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AIRO: Development of an Intelligent IoT-based Air Quality Monitoring Solution for Urban Areas

Kumar, T and Doss, A (2022) AIRO: Development of an Intelligent IoT-based Air Quality Monitoring Solution for Urban Areas. In: 2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022, 7- 8 September 2022, Dehradun, pp. 262-273.

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Official URL: https://doi.org/10.1016/j.procs.2023.01.008


Air pollution is the contamination of the atmosphere by any biological, physical, or chemical means. Bengaluru, the Silicon Valley of India, has air pollution levels that exceed WHO standards. Its air has high PM10, PM2.5, SO2, NO2, and CO2 levels, exposing residents to an increased risk of respiratory diseases. AIRO, a decentralised IoT-based air quality monitoring solution, is proposed to calculate the (Air Quality Index) AQI in real-time and notify the users of dangerous AQI levels. Unlike the city's fixed air quality monitoring stations, this portable system can easily be integrated into everyday items for computing real-time air quality at any given place. The proposed solution is then validated using a physical prototype incorporated into a water bottle. Using the Intel Edison development platform, this prototype is equipped with a GPS module, Wi-Fi module, PMS5003, MQ131, MQ135, MQ136, MQ7 sensors, and an LCD Display. The prototype records the air quality indicators, calculates the AQI, and sends the data to the AWS Cloud Server. After an in-depth analysis of the cloud data, the daily and weekly AQI is predicted for multiple locations in the city. A hybrid CNN-Bi-LSTM model is proposed for predicting AQI, which needs to be evaluated at the city scale for dependable results. Finally, a smartphone app is developed using Android Studio and Python to monitor the air quality and notify the users. The users also have an option to select a location and get the real-time and predicted AQI. In the future, this hybrid deep-learning model can be extended to other cities using the transfer learning approach.

Item Type: Conference Paper
Publication: Procedia Computer Science
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Author.
Keywords: Air Quality Monitoring; Artificial Intelligence; Deep Neural Network; Design; Internet of Things (IoT).
Department/Centre: Division of Mechanical Sciences > Centre for Product Design & Manufacturing
Date Deposited: 25 Jul 2023 10:19
Last Modified: 25 Jul 2023 10:19
URI: https://eprints.iisc.ac.in/id/eprint/82654

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