## 2 Existing Solutions and Similar Testbeds
### Digital Twin Frameworks and Platforms
These platforms offer higher adaptability for research purposes and experimental testbeds due to their open-source nature, but they often require more intricate development and configuration efforts. Furthermore, while these platforms have made significant strides in terms of data management and resource orchestration, they still face limitations in real-time, high-volume data processing, which is essential for next-generation applications that rely on ultra-reliable low-latency communication (URLLC) and large-scale IoT deployments. FIWARE's recent updates emphasize modular interoperability, allowing the flexible integration of different IoT devices through open-standard APIs, yet studies indicate potential scalability challenges in handling the high-frequency data streams necessary for real-time Digital Twins [25]. Eclipse Ditto, while effective for synchronizing device states, still requires advancements in latency handling for time-sensitive IoT applications, as highlighted in recent evaluations [26]. Real-time applications of Digital Twins demand a tremendous amount of data collection, as well as virtualization, analytics, and rendering mechanisms. Also, a Digital Twin representation requires a high computational capacity in both the Edge and Cloud domains, which 5G may not be able to adequately fulfill, but 5G-Advanced and 6G features will [27]. To this end, future Digital Twins applications will need to rely on the adoption of 5G-Advanced technologies [28] that will enable the maximum exploitation of Digital Twin functionalities: (i) 5G-Advanced-IoT (5G-A-IoT) to connect machines and sensors on a large scale; (ii) a distributed IoT-to-Edge-to-Cloud continuum platform composed of resources from different providers in a transparent manner for the end users and verticals; and (iii) the integration of AI/ML analytic tools to furnish Digital Twins and the IoT-to-Edge-Cloud continuum platform with intelligence to enable real-time performance. After a period of experimental evaluation of the first IoT generations and 5G, companies are now moving to the next level of digitalization of their supply chains. Several industrial elements, like sensors, devices, and machines, still have a gap in interconnection [29], which offers the opportunity to be fully exploited in order to take one step forward toward the overall connection of the industrial ecosystem. In this regard, the 3 GPP roadmap for future releases aims to explore new 5G-A-IoT technologies to cover emerging market demands. While 5G adopts a human-centric approach mainly focused on user connectivity aspects and the early demands of verticals, 5G-Advanced needs to go a step beyond to address IoT machine-centered use cases (5G-A-IoT) [30]. At the moment, radio access networks (RANs) in many industrial premises still rely on wired network technologies since wireless interfaces do not satisfy their requirements [31], and machine-type communications scenarios are covered by LTE-based technologies such as LTE-M and NB-IoT, whose capabilities do not meet strict latency demands for real-time monitoring [32]. Requirements in terms of capacity, latency, reliability, and flexibility for automated real-time and collaborative robotics applications can only be met with 5G Rel-18, 5G-Advanced, and beyond.