Towards real-time outdoor air quality prediction using a hybrid model based on internet of things devices

Abstract

Monitoring Air Quality Index (AQI) provides comprehensive air quality and valuable information about health risks and environmental impacts. The proliferation of IoT devices has enabled real-time AQI prediction by allowing individuals to locally collect and analyze air quality. However, deploying these AQI prediction models on resource-constrained IoT devices poses significant challenges, including computational limitations, model optimization, and data synchronization. In this paper, we propose a hybrid model combining convolutional neural networks and long short-term memory networks to predict the hourly air quality index. Our proposed model achieved a high accuracy of 95% while maintaining a lightweight model size. These results demonstrate that the proposed model can operate effectively on resource-constrained devices, such as the Raspberry Pi 3, without impacting other tasks.