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IF=3.954; International audience; This editorial introduces the special issue in the Elsevier journal Computers in Industry that analyses how the digital transformation of manufacturing is speeded up by two important drivers: cloud services and resource virtualization, which are vital for implementing the main building blocks - Cyber Physical Production Systems and Industrial Internet of Things - in the “Industry of the future” framework. The context of this special issue is firstly presented, with a specific focus on the federative concept of Industry 4.0. A framework characterizing research activities led in the field of the digital transformation of manufacturing processes and systems is then introduced. This framework is used to present and position the 12 papers composing the special issue. Perspectives are finally introduced as a guideline for future work in the digital transformation of manufacturing through cloud services and resource virtualization

TC 5: Information Technology Applications; International audience; The last two decades have witnessed considerable boost in emergence of a networked society, reflecting the increasing growth in hyper-connectivity among the organizations, people, smart machines, and intelligent systems. This trend was enabled by advances in ICT and more specifically in computer networking. In this context, new forms of coworking and collaboration in networks, composed of distributed, autonomous, and heterogeneous entities have emerged, which first led to the formation of Collaborative Networks (CN) as a new discipline, and then followed by series of milestones leading to its gradual evolution.Nowadays CNs play a key role in the ongoing process of digital transformation in industry and services. Although it is relatively young, a number of “generations” can be identified through the last decades for the CN discipline. We are now at the beginning of what can be identified as the Collaborative Networks 4.0, characterized by features such as: hybridization in CNs, collaboration between humans and intelligent autonomous systems, collaborative distributed cognitive systems, reflecting on collaborative accountability, handling ethics and coping with risks and disruptions faced in CNs, managing large amounts of collaborative data, monetization of collaboration, creating a collaboration culture, supporting collaboration creativity, handling mass collaboration, and supporting collaborative value creation through new business models, among others.The IFIP WG 5.5, through its annual conference the PRO-VE, which is now in its 21st edition, has played a determinant role, along these two decades. It has contributed to shaping, promoting, and extending the CN research and development community and its practices; thus, consolidating this area, and identifying and introducing new directions and preserving it as an active research agenda.

In modern society, citizens aspire to get trusted and reliable digital services to authenticate theirs to payments. With the COVID-19 crisis, online shopping’s fast growth has led citizens to increase registration in different systems. The registration is typically done without any guarantee that the involved business entity is trusted and that private data is managed adequately, namely according to the General Data Protection Regulation (GDPR). There are cases where online business adopts a federated authentication mechanism based on the existing and extensively adopted service providers, e.g., Facebook, and Google. With the European authorities’ complacency, this de facto trend seems to contribute to a dangerous unregulated digital services model. While avoiding the centralization risks, a possible alternative is to pursue the concept of regulated and competing digital online shops or services offered under a single collaborative model across Europe. Citizens aspire to get simple mechanisms based on a single provider for authentication and pay anywhere, even with some associated costs. In this direction, we propose a model that considers regulated providers managing citizens’ access to any online business in Europe, avoiding, in this way, the spreading of personal data across (business) organizations, thus decreasing the risk of personal data leaks. A collaborative network is foreseen to logically tie committed regulating authorities, providers, and digital online service providers. The proposed approach is ground on our previous research on systems integration, collaborative network infrastructure, and unified mobility payment services. This position paper offers a digital strategy for citizens, designated by Digital Person Ecosystem (DPE), which relies on Collaborative Networks concepts and centered on public authority leadership.

This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.

Part 5: Biomedical Applications; International audience; A low-cost inertial measurement unit has been developed for accurate motion capture, allowing real-time spatial position registration (linear and angular) of the user’s whole-body. For this, we implemented a dedicated circuit for 9 degrees of freedom motion sensors, composed of an accelerometer, gyroscope and a magnetometer. We also applied signal processing and data fusion algorithms to prevent the inherent drift of the position signal. This drift is known to exist during the sensor integration process and the implemented algorithms showed promising results. This system is meant to be used in two specific biomedical applications. The first one is linked to the development of a low-cost system for gait analysis of the whole-body, which can be used in home-based rehabilitation systems. The second application is related to the real-time analysis of working postures and the identification of ergonomic risk factors for musculoskeletal disorders.

Part 18: Telecommunications; International audience; In this paper we address the node localization problem in large-scale wireless sensor networks (WSNs) by using the received signal strength (RSS) measurements. According to the conventional path loss model, we first pose the maximum likelihood (ML) problem. The ML-based solutions are of particular importance due to their asymptotically optimal performance (for large enough data records). However, the ML problem is highly non-linear and non-convex, which makes the search for the globally optimal solution difficult. To overcome the non-linearity and the non-convexity of the objective function, we propose an efficient second-order cone programming (SOCP) relaxation, which solves the node localization problem in a completely distributed manner. We investigate both synchronous and asynchronous node communication cases. Computer simulations show that the proposed approach works well in various scenarios, and efficiently solves the localization problem. Moreover, simulation results show that the performance of the proposed approach does not deteriorate when synchronous node communication is not feasible.

International audience; The key idea of this work, is to use risk assessment to support the user in deciding which service should be used, from a set of services developed to support life-cycle optimization, in a specific situation. The risk of a specific situation affecting an industrial plant, characterized by the symptoms, is estimated from the information stored on the system concerning the probability of occurrence of the consequence and its impact. It is expected that this knowledge grows along the life-cycle of a industrial plant. Then, depending of the knowledge available and on the risk of the situation, the adequate service is suggested for promptly reaction in eliminating the problem or avoiding critical situations.

International audience; Age-Related Macular Degeneration (ARMD) is considered the leading cause of irreversible blindness in developed countries. One of its risk factors is the presence of drusens, which are retina abnormalities appearing as yellowish spots in fundus images. In this article a methodology using image processing techniques for the quantification of drusens is presented. The method uses splines combined with a contrast normalization to correct uneven illumination, followed by a drusen detection and modelling algorithm. The detection uses a gradient based segmentation algorithm that isolates drusens. They are then fitted by Gaussian functions, producing a model that is used to compute the area affected. To validate the methodology, 22 images were marked by three ophthalmologists and compared to the automated method. The sensitivity and specificity for the automated process (0.664 and 0.963) were comparable to that obtained among the specialists (0.656 and 0.971). Also, the Intraclass Correlation Coefficient showed an agreement of 74.9% between the processed images and the specialists' analysis.

Part 7: Perception and Signal Processing; International audience; In this work, we address the target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy range measurements, extracted from the received signal strength (RSS) information, we formulate the localization problem based on the maximum likelihood (ML) criterion. ML-based solutions are particularly important due to their asymptotically optimal performance, but the localization problem is highly non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP), which can be efficiently solved by interior-point algorithms. Furthermore, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS measurements. This simple procedure may decrease the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy. Moreover, they show that the new approach does not suffer significant degradation in its performance when the number of cooperating nodes is reduced.

International audience; This paper discusses how to map between Semantic Resources (SRs) specifically created to represent knowledge in the Construction Sector and how to measure and assess the quality of such mappings. In particular results from the FUNSIEC project are presented, which investigated the feasibility of establishing semantic mappings among Construction-oriented SRs. The paper points to the next lines of inquiry to extend such work. In FUNSIEC, a Semantic Infrastructure was built using SRs that were semantically mapped among them. After the positive results from FUNSIEC, the obvious questions came up: how good are the mappings? Can we trust them? Can we use them? This paper presents FUNSIEC research (approach, methodology, and results) and the main di-rections of investigation to support its continuation, which is based on the ap-plication of fuzzy logics to qualify the mappings produced.

International audience; Deciding whether an open source software (OSS) meets the requiredstandards for adoption in terms of quality, maturity, activity of development anduser support is not a straightforward process. It involves analysing various sourcesof information, including the project’s source code repositories, communicationchannels, and bug tracking systems. OSSMETER extends state-of-the-art techniquesin the field of automated analysis and measurement of open-source software(OSS), and develops a platform that supports decision makers in the processof discovering, comparing, assessing and monitoring the health, quality, impactand activity of opensource software. To achieve this, OSSMETER computestrustworthy quality indicators by performing advanced analysis and integrationof information from diverse sources including the project metadata, source coderepositories, communication channels and bug tracking systems of OSS projects.