# Architectural Design for Digital Twin Networks
## 2 Related Works  
The concept of Digital Twin Networks has gained significant attention in recent years, yet many research efforts tend to label them broadly as digital twins for networks without delving deeply into the detailed definitions like those proposed by the work in progress of IRTF. Most studies in the field focus on designing DTN frameworks or conducting surveys, but rarely do they involve real-life implementations or tests of a fully operational DTN. Additionally, discussions on implementation choices are not often addressed in the literature.  
For instance, the survey by [PERSON] et al. [8] provides in-depth insights into the key technologies, applications, challenges, and trends of DTNs. They propose a definition of a Digital Twin (DT) comprising three core components: a physical object, its virtual twin, and the continuous mapping between them that ensures their co-evolution. While this aligns with the foundational elements of a DT, their discussion of DTNs remains at a high level and builds upon the general concept of digital twins without addressing the specifics of network implementations in depth.  
Similarly, works by [PERSON] et al. [17, 18] propose DTNs enhanced with Machine Learning (ML) and AI for future 6G networks. These studies adhere somewhat to the IRTF definition but focus predominantly on the conceptual integration of ML and AI rather than providing detailed architectural guidelines or implementation strategies.  
Other research, like that of [PERSON] et al. [19], introduces a DTN approach using Graph Neural Networks (GNN). However, this work diverges from the IRTF definition, functioning more as an updated GNN framework rather than a full-fledged DTN model as outlined by the IRTF. Similarly, [PERSON] et al. [20] describe a DTN that integrates modeling, simulation, and neural networks, closely mirroring the IRTF structure but without the same precision or focus on real-world applicability.  
[PERSON] et al. [21] mainly explore the design aspects of DTNs, closely following the IRTF's guidelines but not extending them into practical implementation. Their work remains theoretical, offering valuable insights into design considerations without addressing the complexities of deployment.  
Other studies take unique approaches that either align partially with or deviate from the IRTF definition. For example, [PERSON] et al. [22] apply a deep learning methodology to DTNs, devising their own structure that closely resembles the application aspects of the IRTF's model. Conversely, [PERSON] et al. [23] refer to their work as a middleware solution for DTNs but focus on a network of digital twins rather than adhering to the IRTF's network-centric perspective.  
In a different vein, [PERSON] et al. [24] propose a flow emulation framework for DTNs, similar to the IRTF's structure, yet they lack a detailed description of the implementation process. Other notable works include [PERSON] et al. [25], who define an Intelligent Digital Twin (IDT) that aligns with the IRTF's vision and employs a deep learning approach, and [PERSON] et al. [26], who use the Cooja simulator [27] as a modeling tool. The latter is particularly notable for applying DTNs to the Industrial Internet of Things (IIoT), expanding beyond the common focus on 5G, 6G, or Wi-Fi networks.  
[PERSON] et al. [28] diverge significantly from the IRTF's framework by splitting their digital twin concept into separate entities for devices and services, thereby not following the structure proposed by the IRTF.  
Lastly, [PERSON] et al. [29] attempted to create a work similar to this one, providing a brief and limited discussion of the issues and challenges within DTNs. However, their work does not delve deeply into the subject, nor does it pair these challenges with concrete solutions or draw on extensive practical experience, leaving significant gaps in addressing the complexities of DTNs.