A Low-Cost, Real-Time Environmental Digital Twin Architecture for Predictive Analytics in Smart Facilities
Abstract The integration of Internet of Things (IoT) and Digital Twin (DT) technologies provides a transformative paradigm for intelligent monitoring and predictive maintenance in industrial environments. Traditional environmental control systems rely on reactive architectures, leading to potential equipment failure before corrective actions are deployed. This paper presents a low-cost, real-time Environmental Digital Twin architecture designed to shift facility management from reactive to predictive. Utilizing a dual-node edge computing architecture—featuring an ATmega2560 for real-time spatial mapping and physical actuation, paired with an ESP32 for wireless network bridging—the physical system streams high-fidelity telemetry and proximity data. Simultaneously, a full-stack software architecture featuring a Python-driven backend and a React-based WebSocket dashboard provides low-latency visualization and algorithmic threshold monitoring. The system actively logs historical temperature and humidity data to forecast critical thermal breaches, proving that enterprise-grade predictive analytics can be achieved using accessible, scalable hardware.