Implementation of Wireless Sensor Network for IoT-Based Air Quality Monitoring System

Abstract

This study discusses the application of an Internet of Things-based Wireless Sensor Network for measurable, reliable, and economical air quality monitoring. The system architecture includes low-power sensing nodes, wireless communication, edge gateways, and cloud services for storage and analytics. The monitored parameters include PM2.5, PM10, carbon monoxide, carbon dioxide, temperature, and humidity. Accuracy is improved through two-stage calibration and signal filtering using the median and simple Kalman filters. The MQTT protocol was chosen for bandwidth efficiency and reliability, while the mesh topology extends range, reduces latency, and improves resilience to node failure. The web application provides spatiotemporal visualization, heat maps, warning thresholds, and daily summaries to support operational decisions and policy. Field testing in urban areas showed average latency below two seconds, packet loss below one percent, high correlation with reference stations, and operational stability for more than seven days with batteries and small solar panels. These results confirm that the integration of WSN and IoT can produce a scalable, easy-to-install system that is relevant for urban environmental management. This design has the potential to support public participation, enrich air quality databases, and enable early response to pollution through automated notifications and easily accessible analytical dashboards.  Further research will explore edge AI, energy savings, and open standard interoperability.