IoT-Based Environmental Monitoring for Urban Air Quality Improvement Using Kalman Filters and Clustering Algorithms

Thus, the relatively short period of recent years with sharp growth of rural transformation and expanding industries settled in the regions has resulted in increased air pollution and threat to people's health. In this research, the development of an IoT-based environmental monitoring system that would improve urban air quality through data analysis is proposed. The system also utilizes different IoT sensors for real time AQI data collection comprising particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2). Moreover, to increase the quality of the received data and minimize the differences of the recorded values, the Kalman filters are used to reduce noise and merge multiple sensors' data. In addition, in order to make sense of air quality data, clustering techniques, including K-means and DBSCAN are used to cluster the data into coherent groups that can help detect areas of high pollution frequency and characteristics in various urban areas. Stakeholders in the health, urban and environmental sectors find the information useful because it offers prescriptive insight into air quality interventions. Thus, the research has the purpose of using IoT with Kalman filters and clustering techniques to establish an efficient and scalable real-time monitoring of and enhancement of the air quality in cities benefiting the health of the citizens and quality of the atmosphere.