Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.

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.

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.

The infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt.

This paper proposes context modelling and reasoning to enable intelligent services in a ubiquitous campus. An ontology-based modelling includes upper level context modelling and domain-specific modelling for the campus area. Ontological and rule-based inferencing, which facilitate ubiquitous functionality for daily life, are implemented by utilizing the context model developed. A student assistant scenario is presented, demonstrating the usefulness of ontological context modelling and reasoning for highly distributed environments, such as a university campus.

Context is a multi-dimensional concept. It is hard to define context generally for computer science. Which information is considered as context, which is not? Why are the certain context elements relevant for a certain case, but irrelevant for another? How to explain this to computers? Can computers learn these issues as humans do? In our paper we present different viewpoints to the concept of context and to context modelling starting from requirements engineering and ending up to multi-disciplinary education. Based on context related literature research and discussions in our paper, we can summarize that a complete and comprehensive definition and model of context is difficult to achieve and may not even be appropriate at all. However we can conclude that there is a common understanding that context always relates to an entity, context is used to solve a problem, context depends on the domain of use, context depends on time and context is evolutionary.