Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

dc.contributor.author Tariq, Hasan
dc.contributor.author Abdaoui, Abderrazak
dc.contributor.author Touati, Farid
dc.contributor.author Al Hitmi, Mohammad Abdullah
dc.contributor.author Crescini, Damiano
dc.contributor.author Mnaouer, Adel Ben
dc.date.accessioned 2021-02-07T12:37:18Z
dc.date.available 2021-02-07T12:37:18Z
dc.date.copyright © 2020
dc.date.issued 2020-12
dc.description This 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/aj0506198 en_US
dc.description.abstract Environmental 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.sponsorship Qatar National Research Fund - QNRF Qatar Foundation - QF en_US
dc.identifier.citation Tariq, 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/aj0506198 en_US
dc.identifier.issn 24156698
dc.identifier.uri https://doi.org/10.25046/aj0506198
dc.identifier.uri http://hdl.handle.net/20.500.12519/327
dc.language.iso en en_US
dc.publisher ASTES Publishers en_US
dc.relation Authors 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.ispartofseries Advances in Science, Technology and Engineering Systems;Volume 5, Issue 6
dc.rights Creative Commons Attribution-ShareAlike 4.0 International License.
dc.rights.holder Copyright : © 2020 ASTES Publishers. All rights reserved.
dc.rights.uri https://creativecommons.org/licenses/by-sa/4.0/
dc.subject Air quality en_US
dc.subject Gas sensors node en_US
dc.subject IoT en_US
dc.subject Mapping en_US
dc.subject Multi-variable environmental en_US
dc.title Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform en_US
dc.type Article en_US
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