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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