# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 2 Background and Related Work
### Digital Twins  
The Digital Twin (DT) concept was first introduced in 2003 at Michigan University by [1] and defined as a digital model or representation of a physical object, process or system; these digital models are constructed with real-time data obtained from the physical object as the source. The models emulate the behavior and characteristics of the physical objects, and thus, maintenance and updating are required to ensure model accuracy. Subsequently, synchronization and data exchange between the Digital Twin and the Physical Twin must be guaranteed. The deployment of DT models is supported by architectures such as: IoT, sensors, AI, data analytics, etc. DTs were conceived as enablers for smart industries achieving an improvement in workers' efficiency by means of three principal ideas: the improvement in human object visualization, simplified object comparisons and optimized collaboration. The utilization of DT models offers several advantages, including virtual representation, real-time monitoring, simulation and prediction, life-cycle management, inter connectivity between Digital Twins, and decision making based on real-time data.  
A framework for the deployment of DT systems is presented in [2], which consists of a data layer, integration layer, service layer and middleware cognitive layer. Moreover, in [3] a framework is presented that integrates different technologies such as IoT, AI and cloud computing, among others, and highlights the possibility to support simulations to predict future behaviors; in addition, it is underscored that the DTs are not static in time: DT models are being updated. In this research paper, DT technology is employed to construct a Digital Twin Network (DTN) environment and is introduced next in Sections 3.2 and 3.3.  
A relevant issue is the integration of DTs and data spaces, defined as decentralized ecosystems that manage their own data, as is addressed in [4] by employing several techniques such as APIs, AI techniques, feedback loops and data classification according to the data processing tier; such integration is fundamental to enabling a DT real-time data acquisition from the data space. On the other hand, [5] states that technologies like IoT, 5G networks and cloud computing, among others, have the potential to enrich data quality and volume, reduce the transmission latency and improve the data processing capabilities. In [6], it is stated that XR technology enables a 3-D DT representation, emphasizing the significance of security and privacy techniques to ensure data integrity and the identity of the real twin.  
The DT technology has been exploited in the development of novel network applications, leading to the emergence of the Digital Twin Network (DTN) concept. The DTN has been employed in several researches. For instance, in [7], the DTN is utilized to simulate and analyze security provisioning in network slices and achieve a dynamic policy control; reference [8] presents an efficient network planing system based on real-time data which enables enhanced data management and visualization; in [9], the routing protocols are enhanced by means of real-time data and employing the DTN as a simulation environment for testing the enhanced routing protocols; and paper [10] underscores the importance of network models in DTN environments and describes two model types: Basic (network entities and topologies) and Functional (based on application requirements and scenarios).  
With the objective of facilitating DTN management, the Digital Twin Manager (DTM) concept is introduced in [11]. The DTM framework consists of different modules including physical-digital twins' communication/synchronization, network configuration, a configuration requests buffer, simulation and feedback. In this paper, a different approach for deploying a DTM is presented in Section 3.2.