## 3 A Framework for the IoT-Edge-Cloud Continuum  
Encompassed within a three-year EU-funded research program and currently under development, the COGNIFOG framework embraces the cognitive fog concept to enable the IoT-edge-cloud continuum. This means that the distributed computing resources in the IoT-edge-cloud continuum will be equipped with AI-based functionalities like learning, reasoning, and decision making, allowing them to process data more intelligently and autonomously. These cognitive capabilities will significantly enhance monitoring and orchestration processes within the continuum, improving resource allocation, data transmission, and the detection of malicious activity [48]. At the IoT side, the cognitive fog will provide interoperability facilities so that IoT devices could be easily connected to the continuum and communicate with other edge-side components. At the edge side, it will operate on microservers, extending capabilities typically centralized in the cloud. On the cloud side, it will help to orchestrate all necessary resources, so that an end-to-end service will be provided in a safe and secure way in both directions. Thus, the cognitive-fog concept is multidimensional, aiming to incorporate fully decoupled building blocks that can be interconnected to suit and adapt to specific application requirements. According to the edge-fog-cloud architecture classification proposed in [23], whose three main models are illustrated in Figure 1, COGNIFOG adopts a clustered model, wherein fog nodes are positioned based on geographical constraints to enhance both interfog and intrafog communication capabilities. Furthermore, the COGNIFOG framework will empower the IoT-edge-cloud continuum by harnessing the synergy resulting from combining two key concepts:  
* **Fog computing robustness:** By embracing the fog computing model, COGNIFOG will enable data processing closer to the data source, thus supporting reduced latency, enhanced efficiency, and optimized resource utilization. This decentralization will facilitate IoT-edge-cloud operations, promoting scalability and adaptability. * **Cognitive orchestration:** The cognitive capabilities of COGNIFOG will emphasize the system's capacity to autonomously process, analyze, and react to generated data. This will be empowered by advanced AI-driven analytics that provide real-time insights, proactive issue solving, and an easier understanding of complex data landscapes. These key concepts will be made accessible to end users through an open, modular, adaptable, and secure platform that will cover the entire lifecycle of a software workload--from design and development to execution and data generation, as it can be observed in Figure 2. This architectural approach is inspired by the LF Edge Foundation [49], which brings together industry leaders to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system (OS). Figure 1: Examples of edge–fog–cloud architectures: (**a**) three-layer model; (**b**) clustered model; (**c**) tree-based model (from [23]). Figure 2: The COGNIFOG high-level architecture. When applied to the IoT-edge-cloud continuum, this approach may offer several advantages. Firstly, it ensures interoperability, which is a fundamental aspect for integrating heterogeneous systems and technologies. Secondly, it enables the provision of distinct, loosely coupled tools, each addressing specific functionalities vital to the continuum. Thirdly, it is flexible enough to apply different configurations to its building blocks, customizing them to meet the unique demands of various use cases. Lastly, this level of flexibility also ensures the system's resilience and longevity in a rapidly evolving technological landscape.