## 4 Development and Validation of the Platform
### Digital Twin Application Prototype  
The experimental setup is designed to support and validate a wide range of Digital Twin applications. One notable example is an immersive remote driving application, shown in Figure 6. This application in particular has been tested on Edge Site 1 but could also be deployed on Edge Site 2, as it shares a very similar architecture. It features the remote control of two mobile robots over the private 5G network, while a Digital Twin of the robots, the network, and the scenario is represented in the user interface.  
The robots, situated outdoors, are equipped with 360 deg cameras and an array of sensors, such as LiDAR, GNSS, and IMUs, to capture high-fidelity environmental data in real time. These sensor data are transmitted to immersive cockpits located indoors at the laboratory, where users experience a fully immersive environment through racing seats, pedal controls, VR headsets, and haptic vests. This setup simulates an authentic remote driving experience, where users perceive the robot's point of view and receive real-time haptic feedback based on robot-environment interactions. The integration of control, perception, and telemetry data (gathered by the Digital Twin) ensures that accurate, real-time feedback is provided to the operators, allowing for seamless bidirectional communication between the physical and digital realms.  
Each robot and cockpit is connected via the 5G network, with robots using the n40 band for robust outdoor connectivity, while the indoor cockpits rely on a direct connection for enhanced bandwidth and performance. In this context, the high-speed data exchange between the robots and the control center is fundamental to ensuring the quality of both visual  
Figure 5: A comparison of the theoretical maximum data rates against the measured rates across different frequency bands (n78 and n40) for both download (DL) and upload (UL) links.  
and haptic feedback, which is critical for enhancing the operator's situational awareness and control accuracy.  
The application's architecture employs Edge computing to process incoming data streams directly at the Edge server, avoiding the need to route data to centralized Cloud servers, thus minimizing delays in teleoperation scenarios where split-second decision-making is essential for safe and efficient robot control. Furthermore, the distributed approach ensures system scalability, allowing the deployment of additional robots without significantly increasing network overhead or latency, which is critical as the platform expands to cover more complex missions or larger areas.  
The Edge server executes two distributed applications: AI-based object detection (using models like YOLO) to identify pedestrians and obstacles in order to avoid collisions, and a Cloud Robotics Platform that plays a critical role in integrating the physical, digital, and virtual worlds. The latter is the core of the Digital Twin data, which are stored using InfluxDB and Network-as-Code (NaC) and interpreted to create a virtual replica of the robot, the network, and its environment, which are represented in the 3D user interface.