Air Quality Monitoring System With Mariadb Integration And Real-Time Dashboard For Data Visualization - Syed Abdul Khalim, Syarifah Ainul Mardhiah; A MAJID, HUDA (Progress in Engineering Application and Technology, 2026)
This project presents an IoT-based air quality monitoring system designed for indoor environments in smart cities, integrating MariaDB for data storage and a real-time dashboard for visualization. The system addresses the pressing issue of indoor air pollution, which impacts human health by contributing to respiratory issues and reduced productivity. It utilizes the DFRobot SEN0460 sensor to measure particulate matter. Data is collected via the Heltec WiFi LoRa 32 microcontroller and transmitted through LoRaWAN to The Things Network (TTN). Node-RED processes the data, storing it in MariaDB for historical analysis, while Tago IO provides real-time visualization through interactive dashboards with alerts for exceeding air quality thresholds. The methodology includes hardware integration, LoRaWAN transmission software development, and database scripting, validated through repeated operational tests. The system achieved reliable data transmission with minimal packet loss, accurate sensor readings, and efficient solar-powered operation. This solution enhances urban air quality monitoring, supports data-driven public health policies, and promotes sustainability through energy-efficient design.