An Autonomous Multi-Variable Outdoor Air Quality Mapping Wireless Sensors IoT Node for Qatar

Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al‐Hitmi, Mohammed; Crescini, Damiano; Mnaouer, Adel Ben

doi:10.1109/iwcmc48107.2020.9148392

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The existing O-AQNs, Urban AirQ, Smart Citizen Kit, SeReNo V1, AirQ Mesh needed improvement in energy efficiency, GPS, time-series vector structuring, and remote data transmission to ensure dynamic air quality mapping [16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…The Sensor Node (SeReNo) V1 [19] by Farid et al was the first node developed with indoor air quality monitoring and energy harvesting capabilities. SeReNo V2 presented in this work [20] is the improvement of [19] that needed to be upgraded with GSM and GPS, more Flash to perform AQI data processing needed for optimized energy efficiency, electrochemical gas sensors warm-up times, and outdoor air quality.…”

Section: Sereno V1mentioning

confidence: 99%

“…The works Lebilium Waspmote [21] was Zigbee but needed GSM/GPRS support for outdoor sensing and have been more contributory if equipped with geo-spatial traces or cadastral layouts support for all the measured variables like AirBox [22]. An infrastructure embedded system was needed to accommodate the maximum sensing, minimum data processing, AQI estimation, and long-haul deployability [19][20][21][22][23].…”

Section: Airq Meshmentioning

confidence: 99%

“…The block diagram of a generic AQM node is given below in figure 1. In our previous works [14,15,19,20], we had filled the design gaps in indoor air quality monitoring and developed a multi-sensor AQM node called Sensors V1 revision 1 and revision 2 presented in figure 1. In this work, work proposed a novel O-AQM node by improving the system architecture of SeReNo V1 as SeReNo V2.…”

Section: The Sereno V2 Aqm Sensing Platformmentioning

confidence: 99%

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Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Tariq¹,

Abdaoui²,

Touati³

et al. 2020

Adv. sci. technol. eng. syst. j.

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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).

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Section: Related Workmentioning

confidence: 99%

Section: Sereno V1mentioning

confidence: 99%

Section: Airq Meshmentioning

confidence: 99%

Section: The Sereno V2 Aqm Sensing Platformmentioning

confidence: 99%

See 2 more Smart Citations

Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Tariq¹,

Abdaoui²,

Touati³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

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“…In clustered sensing for CO2 and temperature, there was a pressing need for a concurrent forecasting chain in addition to dimensionality reduction using matrix factorization (MF) [21] for the air quality nodes with parametric ML model deployment support on embedded systems like SeReNoV2 [22]. Considering the recent studies conducted at European Commission, Joint Research Centre (JRC) [23] on the impact of masks on CO2 concentration zones in the breathing zones have concluded that the increase in the CO2 due to breathing exhaled air temperature as well [24].…”

Section: Introductionmentioning

confidence: 99%

An IoT-based Bi-Cluster Forecasting using Automated ML-Model Optimization for COVID19

Tariq¹,

Touati²,

Crescini³

et al. 2022

Preprint

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The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

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