Edge-Based Air Quality Monitoring Network Using Low-Cost Sensors and MQTT Protocol

I. INTRODUCTION

Reference-grade air quality monitoring stations are expensive to deploy at the density needed to capture hyperlocal pollution variation, motivating dense low-cost sensor networks despite their well-documented drift and cross-sensitivity issues.

II. METHODOLOGY

Forty nodes equipped with low-cost optical PM2.5 sensors and electrochemical NO2 sensors were deployed across a 12 square kilometre urban area, publishing readings every 60 seconds over MQTT to an edge gateway. A random forest regression model, trained against a co-located reference-grade analyser, was applied to correct for temperature, humidity and sensor-age drift before data was pushed to a public dashboard.

III. RESULTS AND EVALUATION

Following calibration, network-wide mean absolute error for PM2.5 dropped from 12.8 to 4.1 micrograms per cubic metre against reference measurements, a 68 percent improvement, while NO2 MAE improved from 9.3 to 3.7 parts per billion. Estimated deployment cost was approximately 3 percent of an equivalent-density reference-grade network.

IV. CONCLUSION

Machine-learning calibration substantially closes the accuracy gap between low-cost and reference-grade air quality sensors, supporting dense urban deployment at a fraction of conventional cost. Future work will investigate transfer of calibration models across sensor batches without site-specific co-location.

V. REFERENCES