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In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

This article presents a comprehensive study of human physiology to determine the impact of body mass index (BMI) on human gait. The approach followed in this study consists of a mathematical model based on the centre of mass of the human body, the inertia of a person in motion and the human gait speed. Moreover, the study includes the representation of a building using graph theory and emulates the presence of a person inside the building when an emergency takes place. The optimal evacuation route is obtained using the breadth-first search (BFS) algorithm, and the evacuation time prediction is calculated using a Gaussian process model. Then, the risk of the building is quantified by using a non-sequential Monte Carlo simulation. The results open up a new horizon for developing a more realistic model for the assessment of civil safety.

The European electronic IDentification, Authentication and trust Services (eIDAS) regulation makes available a solution to ensure the cross-border mutual recognition of electronic IDentification (eID) mechanisms among Member States. However, the basic set of attributes currently provided by each country only contains citizens’ personal and legal attributes, preventing e-services to take full advantage of citizens’ domain-specific information, such as academic or medical data. In this article, we propose an extension of the eIDAS specification to support academic attributes as part of citizens’ profiles. In addition, we present an architecture to enable the connection of eIDAS nodes to national attribute providers to enrich citizens’ profiles with additional academic attributes. We have deployed the eIDAS extension in the specific case of the Spanish eIDAS infrastructure, and we have connected it to an attribute provider of the Technical University of Madrid (UPM). We have also improved a set of institutional services of that university by enabling the connection to eIDAS and enhancing the features offered to students based on their academic profiles retrieved from the eIDAS extended infrastructure. Finally, we have evaluated the resulting services thanks to real students from two different countries, concluding that the widespread adoption of the proposed solution in the academic services of European universities will greatly improve their quality and usability.

In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to create new applications and services. Protecting data goes beyond restricting who can access what resource (covered by identity and Access Control): it becomes necessary to control how data are treated once accessed, which is known as data Usage Control. Data Usage Control provides a common and trustful security framework to guarantee the compliance with data governance rules and responsible use of organizations’ data by third-party entities, easing and ensuring secure data sharing in ecosystems such as Smart Cities and Industry 4.0. In this article, we present an implementation of a previously published architecture for enabling access and Usage Control in data-sharing ecosystems among multiple organizations using the FIWARE European open source platform. Additionally, we validate this implementation through a real use case in the food industry. We conclude that the proposed model, implemented using FIWARE components, provides a flexible and powerful architecture to manage Usage Control in data-sharing ecosystems.

Although many courses in computer science and software engineering require students to work on practical assignments, these are usually toy projects that do not come close to real professional developments. As such, recent graduates often fail to meet industry expectations when they first enter the workforce. In view of the gap between graduates’ skills and industry expectations, several institutions have resorted to integrating open-source software development as part of their programs. In this pilot study, we report on the results of the contributions of eleven students to the FIWARE open-source project as part of their final year project. Our findings suggest that both teachers and students have a positive perception towards contributing to the FIWARE open-source initiative and that students increased their knowledge of technologies valued by the industry. We also found that this kind of project requires an additional initial effort for the students as well as for the instructor to monitor their progress. Consequently, it is important that the instructors have previous experience in FIWARE, as many of the students need help during the process.

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization Protocol (GNAP) and integrates OpenID Connect for Verifiable Presentations (OIDC4VP) along with support for Linked Verifiable Presentations (LVP), providing a robust foundation for secure and privacy-preserving interactions. The proposed solution adheres to the principles of Self-Sovereign Identity (SSI) to facilitate decentralized, user-centric identity management while maintaining flexibility through protocol negotiation. Two alternative interaction flows are introduced: a \"Wallet-Driven Interaction\" utilizing OIDC4VP, and a \"LVP Authorization\" model for fully automated machine-to-machine communication. These flows address critical challenges encountered in Data Spaces, including privacy, interoperability, and regulatory compliance while simultaneously ensuring scalability and minimizing trust assumptions. The paper provides a detailed technical design, outlining the implementation considerations, and demonstrating how the proposed flows guarantee verifiable, secure, and efficient interactions between participants. This work contributes towards the establishment of a more trustworthy and sovereign digital infrastructure, in alignment with emerging European data governance initiatives.

We study the benefits and challenges of using Linked Open Data in smart city applications and propose a set of open source, highly scalable tools within the case of a public-rental bicycle system, which can act as a reference guide for other smart city applications.

The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation's and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.

The collaboration of the real world and the virtual world, known as Digital Twin, has become a trend with numerous successful use cases. However, there are challenges mentioned in the literature that must be addressed. One of the most important issues is the difficulty of collaboration of Digital Twins due to the lack of standardization in their implementation. This article continues a previous work that proposed a generic architecture based on the FIWARE components to build Digital Twins in any field. Our work proposes the use of Linked Open Data as a mechanism to facilitate the communication of Digital Twins. We validate our proposal with a use case of an urban Digital Twin that collaborates with a parking Digital Twin. We conclude that Linked Open Data in combination with the FIWARE ecosystem is a real reference option to deploy Digital Twins and to enable the collaboration between Digital Twins.

Data has become a crucial resource in the digital economy, fostering initiatives for secure and sovereign data sharing frameworks such as Data Spaces. However, these distributed environments require fine-grained access control mechanisms that balance openness with sovereignty and security. This paper proposes an extension of the Open Digital Rights Language (ODRL) standard, the ODRL Data Spaces (ODS) profile, aimed at supporting authorization and complementing existing authentication mechanisms throughout the data lifecycle. Additionally, a policy execution engine is introduced to translate ODRL policies into executable formats, enabling effective enforcement. The approach is validated through a use case involving OpenFGA, demonstrating its applicability to relationship-based access control scenarios.