Research on the design of indoor air quality monitoring system based on cloud-side-end collaboration

Given that individuals spend more than 90% of their time indoors, research indicates a direct correlation between indoor air quality and overall health and well-being. With the rise of respiratory illnesses and increasing concerns about indoor air quality, traditional methods such as using potted plants, air purifiers, and basic air conditioning units with purification capabilities are insufficient for real-time monitoring. To address these challenges, this study proposes an intelligent indoor air quality monitoring system that leverages cloud-edge-end collaboration. The system includes a cost-effective air quality monitor on the device side to collect and localize indoor air quality data. This data is then transmitted to the edge-side ceiling light via WiFi-Mesh. A machine learning model deployed using the CNN-LSTM algorithm on the Edge Impluse platform is used to analyze and predict indoor air quality levels over time. The system uploads this information to a cloud MQTT server using the Cat.1 protocol and sends notifications to subscribed users via a mobile app, enabling individuals to monitor and adjust indoor air quality as needed. This proactive approach can help reduce air pollution, decrease disease incidence, and enhance overall quality of life. Physical and deployment tests demonstrate the system's high performance, with a training accuracy of 97.5%, F1-SCORE of 95%, and prediction accuracy of 94.1%, making it a reliable tool for indoor air quality management.