Architecting Internet-of-Things-Enabled Digital Twins: An Evaluation Framework

Recently, research on digital twins has been increasing in several areas, extensively serving industries such as agriculture, construction, manufacturing, workplace, and cultural heritage preservation with Internet of Things (IoT) support. However, the utilised digital twin architectures often show differences. These uncovered differences in system architecture and data flow were not evaluated to guide the design procedure of a digital twin system. Accordingly, this paper proposes an evaluation framework to assess the differences brought about by the digital twin architectures and experimentally examines this framework through two typical architectures, widely utilized in the literature. With IoT being a common data source for digital twins, this paper utilizes an indoor environment monitoring IoT testbed to perform a comprehensive evaluation of the latency, accuracy, and AI capacity. The study showed that the architecture significantly influences the system latency, such as reducing the intermediate link of data transmission from the data source to the application to improve the application's real-time capacity.