## 2 Existing Solutions and Similar Testbeds
### Edge and Cloud Computing Solutions for IoT Applications  
The advent of hyper-distributed platforms integrating the Internet of Things (IoT) and Edge and Cloud computing is key to tackling the complex challenges in modern logistics and industrial settings, especially for real-time Digital Twin applications. As the 6G network paradigm emerges, the development of systems capable of handling vast data from IoT devices while meeting the low-latency and high-throughput demands of real-time applications becomes increasingly crucial. The integration of IoT, Edge, and Cloud layers forms a cohesive platform where data from IoT devices are first processed at Edge nodes and then analyzed further in Cloud infrastructures [33]. By performing initial data filtering and aggregation at Edge nodes, the platform minimizes unnecessary data transmission to the Cloud, which reduces bandwidth usage and alleviates network congestion. This approach is especially beneficial in scenarios with high data volumes, where only relevant or summarized data need to be forwarded for further analysis, ultimately improving both system efficiency and responsiveness. This distribution of computational resources is necessary to optimize performance while minimizing latency, which is vital for real-time applications like Digital Twins that replicate physical environments in real time [34]. In particular, ref. [35] presents an architecture focused on real-time optimization and control within 6G Digital Twin Networks (DTNs). Although primarily focused on DTNs rather than IoT-Edge-Cloud systems for logistics, its architectural principles and real-time control insights indirectly contribute to the development of a hyper-distributed IoT platform, aligning with long-term 6G objectives for high-performance, ultra-reliable systems. Another example [36] introduces a Cloud-based framework for modular and context-aware services in healthcare, but its adaptable, reconfigurable design directly applies to hyper-distributed IoT systems in logistics and manufacturing. By understanding such modular frameworks, we can develop a flexible IoT-Edge-Cloud platform capable of seamlessly handling multiple applications and efficiently managing resource allocation. This flexibility is paramount for high-performance 6G testbeds that support Digital Twin applications and adapt to varying industrial and real-time demands. IoT-to-Cloud technologies serve as the backbone for connecting a vast array of IoT devices to centralized Cloud infrastructures, enabling data aggregation, analysis, and decision-making at scale. Key technologies facilitating this integration include protocols like Message Queuing Telemetry Transport (MQTT) [37], Hypertext Transfer Protocol (HTTP) [38], and Constrained Application Protocol (CoAP) [39]. These lightweight communication protocols are designed for efficient data transfer between resource-constrained IoT devices and Cloud services, with MQTT being particularly popular for its low-bandwidth consumption and reliable messaging over unreliable networks. Coupled with these protocols, middleware platforms like AWS IoT Core [40], Microsoft Azure IoT Hub [41], and Google Cloud IoT [42] offer essential services, such as device management, data storage, and analytics. These platforms provide seamless integration between IoT devices and Cloud environments, allowing organizations to scale their deployments, implement security mechanisms, and leverage Cloud-native services for data processing and AI-driven insights. Furthermore, Apache OpenWhisk, an open-source platform, allows for serverless function execution in Edge environments, providing flexible resource scaling; however, its performance in latency-sensitive applications is limited by event-driven processing speeds, as reported in recent studies [43]. In addition to communication protocols and Cloud platforms, Edge computing plays a pivotal role in enhancing IoT-to-Cloud integration by decentralizing data processing and reducing the load on Cloud infrastructures.