IOT ARCHITECTURE FOR VEHICLE POLLUTANT GAS EMISSION MONITORING AND VALIDATION THROUGH MACHINE LEARNING

This study proposes an IoT architecture for monitoring vehicle pollutant gas emissions in response to increasing concerns about air pollution and global warming. The architecture is based on a node equipped with DHT22, MQ9, and MQ135 sensors to capture temperature, humidity, and gas emissions. This node effectively communicates through the LTE network to send the data to the ThingSpeak platform. An analysis of CO2, CO, and CH4 pollution levels is conducted using the collected data. This data is validated through the technical review of a test vehicle. Subsequently, an Artificial Neural Network (ANN) is trained using a specific database of CO2 emissions from cars in Canada. As a result, a high coefficient of determination (R2) of 99.2 % is achieved, along with low values of Root Mean Square Error (RMSE) and Mean Squared Error (MSE), indicating that the model makes accurate predictions and fits well with the training data. The ANN aims to predict CO2 emissions and verify CO2 data from the IoT network. The architecture demonstrates its capability for realtime monitoring and its potential to contribute to pollution reduction.