# B5 GEMINI: AI-Driven Network Digital Twin
## 2 Related Work
The proposal is composed of two communication networks, a local network that connects individual wind turbines with the control system, and a cloud-based communication network that allows operators to configure and operate the wind farm remotely via the Internet. The proposed solution includes ML models to analyze the data generated by the DT to recommend actions and simulate them on the provided graphical user interface (GUI) for continuous feedback. ML techniques are trained on real data and integrated in the NDT. The developed models are then used to assist operators in the management of the wind turbines through the GUI, offering automated suggestions and allowing one to simulate the selected strategies in the NDT to quickly obtain feedback. From a more creative point of view, there have been several interesting proposals that attempt to determine other roles that ML can play within NDT. One of those interesting use cases of ML in the operation of NDT is presented in [2]. As noted by the authors, the behavior of the virtual twin is expected not to disturb the physical space. To that end, ML techniques could be used to validate the next actions of the real system using data generated from the NDT model to prevent unwanted actions in the physical environment. In this scenario, which is also envisaged in [3; 18], ML would serve as an anomaly detection tool for network operators, enabling them to quickly anticipate abnormal situations and mitigate further damage. [PERSON] et al. anticipated 5G-based NDT technology as a city management service that can provide situational awareness and decision-making for city managers and first responders [3]. In particular, they present the case for NDT as a ML-powered tool for the prediction of infectious disease outbreaks. More specifically, they propose to create an NDTthat represents the city infrastructure and the overlaying 5G network. With the increased bandwidth and low latency that 5G networks provide, it will be possible to collect and analyze data from a large number of sensors in real time to track people movement, activities and interactions. Combining this information with past and current epidemiological data and using a combination of ML and conventional data analytic processes, it is possible to predict the most likely outbreak scenarios to occur. In this way, public health officials and other related professionals can be better prepared to develop strategies to respond to these situations more effectively. Extending this idea, the combination of 5G-based DT and ML can be applied to the development of more efficient and ambitious smart city applications [19], such as intelligent transportation, smart energy, and intelligent security. We have also identified some works where DT-based systems are developed with the help of ML techniques [20; 21]. ML and DL methods are also expected to play an important role in NDT model building, as also anticipated in [18], but, to this day, there is a need for further systematic research in this area to find the most suitable methods and models for different types of data and to determine how these models will interoperate with the rest of the elements and contribute to the NDT. In summary, there is a lack of studies on the design and development of NDT and, in particular, on the integration of ML and DL methods in such systems. The research proposed in this article aims to fill this gap and contribute to the advancement of this area by providing B5 GEMINI, an architecture for NDTs that considers AI components by design. Furthermore, all the articles we have collected for this research approach the design and construction of NDTs from a holistic perspective, aiming to implement all their components and interconnections in a complete development cycle to achieve the desired functionality, which requires extensive and careful planning and implies high risk in the development process to achieve a successful and sustainable outcome.