Partners

    UNIVERSIDAD DE CANTABRIA, ISTR Group, IPs: J. Javier Gutiérrez and Marta E. Zorrilla (Coordinators)
    UNIVERSITAT POLITÈCNICA DE VALÈNCIA, GII Group, IPs: Patricia Balbastre and José Simó
    UNIVERSIDAD POLITECNICA DE MADRID, STRAST Group, IPs: José M. del Álamo and Félix Cuadrado
    IKERLAN S.COOP., Distributed and Connected Intelligence Department, IP: Unai Díaz de Cerio

Summary

The so-called Industry 4.0 has initiated a transformation in all social and economic spheres with the introduction of the concepts of the Industrial Internet of Things (IIoT), that is, the digital interconnection of cyber-physical systems that include monitoring and control systems for tools and machines, with systems connected to control centers. A major key to this is the improvement of predictability and reliability at all levels in the digital transition of highlyconnected industry. These systems are supported by the use of digital enabling technologies and the increasing application of artificial intelligence techniques that allow enhancing the automation value chain. Some of the technologies and techniques mentioned above are not exclusive to industrial systems and are shared by other new domains with similar needs, e.g. the smart mobility. Recently, the European Cloud, Edge and IoT Continuum initiative opens a new perspective on how to deal with the development of new technologies looking at reducing the communications load by processing data closer to where it is produced, which is enabled by the availability of more powerful computing platforms in the edge. This initiative identifies applications from different sectors (including manufacturing and mobility among others) where reliability aspects are present.

The proposed project is based on previous experience in developing basic technologies for Industry 4.0, which can be naturally adapted to this new paradigm, and which are intended to be further explored. The present project aims to address the integration and extension of enabling technologies in highly connected industrial applications while achieving coherence across the computing continuum. Key contributions include updated operating systems, middleware integration, system modeling tools, response time analysis, data management frameworks, and application to industrial use cases.

The project is structured by following the computing continuum view in which data and the place where they are processed play a fundamental role. Thus, the control and management of IoT devices by designing minimal operating systems, creating efficient communication interfaces, and exploring the application of AI at the IoT level will be explored. Challenges at the edge level include: data distribution across the continuum, advanced scheduling and optimization on heterogeneous platforms, and the implementation of AI-powered analytics and autonomous decision-making capabilities. Development of smart industrial applications, focusing on enabling data sharing and interoperability across the computing continuum will be addressed at the cloud level. Two industrial case studies are proposed in order to test and validate the applicability of the methods and techniques to be developed. Artificial intelligence, interoperability and governance are cross-cutting pillars that enable predictive analytics and real-time optimization, ensuring integration, reliable decision-making and sustainable management across all layers of the computing continuum. Within this vision, the techniques, methodologies and tools to be developed should be compatible at all levels of the computing continuum.