## 2 State of the Art
### IoT-Edge-Cloud Continuum Frameworks  
Numerous initiatives are addressing global communication and management protocols within the IoT-edge-cloud continuum, aiming to establish a unified data management system that supports multiple heterogeneous services, implements appropriate security mechanisms, and ensures optimal resource allocation. However, to date, there is a notable absence of a unified open-source framework or a reference standard that facilitates comprehensive operations across large-scale infrastructures [13]. In the following section, to the best of our knowledge, we will briefly review some of the most significant initiatives.  
The UNITE architecture, presented in [14], stands for integrating computing, networking, and storage resources across the IoT-edge-cloud continuum. This architecture enables end users to utilize all the aforementioned resources in a seamless and straightforward manner. Furthermore, it conducts ongoing resource monitoring and employs AI methods to predict and proactively address potential issues, such as network congestion and cloud server overload.  
An innovative platform-as-a-service (PaaS) is proposed in [15] to facilitate the dynamic provisioning and management of cloud-native, multi-access edge computing (MEC)-based IoT applications across hybrid clouds. Leveraging open source projects from the Cloud Native Computing Foundation (CNCF) [16] and the Apache Software Foundation [17; 18], the described architecture simplifies development and orchestrates data-intensive, intelligence-driven IoT applications, ensuring security and privacy.  
To enhance the self-management of the IoT-edge-cloud continuum, the authors of [19] introduce the concept of the _intent-based_ cognitive continuum, where the possible intents encompass constraints, preferences, expectations, goals, the type of entities that are relevant to these intents, and the relationships among them. By means of AI techniques, a unified data layer, and intent-based human-AI interaction, their approach addresses limitations in cloud resource optimization. This contributes to sustainable elasticity by providing a high-level means to express application performance requirements.  
In the simulation domain, an osmotic computing environment across an edge-cloud continuum is introduced in [20]. The authors characterize osmotic computing as the dynamic migration of workload between a cloud data center and edge devices, which is triggered by performance and security events. Consequently, they successfully model and simulate an IoT application centered around electricity management and billing within a smart city, operating over a heterogeneous edge-cloud environment.  
The research in [21] proposes an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning (ML). Its two-layered scheme, combining IoT-edge and cloud processing, employs advanced algorithms for efficient data injection, processing, and storage, thus enhancing cluster management and addressing structural challenges in handling massive and heterogeneous data from IoT devices.  
Existing frameworks often overlook the reality that certain constrained IoT devices lack the capacity to support specific tools. On this matter, RAMOS constitutes a novel reference architecture for a meta-OS designed to address the limitations of current IoT-edge-cloud infrastructures by facilitating a dynamic, distributed, and trusted continuum for data-intensive and ML-based applications [22].  
Lastly, for a comprehensive review on further frameworks for the IoT-edge-cloud continuum, we refer the reader to [13; 23].