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A Scalable IoT–Cloud Architecture with Deep Q-Learning for Air Quality Monitoring and Alerting
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 23 |
| Year of Publication: 2025 |
| Authors: Benjamin Aidoo, Frederick Kwame Minta, Abdul-Aziz N-yo, Derrick Attoh Tettey, Osbert Kasiimbura, Albert Essilfie |
10.5120/ijca2025925461
|
Benjamin Aidoo, Frederick Kwame Minta, Abdul-Aziz N-yo, Derrick Attoh Tettey, Osbert Kasiimbura, Albert Essilfie . A Scalable IoT–Cloud Architecture with Deep Q-Learning for Air Quality Monitoring and Alerting. International Journal of Computer Applications. 187, 23 ( Jul 2025), 44-51. DOI=10.5120/ijca2025925461
Abstract
Air pollution poses a growing threat to public health in rapidly urbanizing cities, particularly in Sub-Saharan Africa, where real-time monitoring infrastructure is limited. This paper presents the design and implementation of a scalable IoT and cloud-based architecture for air quality monitoring and intelligent alerting in Accra, Ghana. The system integrates low-cost ESP32-based sensor nodes with a Deep Q-Network (DQN) to classify pollution severity and issue adaptive, context-aware alerts. Eight key environmental parameters, including PM1.0, PM2.5, PM10, VOCs, CO, LPG, temperature, and humidity, are continuously monitored and analyzed using cloud-based processing. Real-time data is visualized through a web dashboard, while critical alerts are disseminated via SMS to ensure user accessibility. The DQN agent supports decision transparency through Q-values, feature importance, and temporal trend analysis. Experimental results demonstrate a training accuracy of 89% and a field test classification accuracy of 82.9%, confirming the system’s effectiveness for scalable, real-time, and interpretable environmental health monitoring in resource-constrained settings.
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