## 3 The Cloud-Edge Continuum  
The Cloud-Edge-IoT continuum has emerged as a logical evolution in distributed computing, offering a seamless integration of cloud computing, edge computing, and IoT technologies. This continuum represents a holistic approach to addressing the challenges posed by the exponential growth of data generated by IoT devices and the resulting demand for real-time processing and low-latency applications [23]. As urban environments become increasingly digitised and interconnected, the CEI continuum presents a promising framework for developing and deploying Smart City applications that can effectively manage the complexity of urban systems while improving efficiency, sustainability, and quality of life for citizens [24]. At the core of this continuum lies the synergy between three key components: cloud computing, which provides large-scale computational resources and storage capabilities; edge computing, which brings processing closer to data sources; and IoT devices, which generate and collect data from the physical world [25]. As shown in Figure 1, this integration allows for a more flexible and efficient allocation of resources, enabling applications to leverage the strengths of each layer while mitigating their individual limitations. Cloud computing, with its virtually unlimited scalability and centralised management, has long been the backbone of many digital services. However, the increasing number of IoT devices and the demand for real-time processing have exposed limitations in the cloud-centric model, particularly in terms of latency and bandwidth constraints [26]. Edge computing, has emerged as a complementary paradigm, pushing computational resources closer to the data sources and end-users, thereby reducing latency and improving response times for time-sensitive applications. When the edge computing layer includes multiple intermediate tiers for aggregation and resource management, it is commonly referred to as the fog computing layer. The IoT layer, comprising a vast network of interconnected devices, serves as the primary source of data in this continuum. These devices, ranging from simple sensors to complex autonomous systems, generate enormous volumes of data that need to be processed, analysed, and acted upon in real-time or near-real-time. The CEI continuum provides a framework for managing this data flow efficiently, allowing for local processingat the edge when appropriate and leveraging cloud resources for more complex analytics and long-term storage. The successful deployment of the CEI continuum relies heavily on the establishment of robust communication protocols and management frameworks capable of operating seamlessly across diverse infrastructures. Several research efforts and projects are currently exploring unified approaches to data handling, security enforcement, service orchestration, and resource optimisation in such distributed environments. Notably, the European Cloud, Edge, and IoT Continuum initiative (EU-CEI) [27], to which the COGNIFOG project belongs along with others such as CODECO [28], MYRTUS [29] and Swarmchestrate [30], stands out as the most significant European effort toward developing a cohesive and integrated CEI computing continuum. Additionally, Refs. [24; 31] offer comprehensive overviews of research conducted on systems and frameworks within the CEI continuum landscape, encompassing both production-ready solutions and research initiatives. In the context of smart cities, the CEI continuum offers significant potential for addressing urban challenges. Smart city applications, such as intelligent transportation systems, energy management, public safety, and environmental monitoring, can benefit from the distributed nature of this continuum [32]. However, the implementation of the CEI continuum in Smart City contexts is not without challenges.