## 2 Existing Solutions and Similar Testbeds
### Edge and Cloud Computing Solutions for IoT Applications
By introducing computation closer to the IoT devices, Edge nodes can handle time-sensitive tasks locally, filtering data before they are sent to the Cloud, thus minimizing latency and bandwidth usage. Technologies such as Kubernetes [44] and OpenStack [45], extended to support Edge environments, facilitate the deployment of microservices across hybrid IoT-to-Cloud architectures. These containerized applications enable scalable, distributed processing across heterogeneous systems, ensuring that critical tasks, such as real-time analytics and AI inference, are performed at the Edge, while the Cloud handles long-term data storage, large-scale analytics, and machine learning model training. However, while these integration technologies significantly improve IoT performance, challenges remain in orchestrating resources dynamically across distributed layers and ensuring secure, seamless interoperability between diverse IoT devices and Cloud services. Edge-to-Cloud orchestration is critical in ensuring seamless resource management across this continuum. Orchestration systems handle the deployment and reconfiguration of services, monitor system performance, and enforce security protocols across heterogeneous environments [46]. AI/ML techniques further enhance the orchestration process by enabling dynamic task offloading, adaptive load balancing, and real-time fault detection. These algorithms optimize task scheduling based on latency, energy efficiency, and resource availability, ensuring that services are allocated to the most suitable layer, whether the Edge, the Cloud, or the device itself, depending on the computational requirements and real-time conditions. This flexibility is essential for maintaining performance across distributed architectures. For instance, ref. [47] offers a valuable model for achieving real-time, adaptable security for dispersed IoT systems. Using Behavior-Interaction-Priority components to ensure data-driven security and model-checking, this approach aligns well with the hyper-distributed architecture's emphasis on security and low latency, providing an additional layer of validation and dependability for Digital Twin and IoT applications that require accurate, real-time data. One of the major challenges in these platforms is managing the heterogeneity and volatility of Edge nodes [46], which often consist of devices with varying computational power, storage capacity, and connectivity. To address this, modern Edge solutions employ resource orchestration frameworks that dynamically allocate tasks based on the capabilitiesand real-time conditions of each Edge node [48]. Technologies such as Kubernetes with KubeEdge [49] and Apache OpenWhisk [50] enable the deployment and management of containerized applications across a distributed Edge infrastructure, ensuring efficient resource utilization and low-latency response times. Frameworks like Microsoft Azure IoT Edge and Google Anthos extend Kubernetes to Edge use cases, but their studies reveal constraints in orchestrating resource-intensive tasks across decentralized nodes under variable network conditions [51]. These platforms allow for dynamic task offloading between the Edge and Cloud, optimizing performance by processing time-sensitive tasks at the Edge and more complex, data-intensive workloads in the Cloud. Additionally, federated learning is being leveraged in Edge environments to mitigate the challenges of decentralized data by training AI models locally on Edge nodes and sharing only the learned parameters with the Cloud, reducing data transmission and enhancing privacy. However, ensuring robust security, fault tolerance, and seamless interoperability across these heterogeneous and often transient Edge devices remains a key technical hurdle in realizing the full potential of Edge computing for IoT applications.