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.

This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. The work emphasizes Continuous Integration/Continuous Deployment (CI/CD) practices, development, and versatile integration infrastructures. The described methodology ensures efficient, reliable, and seamless integration of the framework, offering valuable insights into integration design, data flow, and the incorporation of cutting-edge technologies. Through three real-world trials in smart cities, e-health, and smart manufacturing and the development of a comprehensive QuickStart Guide for deployment, this work highlights the efficiency and adaptability of the COGNIFOG platform, presenting a robust solution for addressing the complexities of next-generation computing environments.

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments.

Semiconductor industry has long been active in standardization of interfaces to deal with interoperability. Even though these standards allow interoperability between different hosts and equipment within the semiconductor industry, it remains hard to make these interfaces interoperable with other domains of manufacturing. One of the main pillars of Industry 4.0 is the interoperability of different equipment and devices across multiple domains and between the operational and information technologies. Though OPC UA is becoming the de facto architecture for Industry 4.0, semiconductor equipment manufacturers need to comply with the existing SEMI standards to ensure the sales of their equipment. In this context, the primary goal of this work is to develop a comprehensive metamodel of the SEMI communication interfaces. As a secondary goal, we want to use this model to carry out a preliminary experimentation on using OPC UA as a communication architecture in semiconductor industry, maintaining maximum compliance to the established SEMI standards.