IoT and Machine Learning-Based System for Predicting, Monitoring and Controlling Indoor Air Quality

The pandemic highlighted the importance of our environment, leading to efforts to make it comfortable. Air quality, a crucial factor affecting our health and comfort, can be measured using various methods. The challenge is maintaining the indoor air quality in a good range, particularly when ventilation is limited. This research aims to develop a system using IoT cloud architecture for monitoring an extensive range of dangerous gas concentrations in indoor environments. The proposed solution includes a predictive algorithm, user-friendly interface, and IoT connectivity. The system integrates gas sensors, Arduino MKR 1010 WIFI, ThingSpeak platform, Blynk platform, and a smart outlet for ventilation control. Predictive modeling is based on Neural Network Time Series.