A Digital Twin Framework for Indoor Environmental Quality using IoT and Wireless Mesh Networks

Indoor and outdoor air quality and environmental conditions are considered as public health concerns, especially in hospital settings. While existing studies, such as Menguito et al. [9], use wireless mesh networks for indoor air quality monitoring, they lack outdoor measurements and scalable environmental sensor nodes. Additionally, there is minimal integration of digital twin technology in hospital air quality and environment systems, which could enhance real-time visualization and intuitive analysis. This project developed a Wireless Mesh Network (WMN) and Internet of Things (IoT)-based digital twin system to monitor both indoor and outdoor air quality and environmental conditions. Key enhancements include the use of the low-power Seeed XIAO nRF52840 developmental board, separate environmental sensor node for scalability and implementation of OpenThread for energy-efficient and resilient communication among sensor nodes. Compared to previous work that used WiFi with an average power consumption of 1.9W, this system achieved a significantly lower average consumption total of 840.1mW, demonstrating improved energy efficiency. Autodesk Tandem provided a 3D interface for real-time data visualization, trend analysis and intuitive heatmaps. Onsite deployment generally recorded good to moderate levels of CO2, CO, PM2.5, relative humidity, and temperature for both indoor and outdoor. However, indoor TVOC levels were relatively high, suggesting thermal effects and pollutant concentration within the area. The Enhanced Air Quality Index, which combines air quality index and humidex, indicated good air quality, with occasional spikes due to certain events. Further analysis revealed positive correlations among all indoor and outdoor air quality and environment parameters.