DT4GS is aimed at delivering an “Open Digital Twin Framework” for both shipping companies and the broader waterborne industry actors to tap into new opportunities made available through the use of Digital Twins(DTs). The project will enable shipping stakeholders to embrace the full spectrum of DT innovations to support smart green shipping in the upgrade of existing ships and new vessels. DT4GS will cover the full ship lifecycle by embracing federation of DT applications as well as utilising DTLF policies and related shared-dataspace developments for the sector. DT4GS applications will focus on shipping companies but will also provide decarbonisation decision-support system for shipyards, equipment manufacturers, port authorities and operators, river commissions, classification societies, energy companies and transport/corridor infrastructure companies. DT4GS’s objectives are to: 1. Support shipping companies in achieving up to 20% reduction in CO2e with a 2026 horizon, by developing and deploying real-time configurable DTs for ship and fleet operational performance optimisation in 4 Living Labs involving shipping companies, with different vessel types, and establishing fully validated industry services for Green Shipping Operational Optimisation DTs expected to be adopted by 1000+ ships by 2030. 2. Establish a comprehensive zero-emission shipping methodology and support Virtual Testbed and Decision Support Systems that address both new builds and retrofits comprising: a. A DT4GS (Green Shipping) Dataspace for the broader shipping sector contributing to GAIA-X by establishing a core European industry resource that accelerates the green and digital transition of waterborne shipping and transport value chains. b. Simulation based solutions to retrofit ships, targeting 55% reduced CO2e reduction by 2030. c. A smart green “new-build” reference design per vessel type. d. Virtual Testbed services for reducing the cost of physical testing of GS solutions by 20%.

The integration of edge computing, advanced 5G connectivity, and decentralized processing drives the widespread deployment of private edge ecosystems capable to reshape numerous industry sectors. However, unlocking the full potentials of edge-level intelligent management requires concerted efforts in platform development and cross-sector collaboration. COP-PILOT, develops a Collaborative Open Platform framework geared towards orchestrating end-to-end services across diverse industry domains. In crafting an open platform, COP-PILOT provides a flexible solution designed to effectively manage various industry sectors while ensuring robust security, automation, and intelligence features. Regarding interoperability, the framework seamlessly integrates with underlying technologies, ranging from IoT platforms to core infrastructure, facilitating collaboration across the compute continuum. Furthermore, COP-PILOT empowers the development of advanced cross-sector applications by offering support for cutting-edge network services, thereby enabling heightened security, resource management, and automation capabilities. The implementation strategy revolves around two primary directions: enabling platform implementation and real environment integration. For the former, COP-PILOT adopts a modular orchestration approach, simplifying the onboarding of complex applications through a user-friendly generative AI interface. This approach includes integration with multi-tiered data processing, policy-driven optimization, and dynamic reasoning capabilities, ensuring alignment with prevailing industry standards. In terms of real environment integration, the platform is deployed across four large piloting clusters, addressing a diverse array of edge paradigms. These use cases span across energy, smart city, agriculture, and industrial manufacturing sectors, fostering the development of cross-sector applications in mobility, logistics, and resource management.

There is a strong upcoming need for communication and beyond technology on the 2030 horizon, with the transformations having been set in motion by 5G, and increasing expectations in society, accelerated by advancements in enabling technology, and moving toward new services and use cases. To address the demanding and diverse needs of the anticipated use cases, 6G networks must be highly programmable, exceedingly adaptable, and efficient. Nevertheless, the additional level of efficiency, programmability and flexibility will come at the expense of increasing complexity in managing and operating 6G networks. To bring this complexity under control, a paradigm shift toward complete automation of network and service management is required. However, a significant obstacle to full automation is the protection of the network services, infrastructure and data against possible cybersecurity risks introduced by the unheard-of expansion of the 6G threat landscape. ROBUST-6G aims to address these cybersecurity risks by 1) providing fundamental contributions to the development of data-driven, AI/ML-based security solutions to meet the new concerns posed by the dynamic nature of the future cyber-physical continuum 2) implementing fully autonomous zero-touch security management functionalities 3) exposing security functionalities to verticals and providing necessary data management 4) protecting AI/ML systems from security attacks and ensuring privacy of individuals whose data is used in the AI-driven systems. 5) providing secure, privacy-preserving and robust distributed intelligence with transparency enhancements by elaborating the explainability in AI/ML 6) achieving energy efficient 6G network design with green and sustainable AI methodologies 7) leveraging different sources of information, ranging from sensing, positioning to authentication, and advanced AI/ML methodologies for detection and mitigation of physical layer attacks and threats.

We will develop an Integrated Software Toolbox that will offer predictive Cybersecurity sensing, optimization and management services for the distributed IoT-to-Cloud continuum. The scope is to continuously maximizing the continuum’s infrastructure security and data privacy with minimal effect on its computing capacity, energy consumption, monetary costs, etc. that are necessary for the businesses to run. To do so, Twinning tools will be replicating the continuum as virtual (isolated) dataspaces, such that Threat Intelligence tools can stress-test the attack-surface of these dataspaces and predict optimal risk mitigation measures accurately and safely (i.e. pre-emptively protecting and without affecting the real system operation), subject to certain computing, energy, etc. thresholds set by the end-user. Scalability & Interoperability of services and data will be achieved via the use of open standards & APIs (including Eclipse Connector), while AI Automation will be embedded in all Toolbox processes. Identity & Access Management will be advanced over a zero-trust distributed computing pipeline covering all Hardware-, System-, and Application-level security. An initial Lab-testbed with matured DevSecOps & ML/Data-Ops stemming from previous EU Actions will lead our developments roadmap to 4x + 1 final TRL7 operational deployments that will be validated over 1st & 2nd Stage Demos covering use cases related to Telcos, Health 4.0, Transportation, Safety-critical Nuclear Infrastructures, and Smart Cities (cross-vertical supply-chain assessments). The impact will not only safeguard EU position in data security, economy and applications verticals, but lower energy efficiency and CO2 footprint. Sustainability & Industry acceptance will be achieved via open source ecosystems, standardization involvement, and a dedicated Portal for linking our dataspaces to EU Data Spaces (GAIA-X, IDSA, etc) and our innovations to EU Bodies related to Cybersecurity, AI, IOT and Robotics.