Funding information: This work was partially supported by the project project SERICS (PE00000014) under the NRRP MUR program funded by the NextGenerationEU. Publisher Copyright: Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Search and rescue operations are critical and involve logistical and human infrastructures, requiring high technology trustworthiness. A multi-layer secure architecture on top of a drone mesh is presented to address these challenges. It is leveraged by security features such as blockchain and CIA-compliant communication paradigms.

Context: Systems Of Systems (SoSs) are becoming a widespread emerging architecture, and they are used in several daily life contexts. Therefore, when a new device is integrated into an existing SoS, facilities able to efficaciously assess and prevent anomalous and dangerous situations are necessary. Objective: The aim is to define a reference environment conceived for monitoring and assessing the behavior of SoS when a new device is added. Method: In this paper, we present MENTORS, a monitoring environment for SoS. MENTORS is based on semantic web technologies to formally represent SoS and Monitoring knowledge through a core ontology, called MONTOLOGY. Results and Conclusion: We defined the conceptual model of MENTORS, which is composed of two phases: Off-line and On-line, supported by a reference architecture that allows its (semi-)automation. Validation of the proposal with real use-cases is part of future activities.

Quality of Experience (QoE) refers to the end users level of satisfaction with a real-time service, in particular in relation to its audio and video quality. Advances in WebRTC technology have favored the spread of multimedia services through use of any browser. Provision of adequate QoE in such services is of paramount importance. The assessment of QoE is costly and can be done only late in the service lifecycle. In this work we propose a simple approach for QoE-driven non-functional testing of WebRTC services that relies on the ElasTest open-source platform for end-to-end testing of large complex systems. We describe the ElasTest platform, the proposed approach and an experimental study. In this study, we compared qualitatively and quantitatively the effort required in the ElasTest supported scenario with respect to a "traditional" solution, showing great savings in terms of effort and time.

While great emphasis is given in the current literature about the potential of leveraging the cloud for testing purposes, the authors have scarce factual evidence from real‐world industrial contexts about the motivations, drawbacks and benefits related to the adoption of automated cloud testing technology. In this study, the authors present an empirical study undertaken within the ongoing European Project ElasTest, which has developed an open source platform for end‐to‐end testing of large distributed systems. This study aims at validating the ElasTest solution, and consists of the assessment of four demonstrators belonging to different application domains, namely e‐commerce, 5G networking, WebRTC and Internet of Things. For each demonstrator, they collected differing requirements, and achieved varying results, both positive and negative, showing that cloud testing needs careful assessment before adoption.

The research and industrial environments are struggling to identify practical approaches to highlight the (new) duties of controllers of personal data and foster the transition of IT-based systems, services, and tools to comply with the GDPR. In this paper, we present a solution for enhancing the modelling of business processes with facilities to help evaluate the compliance with the GDPR. The proposal is based on a model describing the constituents of the data protection domain: A structured form of the legal text, an ontology of data protection concepts, and a machine-readable translation of the GDPR provisions. An example of application is also provided.

The introduction of new generation ICT systems into vehicles makes them highly connected with the external World. As drawback, vehicle becomes potentially vulnerable to security attacks. Here, we consider a scenario in which Vehicular Networks and a Urban Network work together to realize a defence mechanism based on Reputation Systems. In this way, we are able to identify and isolate possible malicious vehicles acting that could send messages with the aim of reducing the availability of the network. We propose Context Aware Reputation Systems, CARS, able to identify insider attackers and isolate them taking into account contextual conditions derived from sensors spread along the entire urban network. Then, we experimentally evaluate CARS on a real data-set of mobility traces of taxis in Rome to compare the proposed systems with existing ones that do not consider contextual conditions. The preliminary results obtained are promising and show the feasibility and potentiality of CARS.

Model-based simulation and monitoring are becoming part of advanced learning environments. In this paper, we propose a model-based simulation and monitoring framework for management of learning assessment and we describe its architecture and main functionalities. The proposed framework allows user-friendly learning simulation with a strong support for collaboration and social interactions. Moreover, it monitors the learners' behavior during simulation execution and it is able to compute the learning scores useful for the learner knowledge assessment. The preliminary experimental feedback of the evaluation of the simulation and monitoring framework inside a real environment is also reported.

In model-driven learning, simulation of Business Process is a key step for improving the learners's skills and enhancing teaching performance. In this paper, we provide the architectural design and the main functionalities of a model-based learning simulation framework. The main objectives of the proposed framework are: i) enable user-friendly learning simulation with a strong support for collaboration and social interactions; ii) provide a process-driven learning environment that allows emulation of the learner behavior when no learners are available to be involved on the simulation; iii) include event-based monitoring aiming at providing feedbacks for the learner assessment.

Model-based analysis is a well-established method to assess the dependability of a system before deployment. It is well known that, in highly dynamic contexts, the accuracy of the analysis results can be limited because unpredictable phenomena may affect the system during its operation. In such contexts, the analysis typically needs to be refined with data obtained from real system executions. In this paper we tackle the issue of refining model-based dependability analysis in automated systems through monitoring. Specifically, we report on our preliminary results on the development of a system that exploits the synergic use of an automated approach for model-based dependability analysis and a flexible monitoring architecture.