Phygital Twin IoT: A Hardware-Software Decomposition Architecture for Scalable and Secure Digital Twin IoT Systems

This paper introduces Phygital Twin IoT (PT-IOT), a new and innovative architecture that combines a Digital Twin (DT) with a Physical Twin (PT) to create a flexible and scalable framework for modeling and managing real-world systems. The PT replicates the hardware components of the system, particularly the microcontroller, as a virtual counterpart that is programmed directly at the hardware level. This design ensures that the DT remains independent of the underlying hardware, enabling seamless adaptability to technological upgrades or security enhancements. The proposed architecture is based on the MQTT protocol to facilitate communication between physical and virtual components through an E-Program, which serves as a software coordinator. This approach supports real-time synchronization between physical and virtual nodes, making it highly suitable for large-scale applications such as smart cities, smart parking, and smart buildings. Notably, only the PT requires reprogramming when hardware changes occur, preserving the integrity of the DT. An additional feature of this framework is the ability to execute the E-Program externally on remote servers, allowing for continuous operation and advanced decision-making processes. This capability is particularly advantageous for critical applications, including emergency management and resource optimization. Analytical modeling of this architecture indicates an estimated 15–20% reduction in communication latency compared to direct microcontroller interfacing, while maintaining full functional equivalence. The proposed architecture was validated using the CupCarbon platform and its scalability and practicality make it a promising solution for modern IoT-based systems. Analytical results demonstrate superior performance in terms of scalability and energy efficiency compared to traditional DT architectures. Future research will focus on improving the framework with advanced decision-making algorithms and deploying it in real-world scenarios.