# B5 GEMINI: AI-Driven Network Digital Twin
## 2 Related Work  
The concept of DT as a virtual copy associated with a physical entity was introduced in 2003 by [PERSON] at a Product Lifecycle Management (PLM) conference [5]. NASA was an early adopter of the concept, starting to use DTs to monitor spacecraft health since 2010. However, the term \"digital twin\" was first coined in 2011 by [PERSON]. In practical terms, the first DT was developed by [PERSON] et al. [6] to predict the structural integrity of next-generation fighter aircrafts over their lifetime. The concept began gaining traction in 2015 due to the falling costs of sensors, processors and data storage, and the increasing availability of broadband wireless connectivity, leading to the so-called \"IoT revolution\" [7]. The exchange of information between a physical object and its DT is a crucial element for the creation of a successful DT: the virtual twin must quickly adapt to changes in its physical counterpart, just as the physical object must promptly respond to interventions taking place in the virtual twin. This transfer of information from the physical environment to its virtual twin is made possible using communication networks that connect the two parties. NDT result from the virtualization of the communications infrastructure of a real network, allowing the interconnection of DTs that are part of a complete system as well as supporting the bidirectional data flow with the real network. Thanks to this two-way communication, NDT is expected to enable a new wave of AI-powered applications that can more accurately predict outcomes and make better decisions to support network operators in the deployment and operation procedures involved in network management. In this section, we review the most interesting applications that we discovered in our research. At first, we observed considerable research effort leveraging this technology towards the fulfillment of the expectations placed on 5G and beyond communications. The potential of edge and cloud computing platforms to manage DT-related data should be considered. In particular, edge computing allows for the leveraging of computing capabilities of the distributed nodes at the edge of the network, allowing one to reduce the bandwidth and dependency with the cloud gateway [8]. There is a large body of work focused on providing solutions to further minimize network and data processing latency between end users and edge servers to facilitate the adoption of intelligence-driven applications. In our research, we have observed a particular interest in looking for the most optimal ways to place and migrate DT in a network that combines these two paradigms [9], i.e., an NDT optimization task in which ML and DL techniques emerge as the most attractive solution. They propose a new approach based on DRL to find the optimal placement for DTs in order to reduce system latency. This algorithm takes into account both the placement strategy and the mobility of users. In addition, they suggest using transfer learning to migrate DTs to new users. On the same line, [PERSON] et al. proposed to combine DT with edge computing to efficiently connect IoT devices with CPS in an Industrial IoT scenario (IIoT) [9; 10; 11]. More specifically, they propose to model DTs of the physical devices present in the user layer and place them in the edge to allow their operation from the CPS to achieve a better optimization of the manufacturing process. For this purpose, they propose an asynchronous federated learning scheme to build the DT of those devices using ML techniques. In federated learning, different devices in a network collaborate to train a shared model. This can be conducted by dividing the dataset among the devices, and each device trains its own model. These individual models can then be merged to create a single, more powerful model. Federated learning allows data collected by different entities to be exploited, eliminating the need for a large, centralized ML infrastructure.