Artificial intelligence (AI) is increasingly transforming the healthcare sector by enabling advanced automated decision-making. However, implementing Machine Learning (ML) systems in medical imaging applications presents significant challenges, such as ensuring consistent results across different environments, maintaining comprehensive records of experimental processes, and establishing reliable methods for model comparison and evaluation. These issues become more evident as medical AI shifts towards a hybrid Cloud-Edge Continuum (CEC), adding complexity to workload orchestration and performance monitoring across diverse infrastructures. To address these challenges, this work introduces a modular Edge Micro Data Centre (EMDC) architecture that supports a scalable experiment tracking framework designed for AI model deployments in healthcare. The approach extends MLflow with a domain-specific module for medical image segmentation, incorporating several clinically relevant performance metrics. The EMDC architecture enables distributed processing and performance monitoring within a hybrid CEC environment. By aligning MLOps principles with CEC infrastructure, the proposed system facilitates reliable and scalable AI integration into the healthcare sector.

The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated by smart cities and smart buildings remains unused. A notable problem is the shift of statistical properties in real-world streaming over time caused by unexpected factors, referred to as concept drift, which results in less efficient predictive models. To address this problem, the latest research leverages the cloud–edge continuum paradigm for the deployment of AI and general smart city applications while utilising the available resources optimally. In this article, we propose a computing architecture for different smart city applications in edge micro data centre (EMDC) settings over a hybrid cloud–edge continuum to support the deployment of AI workloads. We implement a feedback-driven automated concept drift detection and adaptation methodology, combining base learner long short-term memory (LSTM) with Page–Hinkley test (PHT), adaptive windowing (ADWIN) and the Kolmogorov–Smirnov windowing (KSWIN). Real-world data streams are utilised to forecast from various environmental sensors installed at the University of Oulu Smart Campus. The feedback-based concept drift detection and adaption process is first evaluated using synthetic datasets with known concept drift points and then employed in the real-world data. Subsequently, the implementation is evaluated using the state-of-the-art MAE, RMSE, and MAPE methods. The results showed a reduction in MAPE from 8.5% to 3.88% when concept drift detection was applied. Additionally, the challenges faced and the effectiveness of the suggested solutions are explored.

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

The rapid integration of Internet of Things (loT) systems in public infrastructure like smart campuses has ex-panded the attack surface through edge devices, amplifying the risk of insider attacks. Traditional defense tools use centralized systems, typically located off-campus, for log monitoring to facilitate intrusion detection and security protocol enforcement in smart infrastructures. The limited real-time data access and observability provided to on-site staff in smart buildings under this traditional approach presents an advantage to intruders with physical access to exploit vulnerabilities in the loT sensing and perception layers. In this study, we explore the integration of Augmented Reality (AR) technology to facilitate real-time monitoring of various sensors and loT devices to provide an application for actionable insights for security and maintenance in a smart campus environment. The proposed application aims to enable key non -expert personnel, such as security guards and maintenance staff, to actively participate in cybersecurity defense by providing immediate data access and connectivity between dispersed endpoints throughout the campus. The paper further investigates the potential benefits and challenges of using AR interfaces to visualize complex sensor networks. In addition, we discuss the implications of our findings on the future design and management of smart campuses, emphasising improving the efficiency and effectiveness of operations and maintenance activities.