Learning Methods for IoT: Use Cases of Air Pollution Monitoring

Abstract

Air pollution monitoring is crucial for assessing the health risks posed by pollutants, identifying pollution sources, and developing effective strategies for reducing pollution and protecting public health. The Indian government has taken initiatives, which provides real-time air quality data to the public and raises awareness of air pollution levels. The initiative measures several pollutants, including Particulate Matter (PM), and categorizes Air Quality Index (AQI) level into six categories ranging from “Good” to “Severe”. The air pollution sensors employed for calculating the AQI are associated with several limitations. Consequently, the central objective of this thesis is to estimate the AQI without relying on any pollution sensor. To achieve this goal, the proposed methodology employs real-time traffic data and images to estimate the AQI in real-time. Firstly, this thesis propose an image processing based technique to estimate the AQI levels using traffic images and weather parameters, which can be used in rural and sub-urban areas where sensors are hard to deploy and maintain. This approach allows for real-time estimation of AQI through smartphones and can be used portably. The proposed method achieves up to 90% accuracy for the AQI classification. Furthermore, a feature-rich dataset is made publicly available to encourage further research. After that, a novel method based on the Internet-of-Things (IoT) and Machine Learning (ML) is proposed to estimate the AQI using real-time traffic data. To build a rich traffic dataset, PM monitoring nodes were deployed in 15 diverse traffic scenarios across Indian roads, and digital map service providers were utilized. Three ML models, namely Random Forest (RF), Support Vector machine (SVM), and Multi Layer Perceptron (MLP), were trained on this dataset to predict AQI categories into five levels. Experimental results demonstrate an accuracy of 82.60% and an F1-score of 83.67% on the complete dataset. In addition, individual node datasets were used to train ML models, and the behavior of AQI levels was observed.