The Internet of Things, Fog, and Cloud Continuum: Integration Challenges and Opportunities for Smart Cities

Abstract

This paper explores the broad area of Smart City services and how the evolving Cloud-Edge-IoT continuum can support application deployment in Smart Cities. We initially introduce a range of Smart City services and highlight their computational needs. We then discuss the role of the Cloud-Edge-IoT continuum as a technological platform to meet those needs. To validate this approach, we present the COGNIFOG platform, a Cloud-Edge-IoT platform developed to support city-centric use cases, and an initial technology trial that shows the early benefits of using the platform. We conclude with plans for improvements to COGNIFOG based on the trials and with a broader set of observations on the future of the Cloud-Edge-IoT continuum in Smart City services and applications.

1. Introduction

Smart cities represent a complex convergence of urban planning, technology integration, and societal needs. As urban populations continue to grow, cities face unprecedented challenges in managing resources, infrastructure, and services efficiently [1]. The intricate nature of urban environments stems from their multifaceted organisational structures, overlapping jurisdictions, diverse infrastructure systems, and complex operational requirements [2].

From an organisational perspective, cities comprise numerous departments, agencies, and stakeholders, each with its own objectives and processes. Jurisdictionally, cities often involve multiple levels of government, from local to national, creating a web of regulations and policies. Infrastructure-wise, cities encompass interconnected systems for transportation, energy, water, waste management, and telecommunications, each with its own complexities. Operationally, cities must balance day-to-day functioning with long-term planning and crisis management.

This inherent complexity necessitates innovative technological solutions to streamline operations, improve decision-making, and enhance the quality of life for citizens. The cloud-edge-IoT (CEI) continuum has emerged as a promising framework to address these challenges. By leveraging distributed computing resources from cloud data centres to edge nodes and Internet of Things (IoT) devices, this continuum enables real-time data processing, adaptive resource allocation, and seamless integration of diverse urban systems. This approach holds the potential to transform urban management, making cities more responsive, efficient, and sustainable in the face of growing demands and limited resources.

In this paper, we first explore some of the main categories of Smart City applications, highlighting their complex ICT needs. We then introduce the CEI continuum and highlight how this emerging infrastructure can support Smart City applications. To better understand the use of CEI technologies in smart cities, we introduce the COGNIFOG project [3] and then introduce our three main use-case trials as well as a technology demonstrator that has been developed to provide initial performance and functionality feedback to the project. We highlight how this demonstrates the advanced features of the COGNIFOG platform and how these features are able to support Smart City’s needs. To validate our approach, we provide some initial performance data from the technology demonstrator and highlight its support for Smart City services and applications. Finally, we finish with observations of the platform, the next steps in its development, and some suggestions for future research.

2. Smart City Applications

Smart City applications span an enormous set of domains, ranging from internal applications used by city staff to manage local tax data, to applications helping citizens engage with civic infrastructure up to real-time applications controlling traffic flows within the city. Recent technology trends such as cloud technologies or IoT are driving new services and applications [4] and while it is impossible to list all these applications—and their ICT requirements—we can usefully categorise Smart City applications into the following broad categories:

• Smart Governance: Applications in the smart governance category aim to improve public services, citizen engagement, and administrative efficiency. It is a critical focus of all cities, with leading cities employing sophisticated ICT solutions. Some key application areas include, E-government services [5], Citizen engagement [6], and Predictive maintenance of public infrastructure [7].
• Smart Economy: Applications in the Smart economy sector focus on fostering innovation, entrepreneurship, and sustainable economic growth in cities. These range from Smart tourism applications that drive inward revenue to a city region [8], through digital marketplaces for local businesses aimed at growing the local economy [9], and even up-to-smart grids for energy management in cities that own or manage their own energy infrastructure [10].
• Smart Mobility: Smart mobility focuses on improving transportation systems and traffic management in urban areas with applications ranging from Intelligent traffic management systems [11] through bike and car-sharing services [12] to real-time public transportation information [13]. While many transportation services are aimed at improving travel times and reducing congestion, increasingly cities are looking to improve sustainability and target NetZero commitments [14].
• Smart Environment: Smart environment applications focus on sustainable resource management and environmental protection. Common services include Smart waste management systems [15], Smart lighting systems, and Air quality monitoring networks [16]. As with other categories, this category crosscuts many other initiatives and services, such as mobility and transportation.
• Smart Living: The Smart living category of applications covers a broad range with a general aim to improve quality of life, health, and safety in urban areas. Some core application areas are Smart home automation systems [17], Telemedicine and e-health services [18], and Public safety and emergency response systems [19]. However, applications such as Smart education platforms [20] and Cultural and entertainment services also fall into this category.
• A final notable category is Smart People which focuses on developing human capital, fostering creativity, and promoting social inclusion. This category includes examples such as Digital literacy programs, Social innovation platforms, and Inclusive technology solutions for diverse populations [21] often focused on engaging users and stakeholders in designing and deploying new services [22].

In conclusion, Smart City applications cover a wide range of technologies and services that touch on every level of the technology stack being developed and deployed in Smart Cities. To facilitate innovation across the entire spectrum of technology deployments requires that Smart City technology providers build platforms that also range across the entire spectrum, from the far-edge devices that are embedded in city infrastructure, through the near edge gateways, to the on-premise computing infrastructure deployed internally by cities, and up to the cloud services running on commercial server farms used by many cities. By leveraging the strengths of each layer—IoT for data collection, edge for low-latency processing, and cloud for complex analytics and storage—cities can create more efficient, responsive, and sustainable urban systems. To realise this potential, researchers are now exploring in more depth the continuum, and its precise mapping to the complex interplay of systems, policies, and infrastructure that make up Smart Cities.

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 processing at 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. Issues such as security and privacy, interoperability between heterogeneous devices and systems, resource management across the continuum, and the development of appropriate orchestration mechanisms need to be addressed. Additionally, the dynamic nature of urban environments, with their complex organisational structures and overlapping jurisdictions, adds another layer of complexity to the deployment and management of CEI solutions [33].

While it is clear that the emerging CEI continuum offers a number of potential benefits to Smart Cities it is equally clear that the potential, to be fully realised, needs intensive real-world study to validate both the overall approach, and the detailed aspects of its implementation.

4. Smart City Service and the CEI Continuum

The use of the CEI continuum in Smart Cities is in its infancy; however, the potential benefits for Smart City technology and service providers are significant. In this section, we highlight several key benefits of the CEI continuum when considering Smart City applications and discuss some of the ongoing research issues that the community is addressing to realise the full potential of these benefits.

4.1. Key Benefits of CEI Continuum in Support of Smart City Applications

We can identify several key benefits of the CEI continuum when considering its use in Smart Cities.

Improved Performance and Responsiveness: The CEI continuum enables real-time data processing and analysis closer to the data sources, reducing latency for time-sensitive applications. This is important for many of the categories of Smart City applications outlined in Section 2. Low latency is particularly beneficial for Smart City applications that exhibit classic real-time behaviours such as Smart Mobility where traffic management and autonomous vehicles require rapid response times or Smart Living, for example, elderly care where issues such as fall monitoring require real-time responsiveness [19,34].

Enhanced Scalability and Resource Optimization: The continuum allows for the dynamic allocation of computing resources across edge devices and cloud infrastructure, optimising performance and resource utilisation and facilitating scalability. Scalability is an issue that touches most of our Smart City application categories ranging from processing scalability which is crucial for managing the vast amounts of data generated by IoT devices in smart cities [35] to deployment scalability when we consider the massive number of far-edge devices embedded in the fabric of the city [33,34].

Equally, the issue of effective orchestration of resources and tasks across the CEI continuum is particularly relevant to all categories of Smart City applications, as efficient resource allocation is crucial for optimising performance across the continuum. For example, in smart mobility applications, proper orchestration can ensure that time-sensitive traffic management tasks are processed at the edge while long-term planning tasks utilise cloud resources. Similarly, when considering Smart Environment and Smart Economy applications that often involve large-scale sensor networks and data-intensive operations, the ability to scale operations and dynamically assign resources is critical to their effective use [36].

Improved Reliability and Operation Continuity: Edge computing capabilities ensure that critical Smart City services can continue functioning even during network interruptions or cloud connectivity issues [37]. This is essential for maintaining the efficiency and reliability of urban services and infrastructure [35] and critical for example in the Emergency Management context where critical city services need to remain running even in the face of natural disasters.

Energy Efficiency and Cost Reduction: Processing data closer to the source, often at the edge, can reduce energy consumption and bandwidth usage compared to sending all data to centralised cloud servers [38]. Obviously, this can lead to cost savings in data transmission and storage for Smart City applications [33].

This issue is particularly relevant to Smart Environment applications, but it affects all categories as energy efficiency is a key concern in Smart City initiatives. For instance, in smart lighting systems or environmental monitoring networks, energy-efficient operation of edge devices, especially those running on batteries, is crucial for long-term sustainability.

Enhanced Security and Privacy: The edge-cloud continuum allows for initial data filtering and anonymization, at the edge, before sensitive information reaches the cloud. Enhancing overall security and privacy protection for citizens [39].

Security and privacy are particularly relevant to Smart Governance and Smart Living applications, where personal and sensitive data is often processed. Implementing robust security measures across the continuum is crucial for maintaining citizen trust and compliance with data protection regulations.

Ultimately, the benefits listed above offer a Flexible and Adaptive Computing Model that can adapt to the specific needs of different Smart City applications. By leveraging these benefits, the edge-cloud continuum can significantly enhance the effectiveness and efficiency of Smart City initiatives, enabling more responsive, resilient, and intelligent urban systems.

We summarise these benefits in table form (see

Table 1. Summary of benefits for Smart City application areas. Each benefit is rated as High, Medium, or Low, indicating the level of impact, where High reflects strong and direct influence, Medium denotes partial or context-dependent improvements, and Low indicates limited or indirect effects.

CEI Benefit/Smart City Area	Smart Governance	Smart Economy	Smart Mobility	Smart Environment	Smart Living	Smart People
Improved Performance and Responsiveness	Medium	Medium	High	Medium	High	Medium
Enhanced Scalability and Resource Optimization	High	High	High	High	Medium	Low
Improved Reliability and Operation Continuity	High	High	High	Low	High	Medium
Energy Efficiency and Cost Reduction	Medium	Medium	Medium	High	Medium	Low
Enhanced Security and Privacy	High	High	Medium	Medium	High	High
Flexible and Adaptive Computing Model	High	High	High	Medium	High	Medium

) with more detail in the points below:

• Smart governance: The CEI continuum can enhance smart governance by providing secure, scalable infrastructure for e-services, with edge computing ensuring low-latency responses for critical services and cloud computing supporting data analytics for policymaking.
• Smart Economy: The CEI continuum can support smart economy applications by providing the infrastructure for secure, distributed systems, with edge computing supporting local economic activities and cloud computing enabling broader economic analysis and planning.
• Smart Mobility: The CEI continuum can support smart mobility applications in several ways. For traffic management systems, it enables real-time data processing at the edge for immediate traffic management decisions, while using cloud resources for long-term pattern analysis and planning. IoT devices like traffic sensors and connected vehicles provide the data input [40]. For personal mobility services such as bike and car share, edge processing enables context-aware suggestions/decisions and support for personal mobile devices.
• Smart Environment: The CEI continuum can support smart environment applications by, for example, enabling real-time data collection and processing from distributed IoT sensors, with edge computing providing immediate responses to environmental issues and cloud computing supporting long-term environmental modelling and policy planning.
• Smart Living: The CEI continuum can enhance smart living applications by providing a seamless integration of IoT devices in homes and public spaces. For example, in emergency management scenarios, edge computing ensures low-latency responses for critical services such as emergency response and in extreme situations (disaster) supports disconnected operations. Conversely, cloud computing can augment edge and IoT processing in situations such as data-intensive applications like personalised health recommendations.
• Smart People: The CEI continuum can support smart people initiatives by providing accessible, user-friendly interfaces at the edge for citizen engagement, while leveraging cloud resources for data-intensive applications like collaborative urban planning and social network analysis.

4.2. Open Issues and Real-World Validation

The distributed and scalable computing model of the CEI continuum has the potential to bring numerous advantages to Smart City applications, significantly improving the efficiency and resilience of urban services. However, several challenges from key research areas remain to be addressed to fully leverage its capabilities. In particular, there are still open issues around how to manage resources across the continuum, and specifically what degree of intelligence is needed to automatically reconfigure system and application resource allocations to meet overall QoS constraints as the real-world infrastructure changes [36,41].

Equally, issues such as security and privacy, data management, and energy efficiency are all areas where Smart City application developers are still experimenting to understand the best approach given the heterogeneous nature of the infrastructure and devices in a typical city deployment [24,42,43].

However, the area where there is still a need for significant work is that of Real-world validation. While there has been significant research on many of the issues underlying the successful development of the CEI, the validation of CEI continuum systems in Smart City scenarios remains a significant challenge. Despite notable advancements in frameworks and methodologies, most experimental evaluations are constrained to Proofs of Concept (PoC) or, at most, small-scale testbeds. As a result, both research and industry initiatives lack the scope required for real-world deployment and comprehensive validation [44]. To begin to address some of these concerns, we, and other researchers have begun to move towards real-world validation of initial research ideas in an attempt to move away from PoCs. The COGNIFOG project represents one such attempt to build a working platform to explore the CEI continuum in real-world, i.e., Smart City, situations.

5. The COGNIFOG Project

5.1. Overview and Goals

The COGNIFOG framework is a novel cognitive CEI solution based on open, flexible, adaptive, and modular principles. It aims to address the challenges of next-generation computing by leveraging intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles. This innovative framework is intended to enable efficient operation, adaptability, and scalability of applications that require autonomous data processing and robust computing capabilities along a CEI continuum formed by heterogeneous IT resources [3].

On the cloud side, COGNIFOG will manage and orchestrate the required resources to ensure the delivery of secure, reliable, and computationally intensive end-to-end services. At the edge, it will be able to dynamically allocate some computing tasks that are typically handled in the cloud, thus enhancing the performance of time-sensitive applications. Lastly, on the IoT side, it will provide interoperability features, enabling multiple and varied IoT devices to seamlessly connect to the continuum and interact with other CEI components. Overall, this makes cognitive fog a multidimensional concept, integrating modular, decoupled building blocks that can be interconnected and adapted to specific application needs.

By establishing a cohesive CEI continuum that provides a seamless bridge between the physical and digital worlds, COGNIFOG aims to achieve the following objectives:

• To enhance service delivery and reduce operational costs for next-generation computing applications in the CEI continuum through the dynamic provisioning of computing, storage, and networking resources.
• To reduce energy consumption by applying (AI-driven) energy-aware workload management techniques in the cloud and, when feasible, by processing data closer to its source of acquisition.
• To facilitate the rapid, systematic, and flexible development and deployment of applications by leveraging continuous integration and development (CI/CD) practices and containerized environments.
• To guarantee a “security-by-design” approach covering the entire CEI continuum, with a Trusted Execution Environment, minimal and secure operating system, cryptographic services, automated verification mechanisms, and detection of threats and vulnerabilities. Although beyond the scope of this paper, readers interested in our security approach and threat model can read further details in the report: The COGNIFOG threat model analysis [45].

5.2. Architecture

The COGNIFOG architecture, as shown in Figure 2, has been conceived to be modular by design and is composed of a set of distinct yet interconnected building blocks distributed across four primary layers:

• The modelling layer empowers users to conceptualize and define the main characteristics of the CEI continuum before its actual deployment. It includes designing the network topology, outlining hardware and software specifications, creating service level agreements (SLA) models, defining application data flow models, and planning infrastructure frameworks.
• The DevOps layer facilitates the automation of the software development and deployment process across the various stages of the software lifecycle, utilizing a comprehensive continuous integration/continuous delivery (CI/CD) framework.
• The runtime layer encompasses the actual environments in which applications function, including on-premises setups, cloud infrastructures, edge devices, and any locations within the COGNIFOG architecture. This layer integrates essential components such as servers, operating systems, containers, serverless environments, databases, and other critical elements required for hosting and executing applications.
• The governance layer focuses on the implementation of tools and practices for effective orchestration, monitoring, logging, auditing, and reporting of the COGNIFOG framework. It also ensures compliance with established processes, enforces policies, and prioritizes software security and data protection.

5.3. Deployment Model and RunTime Services

Referring to Figure 2, we can identify four classes of components: Infrastructure management components (red) that collectively work to define and manage resources across the continuum, security (yellow) that ensures the safety and security of the system; monitoring (green) that work together to model and then monitor resources and applications; and finally application (blue) components that support real-time data and analysis of IoT devices. Assuming proper deployment, interconnection, and availability of the corresponding CEI continuum resources, the COGNIFOG workflow typically begins with the modelling stage, which is carried out by the end-user operating the entire framework. After user authentication, the system requires the introduction of infrastructure resources, application descriptions, and SLA policies via the front-end dashboard. Finally, constraint modelling, via the PolyGraph [46] modelling tool, specifies the time and resource constraints of deployed applications and allows them to be monitored later in real time, ensuring that consistency and processing time constraints are met.

The deployment stage works closely with a CI/CD framework [47], which is responsible for automating and streamlining the software development associated with the applications to be deployed. Then, both the outputs from the modelling stage and the consolidated software are received by the multi-cluster orchestrator, which conducts the proper matchmaking among workloads and available resources, according to the existing constraints and predefined policies.

The runtime stage extends these capabilities over time, so that the multi-cluster orchestrator is able to perform new deployments and modify the existing ones in a dynamic and automatic way, thanks to the assistance of the AI-enabled smart allocator. This last element is fed not only with the set of requirements stated by the end-user, but also with real-time information gathered by the monitoring and telemetry agents deployed in all the managed resources of the underlying infrastructure. Although fully functional AI modules are not yet available in the COGNIFOG framework, several reinforcement learning (RL) techniques are currently under study in COGNIFOG, and in the sister project CODECO [48] recognizing RL’s effectiveness in solving resource allocation problems within the IoT–edge–cloud continuum. In the COGNIFOG context, RL agents would be trained using data collected from monitoring agents, leveraging insights derived from the nodes’ operational dynamics and energy utilization patterns. Once trained, the validated RL agents would be integrated into the smart allocator, which would then be able to provide RL-based placement plans to the orchestrator. When run iteratively, this process would refine the system’s configuration to achieve an optimal solution that balances multiple criteria effectively.

Safety and security issues are handled throughout the CEI continuum by means of Arca Trusted OS. The trusted and secure OS used in the project is provided by project partner CYSEC [49], a hardened Linux-based micro-distribution that protects against system intrusions and prevents data compromise within software containers. It features seamless system updating, an immutable file system, and a hardware root of trust. This minimalist OS not only limits the attack surface for cybercriminals, but also ensures that potential attackers do not alter the operating system images by using a secure boot with a Trusted Platform Module (TPM).

Lastly, the suitability of the COGNIFOG framework for typical IoT-based Smart City applications is ensured with the use of three more elements. First, the IoT edge gateway functions as an entry point for heterogeneous wireless sensors, thus easing the collection, pre-processing, and transmission of city data. Second, for the DataHub, we use an open-source implementation of the OSGi-based Eclipse SensiNact platform [50], which aggregates IoT data from multiple networks and protocols and exposes it to different applications in a simple and standardised way. And third, the Eye, the analytics and display component provided by Kentyou [51]. This provides (a) a dashboard to display meaningful Smart City data and track mission-specific indicators in real-time, and (b) an AI engine to allow the seamless addition of novel city-based intelligent services at any level of the CEI continuum.

5.4. Use Cases

The COGNIFOG project is developing three real-world trials that will be used to test the capabilities of the platform and its CEI functionality. These trials were specified in the 1st year of the project based on a comprehensive set of use cases that cover many of the categories identified in Section 2. Each trial has been designed to focus on a specific area but provide sufficient variety to test a number of key aspects of the CEI continuum.

5.4.1. Trial 1—Disaster Management in Urban Centers

Centennial floods, which occur, on average, once every hundred years, can cause significant disruption to urban infrastructure such as those found in most major cities. These extreme events are often used for planning purposes as they overwhelm transportation, communication systems, and emergency services, making roads and railways impassable and isolating communities. Damaged communication networks further hinder emergency responses, underscoring the need for more resilient infrastructure and effective flood management strategies. Of late, these types of ‘once-in-a-lifetime’ floods are becoming increasingly frequent and so the need to ensure adequate planning is increasing.

Our first real-world deployment is designed to test COGNIFOG in the city of Paris, focusing on planning for a centennial flood event. This trial is managed by the Thales group in collaboration with city partners. In flood emergencies, timely intervention by authorities is crucial, requiring effective synchronization between rescue teams. Current systems, however, face challenges due to the limitations of existing architecture, which is not designed to handle critical factors like network congestion, latency, and the automatic re-provisioning of applications during disruptions. This reliance on standard scheduling policies, such as CPU and RAM usage, leaves the system vulnerable during connectivity failures.

Edge computing without intelligent workload placement exacerbates these issues, leading to inefficient resource use, higher latency, increased power consumption, and network congestion. The lack of dynamic adaptability in workload distribution reduces scalability, system reliability, and real-time processing capabilities. In flood scenarios, this inefficiency impacts low-latency applications, making the system more costly, less effective, and prone to failure.

The requirements that a collaborative mission use case imposes on the COGNIFOG platform emphasize deployment automation across edge and far-edge environments to ensure consistency, scalability, and security. This enables organisations like rescue centres to swiftly deploy software and applications, responding more effectively to changing needs. Rapid infrastructure and device provisioning are essential during disasters, supporting real-time situational awareness. Fast cloud application deployments reduce downtime, enhance scalability, and improve cost efficiency. Time predictability and resiliency to hardware or connectivity failures ensure operational stability and continuity during emergencies. Additionally, energy consumption monitoring is important for reliability and sustainability, while single-touch orchestration allows seamless deployment and management of applications with minimal effort.

Considering these requirements, the primary challenge in this use case is managing data stream processing chains with a diverse array of devices and computing resources. These environments impose stringent constraints on both energy efficiency and processing time. Effective management is crucial because, in such missions, every minute counts. Ensuring that the system operates efficiently under these demanding conditions requires careful coordination and optimisation of resources. This involves maintaining high performance and reliability and minimizing energy consumption to meet the rigorous operational standards necessary for mission-critical applications.

In addition, in a rescue mission, many actors and pieces of equipment, such as vehicles and intervention forces on the ground, are mobile. This not only complicates the network but also imposes significant constraints on the dynamism of the application infrastructure.

Returning to our key benefits table (Section 4.1), this use case has been designed to focus on three key aspects of the CEI continuum, i.e., improved reliability and operational continuity, enhanced scalability, and improved performance and responsiveness. See

Table 2. Summary of benefits from adopting CEI continuum: Smart manufacturing. Each benefit is rated as High, Medium, or Low, indicating the level of impact, where High reflects strong and direct influence, Medium denotes partial or context-dependent improvements, and Low indicates limited or indirect effects.

CEI Benefit/Smart City Area	Disaster Management	e-Health	Smart Manufacturing
Improved Performance and Responsiveness	High	High	Medium
Enhanced Scalability and Resource Optimization	High	Medium	Medium
Improved Reliability and Operation Continuity	High	High	High
Energy Efficiency and Cost Reduction	Medium	High	Medium
Enhanced Security and Privacy	Low	High	Low
Flexible and Adaptive Computing Model	Medium	Low	High

below for a full summary of key benefits for this and the other two use cases.

5.4.2. Trial 2—E-Health and Telemedicine Services

Our second use case focuses on the provision of telemedicine services to construction workers at medium- to large-scale building sites. These construction projects frequently employ hundreds of personnel who work in difficult and safety-critical zones, which presents unique challenges in terms of safety and health monitoring. This trial is managed by Telematic Medical Applications (TNMA), a provider of telemedicine devices based in Greece.

The primary requirement is to ensure the safety of the personnel through continuous monitoring and to provide a first-response telemedicine solution in case of emergencies. In such environments, workers are exposed to various hazards, including accidents, injuries, and health issues exacerbated by the lack of immediate medical facilities. These challenges can be addressed with the implementation of a robust telemedicine solution. This system must be capable of real-time monitoring of workers’ vital signs, immediate detection of health anomalies, and quick initiation of medical consultations.

In the use-case trial planned within the COGNIFOG project, a variety of edge and IoT devices are used. These include portable telemedicine systems that can be deployed on-site and wearable health monitoring devices for personnel. These are coupled with a cloud-based platform for data analysis and communication with medical staff. The portable telemedicine systems, designed as rugged and self-contained units, can be equipped with various medical sensors to conduct accurate health assessments. These systems facilitate real-time video consultations with medical professionals, ensuring workers receive timely medical advice and intervention. Wearable devices such as smartwatches can monitor vital signs like heart rate, blood oxygen levels, and physical activity. They also provide safety features like fall detection and SOS alerts, essential in hazardous work environments. The collected data is transmitted via secure, private networks established on-site, ensuring reliable communication even in areas with limited mobile network coverage. By utilising these advanced telemedicine solutions, the customer can ensure a safer working environment and prompt medical response, significantly reducing the risk of severe health incidents and improving overall operational efficiency.

The COGNIFOG platform requirements for the Telemedicine use case emphasize autonomous deployment and management of edge and IoT systems to ensure consistency, scalability, and reliability. Rapid infrastructure setup is essential, enabling fast provisioning of IoT devices and telemedicine suitcases, ensuring that the cloud-to-edge-to-IoT system becomes operational quickly. Quick deployment of network services is critical for maintaining efficiency, while resilience to hardware and connectivity issues is necessary for continuous operation. Enhanced data security is achieved by processing sensitive information locally at the edge, with encrypted backups sent to the cloud daily. The system must support high device connectivity, handle significant IoT devices per edge server, and ensure scalability and reliability. Additionally, the platform should allow multi-cluster deployments, enabling replication of trial setups based on configuration changes. See Table 2 for a full summary of these benefits.

5.4.3. Trial 3—Smart Manufacturing

The third trial planned within COGNIFOG is focused on Smart Manufacturing in a modern factory setting. This trial is developed by the Laboratory for Manufacturing Systems & Automation, University of Patras, Greece. The manufacturing scenario involves the collaboration of a mobile robot equipped with a collaborative arm alongside two additional robots tasked with transportation. The mobile robot supports an operator in the assembly process, while the other two robots handle the transportation of materials to the workstation. Each robot operates with its own ROS (Robot Operating System) application, collaborating to complete the assembly on time. Task scheduling plays a vital role in ensuring the timely transportation of parts, and in case of failure of one transportation robot, the other must take over both tasks to maintain the workflow. While the assembly robot is a real mobile robot, the transportation robots are simulated.

The smart manufacturing scenario presents several challenges that could be addressed through the integration of COGNIFOG technologies. Given the scenario’s requirement for seamless interaction between multiple devices, efficient cooperation, and control of both hardware and software components is crucial. Additionally, dynamic deployment across all devices and applications, which rely on ROS channels for data exchange, is necessary. Security for data and networks is another key concern. Furthermore, the increased connection of devices to a single network introduces challenges related to latency and data storage capacity, which need to be addressed.

From a technical perspective, intelligent orchestration, dynamic deployment, and high-level coordination are challenges that can be addressed using COGNIFOG components. In simple manufacturing scenarios, each robot uses its own ROS infrastructure, typically utilizing edge resources to minimize delays, though cloud deployment may be necessary for more complex coordination tasks. Centralized coordination also introduces a requirement for innovative and dynamic deployment setups, which can be managed through Kubernetes (K8s) cluster deployments. Robots must first be implemented as ROS containers, deployed on K8s clusters, and then managed by COGNIFOG components. However, deploying multiple robots under the same infrastructure could pose challenges related to network bandwidth or storage capacity, requiring careful consideration.

In summary, the benefits of this use case (and the other two detailed above) are shown in Table 2.

5.5. Technology Demonstrator

The three trials above are scheduled for deployment and evaluation during 2025 with final results ready in late 2025 or early 2026. In preparation for the trials, an internal technology demonstrator has been designed to test all major technology components, evaluate their performance in different simulated conditions, and validate the overall COGNIFOG architecture. Unlike the use-case trials, the technology demonstrator, referred to as ‘Quick Start’, is not focused on testing via a real-world scenario, but rather on facilitating simple applications that help COGNIFOG users/administrators to understand, deploy, test, and use its internal modules/building blocks.

The COGNIFOG framework aims to simplify the deployment and orchestration of services across diverse infrastructures. To achieve this, it is essential to abstract as many deployment and configuration steps as possible, empowering framework users to focus on their core applications. The COGNIFOG Quick Start facilitates this by automating the deployment and configuration of key framework components. Specifically, it uses open-source orchestration technologies like Kubernetes (K8s), the industry-standard container orchestration platform, and its lightweight distribution, K3s, designed for resource-constrained environments. Additionally, some users may be interested in KubeEdge, which extends Kubernetes capabilities to the edge, enabling seamless management of edge devices. These technologies offer several advantages. K3s delivers strong performance even on low compute resources, while KubeEdge enables the management of edge devices and their interconnections without relying on the main Kubernetes control plane. However, these solutions prioritize simplifying deployment and integration in the execution environment, which may come at the expense of certain application performance metrics. The COGNIFOG framework addresses this by providing tools to design the system, verify timing behaviour and energy consumption during application execution, and redeploy as needed. The project’s added value lies in leveraging the well-known K3s technology and incorporating a custom scheduler to enhance application response time and optimize energy consumption.

By streamlining the setup of these foundational technologies alongside COGNIFOG’s building blocks, Quick Start allows users to concentrate on developing and integrating their own value-added services and applications.

To achieve such automation, Quick Start uses the following technologies:

• Vagrant: For standalone environments, Vagrant enables the rapid provisioning of a virtualized cloud-edge system on local machines. This environment encompasses all necessary components, including K8s, K3s, KubeEdge clusters, and COGNIFOG building blocks, facilitating application and workflow validation prior to production deployment. In Quick Start, Vagrant is used to create VMs with the same base system configuration every time.
• Ansible: Utilized in both standalone and distributed environments, Ansible automates infrastructure configuration, ensuring uniformity and reliability, and reducing manual intervention.
• Helm charts: Serving as a Kubernetes package manager, Helm charts define, install, and manage applications within clusters. These YAML-based configurations specify application services, configurations, and dependencies.
• Kubernetes manifests: These YAML or JSON files define Kubernetes resources, such as pods, services, and deployments, specifying configuration parameters for direct application to Kubernetes clusters via the kubectl command.

For more details on Quick Start and the overall COGNIFOG development environment, see [47].

The chosen application for the Quick Start instance reproduces a basic Smart Environment monitoring service from a Smart City. It involves environmental data acquisition and processing from publicly available weather station datasets, and object detection from open-source street camera video datasets. This provides a foundation using real data, with the flexibility to scale up in complexity by adding more simulated weather stations or simulated cameras.

The application is deployed using Quick Start and is composed of two different clusters—see Figure 3:

• The Management cluster is the central control plane responsible for managing other Kubernetes clusters. Its role is to check the managed clusters, schedule workload, and verify the state of the other clusters. In Quick Start, this cluster hosts COGNIFOG components from the modelling, DevOps, and governance layers.
• The Working cluster supports the policies, governance, and execution workload defined by the management cluster. This is the cluster where user applications are deployed. In this cluster, there are COGNIFOG components from runtime and governance layers.

For the Technology Demonstrator, the environment to run Quick Start is a Cloud provided 20-core CPU with 64 GB of RAM. Quick Start uses Vagrant to create eight virtual machines with specific roles:

• Manager (x1): This node is the control plane for the management cluster. At this point, it hosts the components receiving the monitoring data and the components to deploy data on working nodes.
• Master (x1): This node is the control plane of the working cluster. It hosts the Smart Allocator responsible for selecting how the applications are deployed and the native components of Kubernetes.
• Edge node (x2): These nodes are mainly present to host application code including Object detection and webpages to display results of computation. Additionally, they support a Prometheus monitor node to monitor CPU, RAM, and energy consumption. They are hosted in the working cluster.
• Heavy edge node (x1): This node is responsible for hosting components needed by the COGNIFOG framework in the working nodes: Polygraph monitoring (latency, period, and bandwidth application monitoring), an MQTT Broker and Prometheus monitor (CPU, RAM, and energy consumption monitoring). It is hosted in the working cluster.
• Relays (x3): In Quick Start, they represent the entry points of the sensors (weather station data and camera frames) in the COGNIFOG platform. In the use cases of the project, these relays correspond to the IoT Edge Gateway. The components hosted in these VMs are the MQTT Broker, an application responsible for adding localisation metadata in MQTT messages, and the Prometheus monitor. Prometheus is an open-source monitoring and alerting tool. It collects and stores data (like CPU usage, RAM usage, and energy consumption) from configured targets. They are hosted in the working cluster.

One of the key goals of Quick Start and the underlying COGNIFOG platform is to reduce the setup and installation time of the various physical devices needed to run CEI apps. In a traditional scenario, it would typically take 45–60 min to install OS, configure networking, and install and configure Kubernetes for each machine. However, by using Quick Start (which in turn uses Vagrant and Ansible) we can reduce the setup time including creation and configuration of the VMs with K3s and network configuration to less than 5 min. The drastic reduction in setup time, from 45 to 60 min to under 5 min, is achieved by leveraging Vagrant for automated VM provisioning and Ansible for declarative configuration management, eliminating manual, time-consuming steps like OS installation, network setup, and Kubernetes configuration on each individual machine.

Perhaps more importantly, by leveraging the COGNIFOG capabilities, we are able to scale our deployment by multiplying the number of working clusters as computing needs increase. As discussed in Section 5.2, multi-cluster management is achieved by using OCM (Open Cluster Management) and the Orchestrator Scheduler. OCM provides a single control plane to avoid the manual context switches in Kubernetes. It helps in the deployment of the applications by ensuring the placement of the workload in the most suitable cluster.

Initial deployment of the Quick Start architecture confirms successful node monitoring, with key metrics like CPU usage, RAM usage, power consumption, and application metrics collected periodically in the management cluster’s monitoring manager. In the base configuration, these metrics are gathered every minute, but it is configurable. These metrics allow for basic system health checks and redeployment decisions leveraging AI-driven solutions to automate reconfiguration based on these metrics.

In Quick Start, the applications are deployed using the dedicated Helm chart. During the base deployment of our initial test case, the environmental monitoring simulation, the application components are

• Localisation metadata (x3): Application receiving the sensor data. They are located in the relay nodes.
• Weather aggregators (x2): Applications to compute weather data and analyse the current weather.
• Weather map (x1): Web application to display the computed value of the current weather by the aggregators.
• Object detectors (x2): Application to detect the objects in the camera frames received.
• Object map (x1): Web application to display the detected objects by the object detectors.

The test case involves sensor data that originates from data sources external to the working cluster. This data is derived from recorded information from open data weather platforms and then re-introduced using emulators before being sent to an MQTT Broker in the COGNIFOG framework. Importantly, all other applications function precisely as they would during a live deployment, ensuring that the COGNIFOG stack is fully operational rather than simulated. COGNIFOG project deliverables D5.1 and D5.2 provide a better understanding of what live data will be used in trials [52]. This real data (although not delivered in real-time) from weather stations and cameras, upon entering the working cluster, is first processed by the localization metadata application. This application determines the geographic location associated with each data point and enriches the payload with this metadata.

Following localization, the data diverges into two distinct processing paths:

(a)

For weather station data, the aggregator receives the localized message and integrates all available information to determine the current weather conditions. This consolidated weather data, along with its location metadata, is then transmitted to a web application for map-based visualization.

(b)

For camera frame data, the object detection module analyses each frame, identifying objects such as cars, buses, motorcycles, and people. The detector outputs the identified object type and its associated confidence score. This information is subsequently sent to a web application, which selects the object with the highest confidence score for display on a map.

As illustrated in Figure 4, weather data is transmitted at a low frequency of every ten seconds, resulting in minimal bandwidth usage. While this data is non-critical, camera frames are sent at a higher frequency of one frame per second and require significantly more bandwidth. During application design, users specify timing and bandwidth requirements within the application description model. Following deployment, the monitoring system verifies the performance matches these specifications. In the Quick Start implementation, we ensure data periodicity is set at twice the expected period and this defines the threshold used to detect deviations. If an event exceeds this threshold, the information is captured by the Polygraph monitoring component. This component measures application metrics and exposes them via a Prometheus client which allows the monitoring manager to monitor application behaviour and trigger application redeployment if constraints are not met. It is deployed in the working cluster and acquires all messages from the MQTT Broker to extract all application information.

Exploring this scenario, we have run a simple experiment to measure the performance of our test application, identify when constraints are not met, and trigger runtime reconfiguration. The experiment uses real-time monitoring of MQTT topics associated with the object detectors that are processing real-time camera feeds. Timestamps are used to determine the periodicity of messages and message size used to compute bandwidth costs.

In Figure 5, the bar graph shows a snapshot of the measured period for the object detection of all camera frames. The graph highlights eleven frames with periods exceeding the defined 2s threshold (red line), shown as blue bars and labelled ‘period before (ms)’. This observation indicates potential performance bottlenecks. To address this, the options are to (a) migrate object detectors to a more powerful machine or (b) scale the number of object detectors horizontally.

Given that the Quick Start environment only instantiates homogeneous machine types (i.e., only CPUs and no GPUs), horizontal scaling was chosen. COGNIFOG is triggered to re-evaluate resources available and system-wide application constraints and chooses to deploy more object detectors within the working cluster. Since more pods are deployed to manage the camera images, the average response latency for identifying the objects is reduced. Once reconfiguration has taken place, ongoing monitoring shows that the timing constraint violations have been resolved for object messages—this is shown in the bar graph as orange bars labelled ‘period after (ms)’ redeployment.

The bar graph shows the percentage reduction in the message period for object detection in all camera frames after horizontal scaling has been applied. The graph illustrates the improvement ranges from 37% to over 50%. This measurement demonstrates that adding more pods effectively reduces the response latency of the system for all camera feeds. The role of COGNIFOG is to verify if the latencies meet application constraints defined by the user and if not, a rescheduling action is applied. In QuickStart, we can only scale up the number of pods (due to homogeneous VMs) but in the project’s use cases, the intent is to deploy the workload across the full CEI continuum.

The findings from the previous study were then validated across multiple runs, with each run corresponding to 10 min of data acquisition. The chart below (Figure 6) presents a comparison of average object detection periods (in milliseconds) across multiple cameras, measured before and after scaling more pods for object detection computation. Each box plot represents the distribution of detection periods for a specific camera. The ‘period before (ms)’ values correspond to measurements taken prior to the application of horizontal scaling, while the ‘period after (ms)’ values reflect performance following the new deployment. The results show a significant improvement across all cameras, with detection periods consistently decreasing after the enhancement. The outlier visible in the ‘period before (ms)’ section represents an unusually high object detection period (above 3 s). This event can be explained by network latencies and/or processing bottlenecks but the redeployment with more pods helped eliminate such extreme delays. Moreover, the variability in detection times is notably reduced, indicating not only faster but also more stable performance. This indicates the performance improvement, attributed to horizontal scaling.

In summary, our initial tests using the technology demonstrator have shown that two key requirements, automatic deployment and dynamic reconfiguration, have been achieved using the COGNIFOG platform allowing developers to build applications that exploit the CEI continuum. We are currently finalising the Quick Start application framework with the goal of fully testing all core COGNIFOG functionality and supporting the deployment of our three use cases. We plan to report on these at the end of the project in early 2026.

6. Discussion and Future Directions

As cities continue to grow and face increasingly complex challenges, the Cloud-Edge-IoT continuum presents a promising approach for developing more responsive, efficient, and sustainable urban systems. By leveraging the strengths of cloud computing, edge computing, and IoT technologies, this continuum has the potential to transform urban management and improve the quality of life for city dwellers. However, realising this potential will require continued research and innovation to address the technical, organisational, and societal challenges associated with implementing such complex, distributed systems in urban environments.

The COGNIFOG project has been addressing some of these open issues by developing a CEI continuum platform that allows us to explore real-world deployments of Smart City applications and services. In this paper, we have reported on our initial architecture and the use of a technology demonstrator to validate some of our initial design decisions. In particular, we have developed Quick Start, a framework for easy installation and use of COGNIFOG. We have demonstrated its use through a technology demonstrator that deploys a typical Smart City application across the CEI continuum, and we have shown how COGNIFOG can be used to monitor the performance of the application across the continuum, and dynamically reconfigure when application constraints are not met.

This paper and the initial experiments help validate our overall approach. By characterising Smart City applications, and identifying their requirements, we have been able to direct the development of the COGNIFOG architecture to meet those requirements. The experimental data reported in this paper both validates the architecture and helps us shape the next steps in the project.

However, while we believe that the work reported here is a valuable initial analysis of the role of the CEI continuum in supporting Smart City services, we recognize that COGNIFOG is in early development and will require continued improvement to meet the needs of cities. As a project, we plan to address several COGNIFOG-specific issues in the near future. These include:

• Multi-Cluster Orchestration: In the Quick Start implementation of COGNIFOG, orchestration is primarily carried out within clusters. While this is sufficient for many small-scale applications, multi-cluster orchestration is needed for our large-scale trials, especially when involving geographically distributed and heterogeneous clusters across on-premises, hybrid, and even multi-cloud environments. COGNIFOG has been architected with such support [3] and this will be integrated in preparation for our three real-world trials.
• Enhancing orchestration intelligence: As indicated in Section 5.3, integrating AI-driven mechanisms to optimize resource allocation and workload distribution across the IoT–edge–cloud continuum is under development. By better exploiting Reinforcement Learning algorithms we aim to develop better orchestration for our three trials. Of particular concern is Energy Management. The current COGNIFOG platform has basic support for energy optimisations using simple device monitoring. We plan to extend this with better support for consumption-driven placement decisions and reduced networking costs.
• Improved usability for developers: Our current system uses a front-end dashboard to allow developers to model their application deployments and express constraints. We aim to improve this with more flexibility and better PolyGraph integration to allow developers to better understand trade-offs available in the continuum.

In addition to these short-term, concrete steps to improve COGNIFOG, there are still several significant open research areas in the CEI continuum which we plan to address in the future using COGNIFOG. These include:

• Cognitive Self-Management and Intent-Based Systems: Container-based orchestration has emerged as the de facto standard to efficiently manage resources across the continuum. Nonetheless, the complexity of the resulting architectures and the evolving demands of applications complicate manual management and underscore the necessity for automation [36]. In response, smart container orchestration explores the effectiveness of various ML-assisted learning techniques for dynamic workload allocation, predictive scaling, and anomaly detection [41]. Future research should focus on developing self-managing systems that can autonomously adapt to changing conditions based on user intents. These systems should be capable of understanding high-level goals and translating them into actionable plans and will require exploring architectures that can self-adapt and self-organise in response to dynamic environments. These architectures should be able to reconfigure themselves to optimise performance and resource utilisation.
• Edge AI and Federated Learning: Integrating AI at the edge and employing federated learning techniques can enhance data privacy and reduce latency. Research should focus on developing efficient edge AI models and federated learning frameworks [53,54]. AI-driven data management in the CEI continuum is increasingly employed, with intelligent algorithms responsible for classifying, prioritizing, and storing data in real time [55]
• Green Computing in the Cognitive Cloud-Edge-IoT Continuum: Researching green computing solutions will help minimise the environmental impact of the continuum. This includes developing energy-efficient algorithms, hardware, and practices to promote sustainability. Optimizing energy consumption across the CEI continuum is critical for minimizing carbon footprints and reducing operational costs. At the cloud level, dynamic resource allocation techniques allow data centres to scale resources up or down based on real-time demand, thus avoiding over-provisioning [56]. On the edge side, the focus shifts to lightweight and efficient hardware, such as novel low-power devices that may prioritize energy efficiency while maintaining computational capability [43]. Lastly, energy-harvesting technologies, such as solar-powered IoT sensors and backscatter communication, offer the ability to extend device lifetime in remote or resource-constrained environments [57].