## 2 Existing Solutions and Similar Testbeds
### Digital Twin Frameworks and Platforms  
Digital Twins are becoming a paradigm-changer for different verticals, transforming how products and services are made and delivered, and allowing for the full digitalization of industrial elements (data, sensors, robotics, vehicles, etc.). Digital Twins are especially relevant for logistics and industrial environments, as they optimize the manufacturing process to reduce costs and increase operational efficiency and flexibility [13]. In addition, Digital Twins have immense potential in healthcare [14], smart cities [15], and robotics [16]. For instance, in healthcare, Digital Twins could simulate patient-specific models for personalized treatment planning, enhancing precision medicine. In smart cities, they can improve urban mobility management to reduce traffic congestion and fuel consumption. Furthermore, in robotic systems like drones and self-driving vehicles, Digital Twins enable better decision-making by providing real-time updates and scenario-based predictions, significantly improving safety and reliability. Several Digital Twin frameworks and platforms have emerged to meet the growing demand for the real-time simulation and management of physical entities. Siemens' MindSphere [17] and General Electric's Predic [18] are among the leading platforms that provide end-to-end solutions for industrial IoT and Digital Twin implementations. MindSphere is a Cloud-based open IoT operating system that allows businesses to connect products, plants, systems, and machines, enabling robust analytics and the creation of Digital Twins for predictive maintenance and optimization. GE's Predic, on the other hand, is a dedicated industrial IoT platform that integrates with AI/ML tools to develop Digital Twins for industrial assets. Both platforms emphasize scalability, offering integration with Edge devices, Cloud infrastructures, and third-party applications, making them suitable for large-scale industrial ecosystems. However, despite their broad capabilities, both platforms face challenges in supporting ultra-low latency and real-time synchronization across distributed environments, particularly in scenarios requiring immediate responses, such as autonomous systems or robotics. While these platforms are robust in terms of industrial IoT capabilities, comparisons with certain studies [19; 20] reveal gaps in achieving the ultra-low latency and high scalability necessary for distributed Edge environments. Studies show that MindSphere and Predic face limitations in seamless interoperability, i.e., smooth and application-transparent integration, between Edge and Cloud layers, often relying on proprietary integration methods that restrict flexibility in hyper-distributed architectures. Recent frameworks like IBM's Maximo Application Suite (MAS) [21] and Hitachi's Lumada [22] further exemplify industry efforts to build end-to-end IoT and Digital Twin solutions. IBM MAS focuses on asset performance management and predictive maintenance through AI-driven analytics, enhancing operational efficiency and supporting large-scale industrial IoT deployments. Lumada, developed by Hitachi, offers a modular platform for creating Digital Twins that optimize manufacturing and logistics operations through data-driven insights, with specific capabilities for Edge deployment to minimize latency and improve real-time responsiveness. Open-source solutions such as Eclipse Ditto [23] and FIWARE [24] also play a significant role in advancing Digital Twin applications by offering more customizable and flexible frameworks. Eclipse Ditto focuses on managing digital representations of physical devices by providing a middleware layer that facilitates the synchronization of data and state between Edge devices and Cloud services. FIWARE, combined with its IoT and context-broker components, enables the development of Digital Twins across smart city and industrial use cases by integrating a wide range of IoT data sources.