Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
This addresses the problem of costly and slow Digital Twin development for IoT applications by enabling automated construction without domain knowledge.
The paper tackles the high barrier and slow development of Digital Twins by proposing Digital Twin Graph (DTG), a fully automated and domain-agnostic framework that constructs digital twins using a data-driven graph learning approach, eliminating the need for human experts.
With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world. Digital Twin has long been believed to rely heavily on domain knowledge, but we argue that this leads to a high barrier of entry and slow development due to the scarcity and cost of human experts. In this paper, we propose Digital Twin Graph (DTG), a general data structure associated with a processing framework that constructs digital twins in a fully automated and domain-agnostic manner. This work represents the first effort that takes a completely data-driven and (unconventional) graph learning approach to addresses key digital twin challenges.