IoT-based air pollution monitoring and data analytics using machine learning approach

Abstract

Air pollution poses significant risks to both the environment and human health, making real-time monitoring essential for effective mitigation. This paper presents an IoT-based air pollution monitoring system that utilizes sensor networks to collect real-time air quality data on pollutants like PM2.5, CO, and NO₂. The data is transmitted via wireless communication to a cloud platform for analysis. Machine learning algorithms, including decision trees and support vector machines, are applied to predict future pollution trends and detect anomalies. The system’s architecture, from sensor deployment to data analytics, is outlined, highlighting its scalability and adaptability. Experimental results from a 30-day urban deployment demonstrate the system’s ability to capture pollution levels and provide accurate forecasts. By integrating IoT and machine learning, the system offers a cost-effective, real-time solution for monitoring and predicting air pollution, supporting urban planning and public health initiatives.