With growing concerns about computing’s environmental impact, sustainable IT practices must engage all stakeholders—including end-users. This study investigates how user behavior influences software energy consumption through a mixed-methods approach: a campus survey (N=280) revealed that while 91.5% understand device energy use, only 55.3% recognize software’s role, and just 46.8% modify habits despite 79.3% acknowledging individual responsibility. Follow-up interviews highlighted demographic divides, with older (25-64) and advanced-degree users showing greater awareness. A week-long field experiment at an IT company demonstrates that real-time feedback—especially disaggregated data showing top energy-consuming apps—shifted perceptions, reducing hardware’s perceived influence by 29%. Though users adopted energy-saving actions (e.g., closing idle apps, switching to lightweight software), workplace priorities often limited sustained changes. Our findings prove user-centric interventions can raise awareness, but highlight the need for context-sensitive designs that balance energy efficiency with workflow demands. This work establishes actionable pathways to reduce ICT’s carbon footprint through behavior-aware software ecosystems.

The digital transformation of our society has deeply impacted the way we interact, communicate, and learn. This new digital era raises exceptional challenges for teaching and learning, particularly in adapting to individual learning paces, providing adequate progress traceability, and ensuring inclusive solutions for diverse learners. This paper introduces an Autonomic Cyber-Physical System for Education (A-CPS-E) that addresses these challenges through a framework combining teaching and learning design patterns with autonomous system capabilities. The A-CPS-E implements the autonomic computing paradigm to adapt learning experiences across four instructional interaction modes and generates Augmented Interactive Learning Objects (AILO) using AI support to enhance the teaching and learning process. We deployed the system within the Erasmus+ Connect Unita project, engaging 484 learners and 100 teachers across 12 universities in Europe. Resulting in the creation of 14 international training programs, including 65 learning paths and 400 learning objects, with $91.3 \%$ remote engagement. Preliminary results have allowed us to evaluate the benefits of the A-CPS-E in providing adaptive, traceable, and inclusive educational experiences that transcend traditional boundaries in higher education.

Process mining in the construction industry relies heavily on high-quality event logs, yet existing approaches primarily use structured data sources, leaving unstructured textual reports largely unexploited. This paper proposes a novel two-stage methodology to extract and preprocess event logs from unstructured, natural language on-site construction reports. Initially, domain-customized natural language processing techniques combined with IBM’s multilingual granite text embeddings transform noisy, heterogeneous task descriptions into dense vector representations. Semantic clustering using cosine similarity then aggregates linguistically diverse but conceptually identical activities into unified clusters. Subsequently, a temporal consolidation module leverages the longitudinal occurrence of activities across monthly reports, incorporating spatial metadata to infer continuous task durations and transitions. Evaluation on 2,424 task instances demonstrates effective reduction of redundancy and robust grouping into approximately $102-111$ semantically coherent clusters. This approach addresses a critical knowledge gap in construction process mining by enabling incorporation of rich textual data, thus enhancing event log completeness and quality for downstream analysis. This advancement enables enhanced workflow tracking and delay detection in construction projects, broadening the scope of process mining beyond traditional reliance on structured data sources. Our work lays foundational steps for integrating unstructured textual data into construction process analytics, offering practical insights for improved project monitoring and decision-making.

The evolution of education ecosystems has histor-ically been linked to social, economic, and technological ad-vancements' culminating in the emergence of education of the future, what we might call Education 4.0. To comprehend the challenges and opportunities presented by this new educational era, it is imperative to examine the preceding educational revolutions, ranging from antiquity to the present day. Education 4.0 should respond to the learning challenges posed by mod-ern society's dynamic education needs, while drawing insights from the benefits of past education generations. In this new context, learners should become architects of their education, and institutions must adapt to cater to individual needs. To fully exploit the potential of technology in education, we require a conceptual model that allows us to optimize the benefits from its utilization while emphasizing new pedagogical approaches. This model must include all stakeholders in a common generic semantic space, enabling students to acquire competencies and teachers to design learning resources that precisely meet the needs of our society. Having this generic common semantic space would facilitate the integration of initiatives proposed by major players, such as the curricula recommendations proposed by the ACM and IEEE. The research results are being applied in the development of the ISA NUM program, a new French computer science engineering training initiative. The program leverages an ontology-driven competency model, aligned with the ACM Computing Curricula 2020, to address the current societal demands in the field of computer science. This model forms the basis for an intelligent cyber-physical education system that integrates virtual and physical learning environments, integrating advanced technologies like virtualization, artificial intelligence, IoT, and semantic web. Moreover, this system integrates learning analytics features aimed at facilitating designing and developing personalized, flexible, and dynamic learning paths. Education 4.0 represents a promising future for education, aligning it with the ever-evolving digital landscape and the specific needs of modern learners and society.

International audience

Blockchain brings many added values to modern business systems. However, Blockchain-based applications with massive IoT devices experience some limitations. The limitations are due to the linear structure and the consensus algorithms used in Blockchain that consume the participating nodes' considerable resources. In addition, IoT devices are generally with limited resources and have limited bandwidth connections. IOTA, based on Directed Acyclic Graph (DAG), is a new distributed ledger technology (DLT) for IoT devices. It proves its high scalability by providing parallel data processing. However, DAG is still not mature enough to fully replace Blockchain. In previous work, we proposed combining Blockchain and IOTA technologies to allow scalable transactions where Blockchain is employed in the backend, and Tangle is used in the frontend. This paper considers the proposed solution, focusing mainly on the connector part that intermediates both DLT technologies. The connector is a decentralized software component that supports the interaction between the DLT implicitly. The experiments' results show the flexibility to merge both DLTs using a message queuing protocol that enables smart contracts to run on the Tangle nodes and enriches the new platform with reliability and working offline features.

Ensuring the safety control of Autonomous Mobility-on-Demand systems is one of the biggest challenges facing designers to successful deployment. The addition of adaptability to such systems further hardens and delays modelling and validating phase, especially due to the current lack of design models and tools. The formal methods have proven to be useful for making the development process reliable at early design stages. Based on this approach, this paper proposes a mixed process to specify, design and verify safety requirements in adaptive AMoD Systems. This process provides analytical proofs of safety requirements during the design stage of a system when changes are cheap. This contribution deals with combining the UML MARTE profile for modelling the workload behaviour of the system and the formalism Net Condition Event System for consistency validation of safety properties. To verify the effectiveness of our proposal, several formal analyses are carried out using the model checker SESA. The evaluation of the proposed architecture, simulated by the Sumo software, proves the impact of the number of autonomous vehicles on the global performance and the intended quality of service (QoS) in the framework of the TORNADO project.

In this paper, we propose a novel three-phase fuzzy approach to optimize dispatching and rebalancing for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, consisting of self-driving vehicles, which provide on-demand transportation service, and allowing several customers to share the same vehicle at the same time. We first introduce a new multi-objective possibilistic linear programming (MOPLP) model for the problem of dispatching and rebalancing in RAMoD systems considering the imprecise nature of the customer requests as well as two conflicting objectives simultaneously, namely, improving customer satisfaction and minimizing transportation costs. Then, after transforming this possibilistic programming model into an equivalent crisp multi-objective linear programming (MOLP) model, the Goal Programming (GP) approach is used to provide an efficient compromise solution. Finally, computational results show the practicality and tractability of the proposed model as well as the solution methodology.

In the last few years, Unmanned Aerial Vehicles (UAVs) are receiving more focus in order to execute a wide variety of applications such as the military, agriculture and medical fields. It is known the high vulnerability of the UAV not only to unexpected faults of their software but also to the environment. For this reason, safety should be considered as the main requirement at design time, since any unexpected behavior of the vehicle or any hazard would lead to potential risks. To maintain their safe operation during their missions, a failsafe mechanism based on Net Condition Event System (NCES) is proposed. The failsafe mechanism is a control logic that guides risk reduction actions to be performed when hazards occur. To generate such a controller using formal models, the proposed process is decomposed into three phases: (1) the first phase consists on hazard identification and analysis according to reactive methods of literature, (2) the second phase allows risk estimation using the standard ISO 13849, and (3) the third phase consists of performing reconfiguration scenario in order to risk mitigation while analyzing safety requirements. The motivation behind the use of formal methods is that they have proven to be useful for making the development process reliable at early design stages. We demonstrate the applicability and feasibility of our proposal on an illustrative medical drone as a case study.

Current automation approaches in the Industry 4.0 have generated increased interest in the utilization of Integration Platforms as a Service (iPaaS) cloud architectures in order to unify and synchronize several systems, applications, and services in order to build smart solutions for automated and adaptive industrial process management. Existing iPaaS solutions present several out-of-the-box connectors and automation engines for easier integration of customers' projects, but show issues regarding overall adaptation outside their scope, brand locking, and occasionally high prices. Moreover, existing platforms fail to respond adequately to the needs of deploying multiple decision models capable of offering automated or semi-automated management of processes, thanks to the integration of the large diversity of data and event sources as well as the different physical or logical action entities. With the popularization of open-source software and applications such as BPM Engines, Machine Learning libraries, and Integration suites and libraries, it is possible to develop a fully customizable and adaptable, open-source iPaaS that can be used both in and outside industrial applications. In this paper, we propose a generic iPaaS architecture implemented on the basis of several open source solutions boasting integration, interoperability, and automated decision-making capabilities in the domain of Agriculture 4.0. A proof-of-concept based on these solutions is presented, as well as a case study on MAÏSADOUR's grain storage process with a comparison with the currently human-operated tasks.

The adoption of the Internet of Things (IoT) drastically witnesses an increase in different domains and contributes to the fast digitalization of the universe. Henceforth, next generation of IoT-based systems are set to become more complex to design and manage. Collecting real-time IoT-generated data unleashes a new wave of opportunities for business to take more precise and accurate decisions at the right time. However, a set of challenges, including the design complexity of IoT-based systems and the management of the ensuing heterogeneous big data as well as the system scalability, need to be addressed for the development of flexible smart IoT-based systems. Consequently, we proposed a set of design patterns that diminish the system design complexity through selecting the appropriate combination of patterns based on the system requirements. These patterns identify four maturity levels for the design and development of smart IoT-based systems. In this article, we are mainly dealing with the system design complexity to manage the context changeability at runtime. Thus, we delineate the autonomic cognitive management pattern, which is at the most mature level. Based on the autonomic computing, this pattern identifies a combination of management processes able to continuously detect and manage the context changes. These processes are coordinated based on cognitive mechanisms that allow the system perceiving and understanding the meaning of the received data to make business decisions, as well as dynamically discovering new processes that meet the requirements evolution at runtime. We demonstrated the use of the proposed pattern with a use case from the healthcare domain; more precisely, the patient comorbidity management based on wearables.

Nowadays, statistical based feature extraction approaches are commonly used in the knowledge discovery field with Machine Learning. These features are accurate and give relevant information of the samples; however, these approaches consider some assumptions, such as the membership of the signals or samples to specific statistical distributions. In this work, we propose to model statistical computation through linear regression models; these models will be divided by classes, in order to increase the inner-class identification likelihood. In general, an ensemble of linear regression models will estimate a targeted statistical feature. In an online deployment, the pool of LR models of a given targeted statistical feature will be evaluated to find the most similar value to the current input, which will be as the estimated of the feature. The proposal is tested with a real world application in traffic network classification. In this case study, fast classification response has to be provided, and statistical based features are widely used for this aim. In this sense, the statistical features must give early signs of the status of the network in order to achieve some objectives such as improve the quality of service or detect malicious traffic.

Network analysis has recently become in one of the most challenging tasks to handle due to the rapid growth of communication technologies. For network management, accurate identification and classification of network traffic is a key task. For example, identifying traffic from different applications is critical to manage bandwidth resources and to ensure Quality of Service objectives. Machine learning emerges as a suitable tool for traffic classification; however, it requires several steps that must be followed adequately in order to achieve the goals. In this paper, we proposed an architecture to perform traffic analysis based on Machine Learning techniques and autonomic computing. We analyze the procedures to perform Machine Learning over traffic network classification, and at the same time we give guidelines to introduce all these procedures into the architecture proposed. The main contribution of our proposal is the reconfiguration of the traffic classifier that will change according to the knowledge acquired from the traffic analysis process.

Advances in the Web and healthcare data capture technologies have far-reaching benefits for the development of new clinical decision support systems that accelerate decisionmaking and generate personalized treatments.However, the diversity of healthcare data formats, the lack of computer interpretable representation of medical interventions, and the distribution of reliable medical knowledge sources constitute important barriers to better support the medical decision process.To deal with these issues, we propose the Treatment Plan Ontology (TPO) that formalizes medical interventions, and allows medical systems sharing and reasoning over them.This knowledge together with the acquired patient data are then reused by the autonomic processes that we have developed in order to timely detect anomalies and support the physicians in personalizing the patient treatment at the right time.We demonstrate the system efficiency through a use case for managing hyperglycemia in type 2 diabetes.

Technological advances such as miniaturization of sensors, mobile devices and increased computational capabilities have given way to Context Aware Applications (ACC), which constantly monitor and change depending on the context. These applications require a variety of features and services, particularly on the management of context. When applications/services lack part or some of these services, they must be consumed from other suppliers who can provide them via a middleware. In addition, due to the changes undergone by the context mean that such services consumed by the ACC must be dynamic; and due to the amount of data involved to define the context, it may require more computation capacity than that provided by a mobile device. For these reason it is necessary the use of the Cloud. In this way, to support these requirements is proposed CARMiCLOC (Context Awareness in Reflective Middleware Cloud Computing), a web-service based middleware, which can behave as a SaaS (Software as a Service) or as a PaaS (Platform as a Service).

The large deployment of Internet resources has facilitated the accelerated development of Intelligent Transport Systems (ITS) including a large diversity of distributed applications such as non-safety infotainment vehicular applications. In vehicular networks, nodes can connect to Internet using Road Side Units (RSUs), but the complete deployment of RSUs along all the transport network seems unfeasible. So, we can take advantage of multihomed devices using complementary access network technologies, such as satellite networks, in order to maintain the connectivity. Several solutions have been implemented in order to take advantage of multihoming and multipath capabilities of mobile nodes. However, in dynamic network scenarios involving multipath communication channels, the QoS requirements of these applications is not always globally managed and guaranteed. Moreover, specific multimedia semantics of the transmitted data is not usually considered by the available transmission mechanisms and protocols. In this work, we present a Multipath TCP communication architecture that take full advantage of the intrinsic multimedia QoS semantics based in Deep Packet Inspection in order to self-manage the available resources and to provide a more compliant e2e transport service.

With the development of Internet technologies, distributed and multimedia applications are more and more used. Because these applications have Quality of Service (QoS) expectations (bounded delay, guaranteed bandwidth, etc.), a set of QoS oriented protocols aimed at aiding end-to-end communications are proposed. Each one of these protocols offers one or several mechanisms. Selecting the best protocol with the best mechanism(s) according to the application requirements and network contexts is a challenge if we want to provide enough QoS to the final users. Also because contexts can evolve, for example when network characteristics change, the previous selected protocol can become obsolete. In this context, an adaptation is mandatory. In this paper, we show how the OSI transport layer can become autonomic in order to perform dynamically reconfiguration actions when it is needed. Our approach consists in including at the transport layer the MAPE loop of the autonomic computing proposal to monitor and analyze the communication in order to predict or to detect a QoS degradation induced by changes of network context, and to plan and execute dynamically new reconfiguration actions in order to keep the communication at the expected QoS level. To implement and evaluate our approach, we extend the new Multipath-TCP (MPTCP) protocol, which offers an extensible application oriented layer. This layer gives opportunity to deploy QoS-aware mechanisms. The test results show that the consumption time of autonomic selection and composition of these mechanisms at run time does not prevent enhancing the QoS, and encourage us to continue working in this approach.

The chronicles paradigm has been used to determine fault in dynamic systems, allow modeling the temporal relationships between observable events to describe the patterns of behavior of the system. The chronicle recognition mechanisms used until now are semi-centralized approaches, which consist of a central component that is responsible for making the final inference about the fault diagnosis of the system, based on the information collected from local diagnosers. This model has difficulty when is implemented for monitoring very large systems. This paper proposes a fault diagnosis system based on distributed chronicles. For that, we need to extend both, the definition of the chronicles as its recognition mechanism, so that they are fully distributed. This paper presents these extensions, and then describes the fault diagnosis system based on distributed chronicles proposed, and an example of its use in SOA applications.

As new transport protocols are being proposed and standardized, the choice of the best communication service to be used by applications for delivering their data when distributed is becoming too complex. Application developers need much knowledge on "how the protocol works" to decide whether or not it can be used to fulfill their requirements. Moreover, the performance of the service provided by a given communication protocol is highly dependent on the network context. The Autonomic Transport Protocol presented in this paper is aware of the application requirements and uses learning techniques to adapt the service it provides to best satisfy these requirements as the network conditions vary.

In the context of Publish/Subscribe systems (Pub/Sub), Peer-to-Peer (P2P) solutions based on Distributed Hash Tables (DHT) offer efficient functionalities such as event routing flexibility, scalability, load balancing and fault tolerance. For event routing, it uses event identifiers computed by event encryption. Likewise, Pub/Sub systems based on ontologies offers well adapted semantic expression capabilities allowing consumers and providers to easily describe services' requests and properties. Thanks to the ontologies, semantic reasoning allows to provide enhanced event discovering. Indeed, DHT-based systems are based on traditional matching algorithms while in ontology-based systems, users can use various literals semantically equivalent but syntactically different expressions in order to describe their requirements. However, both approaches present limitations in terms of: semantic expression restrictions for the first one and scalability issues for the second one. Our solution consists in combining the advantage of both of them, by taking advantage of the semantic capabilities of ontologies at local domain level and the scalable P2P-DHT solutions at inter-domains level. This paper proposes an approach based on a community-wide ontology in order to allow publishers and subscribers to use a common semantic space to characterize production and consumption of resources and services. Based on this ontology, a P2P network based on distributed reasoners (i.e. inference engines) will be able to evaluate if a specific content belongs to an ontology-based community topic. Thus, we use these topics to compute keys for routing events in a DHT.

Multi-homed devices are more and more used in the current Internet, and all kinds of multimedia and other real time applications are expected to be distributed between them through different access network (WiFi, UMTS, etc.). To tackle the challenge that consists in providing a sufficient enough QoS to the final mobile users, several attempts have been and are still currently done at the Transport level. One one side, the goal is to define how to make use of the different interfaces available at the device level. On the other side, it is to define how to configure dynamically and autonomously a new generation of multi-path Transport protocols aimed at fitting as best as possible the QoS requirements and the network constraints. Towards this goal, this paper provides contributions aimed at illustrating how the autonomic computing concept may be applied within a multi-path Transport protocol with the aim to provided an as high as possible QoS for infotainment-like video applications distributed between mobiles and multi homed devices. The focus is done on the exploration of a new QoS-aware load balancing mechanism and on the benefits that could be done when the network context changes, by plugging in dynamically into an autonomic multi homed Transport protocol.

The increasing number of interfaces using different access technologies in modern devices gives opportunities for enhancing the quality of service (QoS) delivered to multimedia and interactive data transfers involved in mobile and distributed applications. In the modern Internet though, the presence of "middleboxes" (such as NATs, firewalls or proxies) hardly lets applications use any transport protocol but the well-known Transmission Control Protocol (TCP). Currently under standardization at the IETF, the new Multipath-TCP (MPTCP) protocol uses several TCP flows to make use of the multiple interfaces available on the end terminals, thus improving both network availability and QoS, still being capable to cross over middleboxes. Although originally being fully reliable and fully ordered, its two sub-layers architecture gives opportunity to use QoS techniques over fully reliable paths. This paper studies the QoS benefits induced by the implementation of the "partial reliability" concept in MPTCP for interactive video applications based on the codec H.264. Two different mechanisms are thus proposed and experimented with the aim to enhance global quality of video transmission over paths close to 3G networks characteristics. The IETF ns-2 implementation of MPTCP is used for the experimentations and the performances evaluation of the proposed contribution. The Evalvid toolkit over ns-2 is used to measure the QoS benefits expressed in terms of PSNR.

The design and development process of complex systems require an adequate methodology and efficient instrumental support in order to early detect and correct anomalies in the functional and non-functional properties of the solution. In this article, an unified experimentation framework (UEF) providing experimentation facilities at both design and development stages is introduced. This UEF provides a mean to achieve experiment in both simulation mode with UML2 models of the designed protocol and emulation mode using real protocol implementation. A practical use case of the experimentation framework is illustrated in the context of satellite environment.

No abstract available.

The design of wireless cooperative mobile service-oriented applications with machine-to-machine communication capabilities requires providing solutions that include service and Quality of Service (QoS) provisioning as key features. Self-adaptability and self-organization are properties that help managing services and associated QoS for communication, vertically and horizontally. This paper addresses self-adaptability as a key feature for model-oriented and architecture-driven QoS management. We study the different abstraction levels at which the adaptability can be managed. Such decomposition allows mastering the complex handling of self-adaptive group-wide QoS for cooperative activities supported by wireless and mobile ad-hoc communications. A case study based on cooperative group-activity support is developed to illustrate the proposed concepts.

The design and development process of communication protocols and real-time systems and particularly transport protocol mechanisms requires adequate methodology and efficient instrumental support. In this paper, an extensible and QoS-oriented development framework integrating design and simulation with UML (Unified Modeling Language), and implementation and evaluation with emulation is introduced. An early use of the proposed framework is illustrated with the design and development of simple transport mechanism.

Differential VoIP services for Wi-Fi will greatly enhance the experience of mobile wireless users who have requested premium service for their VoIP applications. In this work we present detailed simulations of modified 802.11b protocols that purport to provide differential service to wireless VoIP users. The modified protocol we identify as providing the best differential service whilst maintaining the least starvation to best effort users - involves a modified minimum contention window, combined with a modified back-off time. A detailed differential VoIP capacity analysis of this modified protocol is presented. Also, we introduce the notion of a "priority service QoS map" for wireless VoIP. Such a map is a two dimensional representation of the VoIP QoS metrics available to a mobile priority VoIP user, as a function of location. We outline how our VoIP capacity analysis is used to construct a priority service QoS map, and discuss the practical benefits of relaying such maps to mobile users of wireless VoIP

Face to the complexity involved in the wide deployment of QoS network services as well as the reduced set of services offered by existing transport protocols, this paper proposes a QoS-oriented transport protocol (QoSTP) as the adequate solution for providing soft QoS guarantees to common Internet users for next few years. The design of this QoSTP is based on a set of fundamental principles aimed at assuring the feasibly and efficient deployment of adequate mechanisms intended to satisfy different applications requirements. Moreover, this new protocol has to be designed within an extensible and compositional architectural aimed at integrating further mechanisms intended to satisfy new requirements and to operate under heterogeneous network environments. This paper presents the UML 2.0 specification model of this advanced architecture as well as experimental results intended to demonstrate the feasibility and the advantages of this approach.

Transport protocols implement mechanisms intended to satisfy the requirements of distributed applications making use of available network services. But the multiplicity of network services and the diversity of quality of service (QoS) requirements of current multimedia applications, make that efficient deployment of transport services becomes a complex function to be performed. The fully programmable transport protocol (FPTP) is a configurable and object-oriented protocol based on a compositional architecture of transport mechanisms suited to the provision, control, and management of the QoS. FPTP offers a partially ordered, partially reliable, congestion controlled and time controlled end-to-end communication service. FPTP services are intended to be easily used by legacy and specialized multimedia applications over heterogeneous transport and network services. In this paper, the unified modelling language (UML) and the specification and description language (SDL) have been used to design this transport protocol within a global QoS framework. Some study cases related to the configurable congestion control policies and aimed to demonstrate the advantages this proposal presented.