AI-Driven Ventilation Management Using Embedded IoT Devices

Effective ventilation management is essential for maintaining optimal indoor air quality and ensuring a healthy environment. This study introduces an AI-based Key Performance Indicator (KPI) system for ventilation, leveraging embedded IoT devices to monitor and control indoor air quality. The system integrates a range of sensors and components, including the DHT11 for temperature and humidity measurement, the PMS7003 for particulate matter detection, the MQ6 for CO2 concentration measurement, and the MQ135 for gas detection. Real-time data from these sensors is collected and processed by an Arduino microcontroller. The system uses an ESP8266 module to upload this data to ThingSpeak, a cloud-based IoT platform, for remote monitoring and analysis. Additionally, the system operates a CPU fan to enhance ventilation when temperature or CO2 levels exceed predefined thresholds. The collected data is also sent to a machine learning model that predicts gas content and CO2 levels, enabling advanced analysis and predictive maintenance.The integration of AI and IoT in this system provides a comprehensive solution for monitoring indoor air quality, enabling proactive management of ventilation systems. This approach not only ensures a healthier indoor environment but also contributes to energy efficiency by automating ventilation controls based on real-time data and predictive analytics.