Big data is nowadays being integrated in systems requiring to process a vast amount of information from (geographically) distributed data sources, while fulfilling the non-functional properties (real-time, energy-efficiency, communication quality and security) inherited from the domain in which analytics are applied. Examples include smart cities or smart manufacturing domains. ELASTIC will develop a novel software architecture (SA) to help system designers to address this challenge. The SA will incorporate a novel elasticity concept to distribute and orchestrate the resources across the compute continuum (from edge to cloud) in an innovative fog computing environment. The new elasticity concept will enable to match analytics workload demands and fulfilling non-functional properties. The fog computing architecture will incorporate energy-efficient parallel architectures, combined with innovative distributed storage, secure communications and advanced cloud solutions. Overall, the SA will enable the combination of reactive data-in-motion and latent data-at-rest analytics into a single extreme-scale analytics solution, in which the analytics workloads will be efficiently distributed across the compute continuum based on their suitability and data processing needs. The capabilities of ELASTIC will be demonstrated on a real smart-mobility use case, featuring a heavy sensor infrastructure to collect data across the Florence tramway network, equipped with advanced embedded architectures, heterogeneous sensors, V2I connectivity and access to cloud resources. Representative applications for advanced driving assistant systems, predictive maintenance and public/private transport interaction, have been selected to efficiently process very large heterogeneous data streams from distributed sensors. ELASTIC technology will enable the development of innovative mobility services while preparing the technological background for the advent of full autonomous mobility systems.

Health care is an essential service that uses a great deal of sensitive personal data which has a high black market value being a lucrative target for data theft and ransomware attacks.The EU NIS Directive (EU 2016/1148) and GDPR (EU 2016/679) will harmonize and improve information security in Europe. Both require relevant ICT infrastructure operators to perform risk assessments, introduce appropriate security measures to manage identified risks, and report security breaches. Unfortunately, risk-based approaches are notoriously difficult to implement in a consistent and comprehensive fashion. They depend on a high level of understanding of both cybersecurity and of the system or network to be protected, are labour intensive and costly and typically done by small teams. This is increasingly inappropriate as health care providers introduce IoT systems, cloud services and (in the near future) 5G networks to provide services in which patients are more engaged, may own some of the devices used, and want access in hospitals, on the move or at home. The ProTego project will develop a toolkit and guidelines to help health care systems users address cybersecurity risks in this new environment by introducing 3 main advances over current approaches: Extensive use of machine intelligence: a combination of machine inference exploiting a priory knowledge for security-by-design, and machine learning from data for run-time threat detection and diagnosis; Advanced data protection measures: advanced encryption techniques and hardware based full memory encryption, and multi-stakeholder IAM to control access to and by user devices, to protect data at rest and provide ultra-secure data exchange portals; Innovative protocols for stakeholder education: using security-by-design analysis to target training and support stakeholders to contribute to networok overall security.The toolkit will be integrated and validated in IoT and BYOD-based case studies at two hospitals.

European manufacturing competes in a global knowledge-driven economy, and thus increasingly seeks competitive advantage through quality, agility and personalisation based on latest advances in IT. Increasing utilisation of IT in mission-critical elements of the production brings opportunities for consistency, transparency and flexibility, bringing the “lot size of 1” closer to reality even for mass-production industries. Most relevant to achieve the expected increase of production performance for highly customized products is to master the complexity of the supply chain and logistics in the global production networks. Ad-hoc collaboration and setup of production coalitions with a wide spectrum of suppliers and service providers is necessary to answer the customization wishes of customers for their individual products in short time, high quality, and at affordable costs. Innovations from small high-tech companies requested by customers have to be integrated into the traditional industrial processes using novel organisational concepts and setups. DIGICOR will address the development of a collaboration platform, tools, and services for the setup and coordination of production networks and in particular the integration of non-traditional, small, yet innovative companies (SMEs) and logistics providers into the supply chain of large manufacturers (OEMs). The solution is based on an open platform integrating tools and services and implementing case specific governance rules and procedures for collaboration, knowledge protection, and security. The open platform will provide services creating a marketplace for the collaboration partners, for planning and control of the collaborative production and the logistics and risk management. It will be open to third parties to add services for advanced analytics, simulation, or optimization etc. The platform will provide seamless connectivity to the automation solutions, smart objects, and real-time data sources across the network

Due to the proliferation of ICT Technologies, manufacturing industry is undergoing substantial transformation in terms of HW but also in terms of Cyber-Physical Production Systems and the SW and services used within production environments. In parallel, the manufacturing processes of the future are changing and need to be highly flexible and dynamic in order to satisfy customer demands for, e.g. large series production, mass customization, or changing orders. To keep pace with the needs of the manufacturing industry of the future, in Manufacturing 4.0 companies need to flexibly react to these demands and be able to offer production capacities in a rapid way. Thus companies looking for manufacturing capacity need to be supported by the means to find these capacities, configure them, and integrate them into their own manufacturing processes. To achieve this, one obvious approach is to port successful concepts from the field of Everything-as-a-Service (XaaS) and Cloud computing to manufacturing to mirror agile collaboration through flexible and scalable manufacturing processes: • Leasing and releasing manufacturing assets in an on-demand, utility-like fashion • Rapid elasticity through scaling leased assets up and down if necessary • Pay-per-use through metered service Applying these principles, Cloud manufacturing can move manufacturing processes from production-oriented to service-oriented networks by modelling single manufacturing assets as services in a similar way as SaaS or PaaS solutions. By modelling all process steps and manufacturing assets as services it is possible to realize cross-organization manufacturing orchestrations and integrate distributed resources and ultimately manufacture products more efficiently. While the theoretical foundations for Cloud manufacturing are manifest there are no proven tools and technologies exist in the market - CREMA aims to change this fact by providing Cloud-based Rapid Elastic Manufacturing based on SaaS and Cloud model

AI is one of the biggest mega-trends towards the 4th industrial revolution. While these technologies promise business sustainability and product/process quality, it seems that the ever-changing market demands and the lack of skilled humans, in combination with the complexity of technologies, raise an urgent need for new suggestions. Suggestions that will be agile, reusable, distributed, scalable, accountable, secure, standardized and collaborative. To break the entry barriers for these technologies and unleash their potential, the knowlEdge project will develop a new generation of AI methods, systems and data management infrastructure. This framework will provide means for the secure management of distributed data and the computational infrastructure to execute the needed analytic algorithms and redistribute the knowledge towards a knowledge exchange society. To do so, knowlEdge proposes 6 major innovations in the areas of data management, data analytics and knowledge management: (i) A set of AI services that allow the usage of edge deployments as computational and live data infrastructure, an edge continuous learning execution pipeline; (ii) A digital twin of the shop-floor to test the AI models; (iii) A data management framework deployed from the edge to the cloud ensuring data quality, privacy and confidentiality, building a data safe fog continuum; (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system to trigger an automatic discovery of knowledge that allows the system to adapt automatically to system changes; (v) A set of standardization mechanisms for the exchange of trained AI-models from one context to another; (vi) A knowledge marketplace platform to distribute and interchange AI trained models. The knowlEdge consortium consists of 12 partners from 7 EU countries, and its solution will be tested and evaluated in 3 manufacturing sectors.