Abstract

Rapid urbanization and industrialization have significantly increased air pollution levels, adversely affecting public health and environmental sustainability. Traditional monitoring systems often suffer from limited spatial coverage and delayed data analysis, making real-time pollution assessment challenging. Existing approaches struggle to efficiently process the large-scale, heterogeneous data generated by IoT-enabled Cognitive Radio-Based Sensor Networks (CRSNs). Conventional machine learning models often fail to capture complex temporal and spatial patterns in pollution dynamics, limiting predictive accuracy and early warning capabilities. This study proposes a transformer-based deep learning framework combined with IoT- enabled CRSNs for accurate and real-time air pollution monitoring. The CRSN comprises distributed sensors collecting continuous data on particulate matter (PM2.5, PM10), NO2, CO, O3, and other pollutants. The transformer model controls its self-attention mechanism to capture temporal dependencies and inter-sensor correlations, enabling robust prediction of pollution trends. Data preprocessing involves normalization, anomaly detection, and feature embedding to enhance model performance. Comparative experiments are conducted against conventional LSTM and GRU models to evaluate prediction accuracy and system responsiveness. Experimental results establish that the transformer-based model achieves superior performance with a mean absolute error (MAE) of 4.2 µg/m³ for PM2.5 prediction, outperforming LSTM (MAE = 6.1 µg/m³) and GRU (MAE = 5.8 µg/m³). The proposed system provides accurate, fine- grained pollution maps in real-time, enabling timely alerts and informed decision-making for environmental authorities.

Authors

Mayur N. Bhalia¹, G.P. Suja²
Government Polytechnic, Rajkot, India¹, Muslim Arts College, India²

Keywords

IoT, Cognitive Radio-Based Sensor Networks, Air Pollution Monitoring, Transformer Deep Learning, Real-Time Prediction