IoT Architecture for Vehicle Pollutant Gas Emission Monitoring and Validation through Machine Learning

Abstract

This study proposes an IoT architecture for monitoring emissions of polluting gases in vehicles, in response to the growing concern 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, respectively. 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. These data are 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 vehicles in Canada. As a result, a high R² of 99.2% is achieved, along with low values of RMSE and MSE, indicating that the model is making accurate predictions and fits well to the training data. The ANN aims to predict CO₂ emissions and verify CO₂ data from the IoT network. The architecture demonstrates its capability for real-time monitoring and its potential to contribute to pollution reduction.