Novel Framework for Real Time Indoor Air Quality Monitoring Using IoT and Mobile Robotics

Abstract

Using IoT technology and mobile robotics, this study presents a novel framework for real-time indoor air quality monitoring, addressing the need for precise, flexible air checks in indoor environments. In order to construct a mobile robotic platform that can continually collect, display, and detect gas levels in real time, the system includes an ESP32 microcontroller, a MQ-135 gas sensor, an IP camera, and a MATLAB interface. By recording changes in air quality across the environment, this mobile setup compared to conventional stationary sensors, can move across various locations and improve detection accuracy. Sensor data is immediately collected for further analysis and transmitted to the Blynk platform for ongoing monitoring. The heatmaps generated by MATLAB provide a clear visual representation of gas levels, allowing users to better identify and solve air quality issues. Early testing demonstrate the system's ability to improve air quality management by giving live data from various regions of an indoor space, allowing for the rapid identification of locations with greater gas concentrations. Future research will include integrating machine learning-based autonomous navigation, improving ESP-NOW communications for wider application, and producing advanced heatmaps for bigger regions. This framework has the potential to improve air safety in indoor environments. This study describes the system's design, operation, and flexibility, providing a realistic and scalable solution for real-time indoor air quality monitoring.