This paper examines the introduction of the NOx-to-fuel mass ratio in the Euro 7 regulation, a parameter designed to evaluate the effectiveness of the vehicle emission control by assessing the mass of NOx emitted relative to fuel consumption. This approach has been selected for its potential to identify high-emitting vehicles, which may result from malfunctioning or manipulated emission control systems. This study reviews in detail the methodology proposed by the European Commission for this parameter utilizing data from an on-road Euro 6d-Temp diesel vehicle, anticipating its outcomes within the Euro 7 framework and highlighting some potential limitations.

The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native principles, where distributed computing nodes often have unfixed and changing networking configurations. Unlike traditional approaches, this architecture also focuses on managing services across massively distributed mobile nodes, as demonstrated in the automotive use case presented. Apart from working as MANO framework, the proposed solution efficiently handles service lifecycle management in large fleets of vehicles without relying on public or static IP addresses for connectivity. Its modular, microservices-based approach ensures adaptability to emerging trends like Edge Native, WebAssembly and RISC-V, positioning it as a forward-looking innovation for IoT ecosystems.

Task placement optimization in cloud-edge-fog environments is a challenging problem that requires balancing multiple objectives, such as minimizing latency and energy consumption, while adhering to resource constraints. This paper proposes a framework that integrates Deep Reinforcement Learning with Graph Neural Networks to address these challenges. Specifically, we explore the effectiveness of such architectures, including Graph Convolutional Networks and Message Parsing Neural Networks, within a DRL agent for task allocation. The framework is evaluated on synthetic task flow graphs, representing parallel workflows of varying complexities ($\mathbf{1 0}$ and $\mathbf{1 0 0}$ tasks), and benchmarked against traditional methods such as Genetic Algorithms and a Random Agent. Our results demonstrate that the RL Agent with GCN layers outperforms the MPNN-based RL Agent, GA, and Random Agent in small-scale scenarios while it performs equivalently with the MPNN-based RL Agent in largescale scenarios in which both surpass the heuristic approach and the Random agent.

The Real Driving Emission (RDE) test became a critical part of the process conducted by manufacturers to fulfill the approval procedure of every new vehicle model. This test measures the regulated emissions from a vehicle during a trip, which follows a specific set of operation requirements, aiming to assess the vehicle's emission levels in real-world conditions. Additionally, In-Service Conformity (ISC) tests, which consist in performing an RDE trip, were also introduced to demonstrate vehicles emissions compliance over their lifespan. Considering that modern vehicles embed exhaust emission sensors and connectivity capabilities, it is believed that there is an opportunity for manufacturers to leverage the data generated by these vehicles to forecast the outcomes of an ISC test. However, as this study presents through the analysis of an extensive database of more than 600 trips from a mild-hybrid diesel vehicle, none of the real-world trips might comply with all the driving requirements of the RDE standard. Faced with this outcome, this work proposes the application of a Genetic Algorithm (GA) optimization to construct virtual RDE trips from real-driving data. In particular, the proposed methodology leverages such algorithm to combine real driving fragments from various trips in order to align with the main RDE trip requirements. The methodology focuses on vehicle, engine, and exhaust after-treatment variables, utilizing signal optimization connections to create a realistic analysis of vehicle pollutants. The research suggests that a combination of vehicle speed, coolant temperature, exhaust temperature, and Selective Catalytic Reduction (SCR) load leads to a significant number of RDE-compliant results under simplified legislative conditions, from which emissions profiles could be assessed. The proposed methodology details the development of an Adaptive Genetic Algorithm (AGA) and the data pipeline to create specific RDE trips, offering the capability to customize the desired Driving Cycles (DC).

The current data landscape presents several challenges in data governance and integration. This paper presents the integration of the Data Fabric implemented in the Horizon Europe project aerOS, which integrates and unifies data available in the IoT-Edge-Cloud continuum, with the Data Space implemented in the RE4DY project in order to extend the capabilities of the Data Fabric by enabling the definition fine-grained data access policies and data monetization. This integration would promote collaboration with other participants in the Data Space based on the reuse of data assets from the Data Fabric by sharing them in a trusted environment and the integration of new data or services with the Data Fabric, thus contributing to the creation and integration of new data ecosystems.

The landscape of computing technologies is changing rapidly, straining existing software engineering practices and tools. The growing need to produce and maintain increasingly complex multi-architecture applications makes it crucial to effectively accelerate and automate software engineering processes. At the same time, artificial intelligence (AI) tools are expected to work hand-in-hand with human developers. Therefore, it becomes critical to model the software accurately, so that the AI and humans can share a common understanding of the problem. In this contribution, firstly, an in-depth overview of these interconnected challenges faced by modern software engineering is presented. Secondly, to tackle them, a novel architecture based on the emerging WebAssembly technology and the latest advancements in neuro-symbolic AI, autonomy, and knowledge graphs is proposed. The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins, which model the software throughout all stages of its lifecycle. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user. The concept takes a leap beyond the current software engineering approaches, addressing some of the most urgent issues in the field. Finally, the efforts towards realizing the proposed approach as well as future research directions are summarized.

The work reflects on the importance of the security layers in the context of ever-increasing distributed computing ecosystems. The IoT-Edge-Cloud computing continuum has emerged as a model for addressing the challenges of distributed computing, integrating data exchanges and processing across multiple tiers. The paper explores the cybersecurity challenges in distributed computing continuum cases, understanding the figures and mechanisms that govern the process. The authors link those reflections to the direct transfer into a research project, aerOS, that faces such challenges head on as a leading project in the IoT-Edge-Cloud continuum. By integrating cutting-edge technologies in data access, trust mechanisms and threat detection, aerOS provides a scalable and adaptive security framework.

This paper reflects on a concept that leverages diverse sensor configurations across a fleet of connected vehicles to enhance their emissions monitoring and diagnostics. In this vision, the vehicles of a same family are equipped with different sensor layouts and grades, and share data to support the monitoring of the entire fleet. Multiple applications within this framework are outlined, and a specific use case consisting in predicting the emissions during the light-off of the tailpipe NOx sensor with artificial neural networks is discussed, demonstrating the benefits of the proposed architecture. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

The realization of the Cloud-Edge-IoT continuum is a vibrant field of research that promises to integrate heterogeneous and dispersed computing elements as an integrated piece to achieve an efficient distribution of containerized workloads. Among the unresolved challenges, some stand out, such as the homogeneous and ubiquitous sharing of the current state of the resources, or the dynamic handling of underlying network complexities. This paper tackles both problems by proposing a mix of theoretical and technological implementation approach departing from the investigations undertaken in the Horizon Europe project aerOS. A lightweight solution is formulated based on the federation of distributed domains drawing on the establishment of peer-to-peer connections between computing nodes.

This study explores on-board data reduction techniques for efficient over-the-air transmission of in-cylinder pressure data in connected vehicles. Besides its significance for combustion diagnostics, the in-cylinder pressure signal conveys valuable information about the engine operation that could enhance fleet monitoring and management. However, the high sampling frequency of this signal leads to substantial data volume. In this work, singular value decomposition (SVD) and QR factorization with column pivoting were explored for data reduction in a vehicle equipped with an in-cylinder pressure sensor, and over-the-air transmission of the reduced data to a cloud server was implemented. This work establishes the feasibility of these techniques for in-vehicle applications and the results indicate accurate signal reconstruction with a significant 95% and 97% reduction in data size for SVD and SVD + QR, respectively. The findings suggest that both methods are appropriate for in-cylinder pressure data transmission, although the SVD + QR technique might be preferred in situations where on-board diagnostics solutions are required.

This work provides a structured approach to the usage of deception mechanisms in Internet of Things deployments within 6G era. Honeypots, honeynets and moving target defence elements are increasingly frequent, and their potential to overcome specific cyber-security issues in IoT is paramount. Authors focus on actual implementations, emphasising those that employ open source technologies, describing actionable articles and tools, which culminates in analysing the upcoming AIAS platform. Arguably, the usage of such mechanisms will lead to refined implementations in the upcoming future.

Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, which is suitable for deployment in diverse environments—including edge devices. The proposed solution has a modular design and is compatible with a wide range of user-defined machine learning pipelines. To improve energy efficiency and scalability, a high-performance communication protocol for inference is propounded, along with a scale-out mechanism based on a load balancer. The inference service plugs into the ASSIST-IoT reference architecture, thus taking advantage of its other components. The solution was evaluated in two scenarios closely emulating real-life use cases, with demanding workloads and requirements constituting several different deployment scenarios. The results from the evaluation show that the proposed software meets the high throughput and low latency of inference requirements of the use cases while effectively adapting to the available hardware. The code and documentation, in addition to the data used in the evaluation, were open-sourced to foster adoption of the solution.

Digital Twins are software technologies that enable the modelling of real-world phenomena in digitised environments, representing and monitoring the reality of various processes, including IoT deployments. Since 2017, the use of Digital Twins has been increasing. However, in the road transport and logistics realm, the adoption rate remains low, primarily due to the costs of processing and validating data in centralised scenarios, among other factors. On the other hand, Blockchain technologies were created to provide immutable and decentralised data storage in diverse scenarios, adding a layer of isolation and reliability in heterogeneous solutions. This paper presents a case study proposing a Digital Twin based on open-source Blockchain technologies, such as FIWARE Canis Major. The primary goal is to design and implement a robust and efficient open-source architecture that allows for the control and optimisation of vehicle fleet allocations in logistics/transport companies within supply chain management. This case study aims to showcase the practical application of the proposed solution in a real-world context, providing insights into its eco-friendly and low-cost attributes while opening the door to a large number of additional applications.

In recent years, ransomware has established itself as one of the most persistent and devastating threats in cybersecu-rity. Traditionally, these attacks have focused on data at rest, encrypting files and demanding a ransom for their recovery. However, an alarming trend has emerged: ransomware for attacks to data in motion. This data, which includes information transmitted over networks, is critical to the day-to-day operations of organisations and often receives less protection than stored data. This paper aims to explore and characterise these new ransomware threats for attacks to data in motion. It also aims to test the feasibility of these attacks through the creation and implementation of a laboratory environment, as well as to analyse the potential consequences (based on likelihood and impact assessments) of such attacks if they were to be carried out, providing a comprehensive view of the potential emerging risks and warning of the need to create tools for the prevention, detection and mitigation of these attacks.

The Euro 7 standard is expected to intensify its focus on real-driving emissions compliance thanks to the on-board monitoring (OBM) system. OBM is designed to bridge the gap between emission limits and real-world vehicle emissions by continuously monitoring their levels using on-board sensors and models. Essentially, this system aims to verify vehicles compliance, prompt timely repairs otherwise, and provide authorities with emissions data for fleet inspection. Leveraging a large real-driving emissions database from an in-service Euro 6d-Temp vehicle, this paper reviews the current OBM proposal and discusses its possible outcomes and challenges using available sensor technology.

The work reflects about the importance of trust in data exchanges in the context of ever-increasing distributed computing ecosystems. It proposes the utilisation of an open-source technology that implements a direct acyclic graph incorporating peer nodes to validate messages in a decentralised network. The tool, IOTA, promises to solve the hindrances of blockchain solutions in highly heterogeneous, IoT-assimilable scenarios, adopting a more lightweight approach, removing the need of mining. The article explores the functioning of IOTA in distributed computing continuum cases, understanding the figures and mechanisms that govern the process. The authors link those reflections to the direct transfer into a research project, aerOS, that uses such a tool as intrinsic part of an IoT-Edge-Cloud continuum framework, enabling the immutability and non-repudiation of key messages in such environments. Also, the authors conclude analysing which next steps might follow to evolve from a not-fully decentralised implementation with the next releases of the tool, and the adaptations for the studied application.

Data-driven industrial value networks increasingly rely on creating and maintaining data value ecosystems, complete with trusted sharing and effective distributed data processing. However, challenges arise due to diverse use cases, large data volumes, data value and quality maintenance, and data source heterogeneity. To enhance data sharing, initiatives like International Data Spaces (IDS) propose enablers for trusted data sharing, allowing data owners to determine sovereignty basis. This chapter addresses the main challenges related to the evolution of sovereign Data Spaces in three critical dimensions: (1) improving their data reuse and exchangeability; (2) defining common models to increase data interoperability: and (3) ensure high data quality. For these reasons, this approach goes beyond the traditional Data Space technologies to propose "Data as a Product" (DaaP) as the core enabling concept to facilitate the implementation of digital continuity across Data Spaces, AI/ML/Data pipelines, Digital Twins workflows and digital threads. This chapter will provide an overview of the state of the art on strategies for Digital 4.0 Continuum. Finally, the Horizon Europe RE4DY project as a DaaP use case will be introduced.

In the last couple of decades, enterprises have relished and leveraged the capacity, performance, scalability, and quality of cloud computing services. However, a few years ago, the edge computing concept enabled data processing and application execution near compute and data resources, aiming to reduce latency and promote higher security and sovereignty regarding data transfers. The simultaneous use of these two service models by enterprises has lately resulted in the concept of edge-to-cloud continuum, which combines edge and cloud technologies and promotes standards and algorithms for resource orchestration, data management, and the deployment of solutions across the connected edge and cloud resources. Taking a step further in this direction, this paper introduces a framework to realise the Cognitive Computing Continuum (CCC). The framework leverages AI techniques to address the needs for optimal (edge and cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. The proposed ENACT framework enables the automated management of distributed (edge and cloud) resources and the development of hyper-distributed applications that can take advantage of distributed deployment and execution opportunities to optimize their behaviors in terms of execution time, resource utilisation and energy efficiency.

Indoor navigation, an innovative service built on indoor localisation, is a game-changer for travellers. This paper introduces a unique multimodal, dynamic indoor navigation service for indoor spaces. The novelty of this service lies in its seamless integration with outdoor routers, paving the way for a comprehensive door-to-door trip planner. The service's indoor multimodality is a key focus, considering accessibility options profiling and incorporating limited vehicular paths (e.g., internal buses in airports). The service's indoor dynamism is another standout feature involving real-time monitoring of events within the navigation path. The integration with outdoor routers is a significant achievement, primarily through the establishment of common interconnection points (shared points where indoor and outdoor navigation systems can exchange data) and a common data format structure (a standardised way of representing and exchanging navigation data). The proposed navigation service was put to the test in three real deployments at Berlin Tegel (TXL), Berlin Schönefeld (SXF), and Palma International (PMI) airports. Users travelling between these cities experienced the system's rapid detection of mechanical problems (e.g., travellators or elevators out of order) and incidents (e.g., temporarily non-navigable areas). The service's integration with other travel assistants and services, such as evaluating waiting times at check-in counters and security checkpoints, provided more accurate estimations of indoor navigation travel time and helped avoid agglomeration. These successful real-world validations underscore the service's effectiveness and reliability. The findings indicate that this innovative service significantly improves the travel experience by enhancing the planning and scheduling of movements from origin to destination. The validation showed an increase in travel efficiency, reduced wait times, and better accessibility options for travellers, underscoring the practical benefits of the proposed door-to-door navigation system. Received: 22 May 2024 | Revised: 11 July 2024 | Accepted: 18 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Benjamin Molina: Conceptualization, Methodology, Software, Validation, Writing - original draft, Writing - review & editing, Visualization. Carlos E. Palau: Conceptualization, Investigation, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Jaime Calvo-Gallego: Conceptualization, Investigation, Writing - original draft, Writing - review & editing.

There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-the-art AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. While majority of research concerning Explainable AI remains focused on weight modelling and timid gray-box approaches, the state-of-the-art has not explored much the deployment of semi-physical architectures combining fuzzy rule-based systems with more opaque models to improve explainability. This contribution aims to explore and make the case for a middle ground of mixed AI architectures that combine the performance of black-box AI models with a more explainable overall architecture, enabling operators to use them, while still retaining the core aspects of explainability, when compared to full black-box AI systems. This work contextualizes a potential application of such approach to the problem of Service Level Agreement compliance, in a case of microservice allocation decision over cloud (and cloud-like) infrastructures.