## 1 Introduction  
The 6G era envisions connecting the human, digital, and physical worlds and revolves around their interaction: a human world of our senses, bodies, intelligence, and values; a digital world of information, communication, and computing; and a physical world of objects and organisms [1; 2]. In this three-pillar vision, depicted in Figure 1, the concept of Digital Twins is presented as a mix of the best of each world's features [3]. Digital Twins are 3D virtual representations that serve as real-time digital counterparts of physical entities, providing ubiquitous tools for the simulation and analysis of complex environments. Taking advantage of the Internet of Things (IoT) data gathered by sensors, machines, robots, and cameras, Digital Twins enable continuous monitoring and adaptive control and optimize resource use and decision-making by leveraging real-time data for a dynamic virtual representation of physical assets. The use of Digital Twins is particularly advantageous in complex logistics and industrial environments, where the coordination of moving objects (e.g., vehicles, cranes, ships, or robots) is critical. Using data from sensors, cameras, and LiDARs, Digital Twins can monitor the real-time locations of these objects, optimizing the scheduling and coordination of resources. Through immersive cockpits, operators can visualize a virtual representation of the entire logistics or factory line, detecting bottlenecks and adjusting workflows in real time. The integration of artificial intelligence (AI)-powered systems, including computer vision and sensor analysis, enables Digital Twins to autonomously monitor the operation. When combined with machine learning (ML), Digital Twins can even predict hazards (e.g., traffic collisions, access to restricted areas, equipment failures, or environmental factors) and send alerts to operators. Nonetheless, the implementation of real-time Digital Twins poses significant challenges to communication networks. Digital Twins rely on real-time data to create virtual replicas of physical environments, including video streams (for 2D object detection, collision avoidance, or security monitoring), LiDAR streams (for precise localization and 3D object detection), or telemetry (e.g., temperature, position, or operational status). Given the computing capacity required to process all this information with low delay, the data gathered by IoT devices must be offloaded to more powerful servers. But, if not managed correctly by communication systems, Digital Twins have the potential to inundate network infrastructures, reducing efficiency and increasing latency, which impedes the real-time functioning of the application. Moreover, the number of IoT devices interconnected in a specific Digital Twin application may be expected to grow in the future, increasing the saturation of the networks [4]. To mitigate this effect, computations must occur closer to the data source, reducing latency and improving response times. This is particularly beneficial in time-sensitive Digital Twin applications such as autonomous driving [5], industrial robotics [6], and immersive teleoperation [7], where even milliseconds of delay can have critical consequences. As  
Figure 1: A conceptual diagram based on the Hexa-X 6G vision of interconnected worlds [1], now also encompassing Digital Twins, IoT, and Edge and Cloud computing components. The physical world comprises IoT devices, sensors, and autonomous machines that collect and act on real-world data, forming the basis of cyber-physical twinning with real-time feedback. The digital world represents a virtual replica of the physical world, comprehended along the IoT-Cloud-Edge computing continuum, which facilitates data processing, AI/ML applications, and storage for large datasets. The human world includes haptic devices, user interfaces, and Human–Machine Interfaces to enable immersive interaction with both digital and physical assets.