IVEMPS: IoT-Based Vehicle Emission Monitoring and Prediction System - Omar Farrag; Ahmad Mansour; Baraa Abed; Amr Abu Alhaj; Taha Landolsi; A. R. Al-Ali (IEEE Access)
Greenhouse gas emissions are a critical issue that can have severe environmental impacts such as global warming and climate change. Gases emitted by road transportation vehicles constitute a significant part of global greenhouse gas emissions. Therefore, this paper proposes an IoT-based real-time vehicle emission monitoring and prediction system to swiftly identify high-emitting vehicles (HEVs). The system consists of an on-board system with wireless networking capabilities housing seven gas sensors (CO, CO2, NO, NO2, H2S, HC, and O2) for real-time data acquisition. It also includes a cloud computing layer for data storage and emission forecasting for predictive maintenance using deep learning time series forecasting techniques. Finally, the system provides visualizations of the collected data through a dedicated web-based dashboard. Trained forecasting models achieved a prediction accuracy of 83% and an F1-score of 0.79. Additionally, real-world on-road test data validated the system’s effectiveness in identifying current and potential HEVs in real-time while generating large datasets to support ongoing research and development aimed at reducing vehicle emissions.