STUE Digital Twin Project
STUE Digital Twin is an IoT-driven smart monitoring platform that creates a real-time digital representation of a physical environment by integrating sensor networks, cloud computing, and artificial intelligence. The system leverages Arduino and Raspberry Pi devices to collect sensor and image data, Firebase for real-time data synchronization, a Flask backend for data processing and AI inference using YOLOv6, and a Next.js web application for visualization and user interaction. By bridging the physical and digital worlds, the platform enables remote monitoring, intelligent analysis, and data-driven decision-making through an interactive dashboard.
The images below show some of the architecture and results of the product.
Develop a Digital Twin platform capable of collecting real-time data from physical sensors and cameras, processing the data through cloud and AI services, and providing an intuitive interface for monitoring and analysis. The challenge was integrating heterogeneous hardware, cloud infrastructure, AI inference, and web technologies into a unified system.
Designed and implemented an end-to-end Digital Twin architecture using Arduino and Raspberry Pi for data acquisition, Firebase for real-time data and image storage, Flask for backend API services, and YOLOv6 for AI-based image analysis. Developed a Next.js dashboard that visualizes sensor readings, AI predictions, and system status in real time.
Successfully delivered a functional Digital Twin system that synchronizes physical and digital environments in real time. The platform enables remote monitoring, AI-assisted analysis of captured images, centralized data management, and interactive visualization through a web-based dashboard, demonstrating the feasibility of integrating IoT, cloud computing, and computer vision technologies into a single solution.
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