Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security

Abstract

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

Keywords

Digital Twin Networks / Network architecture / Privacy and security / Federated learning / Blockchain / Internet of Vehicles / 6G / Supply chain

CRediT authorship contribution statement

Akshita Maradapu Vera Venkata Sai: Writing - review & editing, Writing - original draft, Investigation, Formal analysis, Data curation, Conceptualization. Chenyu Wang: Writing - original draft, Formal analysis, Data curation, Conceptualization. Zhipeng Cai: Writing - review & editing, Writing - original draft, Investigation, Conceptualization. Yingshu Li: Writing - review & editing, Writing - original draft, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Science Foundation (NSF) of the USA (2146497, 2416872, 2315596 and 2244219).

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