Publishersen_US
dc.relationAuthors Affiliations : Tariq, H., Department of Electrical Engineering, College of Engineering, Qatar University2713, Qatar; Abdaoui, A., Department of Electrical Engineering, College of Engineering, Qatar University2713, Qatar; Touati, F., Department of Electrical Engineering, College of Engineering, Qatar University2713, Qatar; Al Hitmi, M.A., Department of Electrical Engineering, College of Engineering, Qatar University2713, Qatar; Crescini, D., Dipartimento di Ingegneria delI'Informazione, Brescia University25121, Italy; Mnaouer, A.B., Department of Computer Engineering and Computational Sciences, Faculty of Engineering, Applied Sciences and Technology, Canadian University Dubai117781, United Arab Emirates
dc.relation.ispartofseriesAdvances in Science, Technology and Engineering Systems;Volume 5, Issue 6
dc.rightsCreative Commons Attribution-ShareAlike 4.0 International License.
dc.rights.holderCopyright : © 2020 ASTES Publishers. All rights reserved.
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectAir qualityen_US
dc.subjectGas sensors nodeen_US
dc.subjectIoTen_US
dc.subjectMappingen_US
dc.subjectMulti-variable environmentalen_US
dc.titleReal-time Gradient-Aware Indigenous AQI Estimation IoT Platformen_US
dc.typeArticleen_US

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