Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al Hitmi, Mohammad Abdullah; Crescini, Damiano; Mnaouer, Adel Ben

Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Date

2020-12

Authors

Tariq, Hasan

Abdaoui, Abderrazak

Touati, Farid

Al Hitmi, Mohammad Abdullah

Crescini, Damiano

Mnaouer, Adel Ben

Publisher

ASTES Publishers

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.

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

Keywords

Air quality, Gas sensors node, IoT, Mapping, Multi-variable environmental

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

URI

https://doi.org/10.25046/aj0506198
http://hdl.handle.net/20.500.12519/327

Collections

Department of Computer Engineering and Computational Sciences

Full item page