IClean: An Intelligent Industrial IoT Framework for Automatic Sustainable Air Quality Monitoring

Abstract

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Air pollution monitoring systems are essential for evaluating rural and industrial environmental quality for safeguarding public health. This study presents a comprehensive IoT-based framework that uses low-cost sensors and machine learning algorithms for real-time monitoring of various pollutants, including LPG, methane, CO, alcohol, PM2.5, PM10, temperature, and humidity. The system gathers sensor data from a gateway node, which is then processed using Support Vector Machines (SVM) and Random Forest Regression (RFR) models to predict pollutant concentrations. Our approach features innovative methodologies for data validation, anomaly detection, and predictive modeling, employing Root Mean Squared Error (RMSE) as the performance metric. The model achieved a remarkably low RMSE value of 0.022, significantly improving the accuracy and reliability of air quality assessments. Experimental results highlight the system's capability to capture complex environmental patterns and predict pollutant levels with high precision. This research intends air pollution monitoring from cost-effective Internet of Things (IoT) solutions and machine learning techniques. Additionally, the user interface is designed for mobile applications, offering real-time data access, alerts, and notifications, thereby enabling personalized environmental health management and targeted pollution control strategies in industrial areas.