A Data-Driven Digital Twin Network Architecture in the Industrial Internet of Things (IIoT) Applications

Autor: Isah, Abubakar, Shin, Hyeju, Aliyu, Ibrahim, Oh, Sangwon, Lee, Sangjoon, Park, Jaehyung, Hahn, Minsoo, Kim, Jinsul
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: A new network named the "Digital Twin Network" (DTN) uses the "Digital Twin" (DT) technology to produce virtual twins of real things. The network load and size continue to grow as a result of the development of 5G, the Internet of Things, and cloud computing technology as well as the advent of new network services. As a result, network operation and maintenance are becoming more difficult. A digital twin connects the real and digital worlds, exchanging data in both directions and revealing information about the progression of a network process. The framework of the Industrial Internet of Things, data processing, and digital twin network is taken into consideration in this article as a key aspect. This paper proposed a data-driven digital twin network architecture, that comprises the physical network layer (PNL), the digital twin layer(DTL), the application layer (AL), and what those layers encompass and beyond. Also, we presented DTN data types and protocols to be used for data integration.
Databáze: arXiv