Transportation sector undergoes a considerable transformation as it enters a new landscape where connectivity is seamless and mobility options and related business models are constantly increasing. Modern transportation systems and services have to mitigate problems emerging from complex mobility environments and intensive use of transport networks including excessive CO2 emissions, high congestion levels and reduced quality of life. Due to the saturation of most urban networks, innovative solutions to the above problems need to be underpinned by collecting, processing and broadcasting an abundance of data from various sensors, systems and service providers. Furthermore, such novel transport systems have to foresee situations in near real time and provide the means for proactive decisions, which in turn will deter problems before they even emerge. Our vision is to provide the required interoperability, adaptability and dynamicity in modern transport systems for a proactive and problem-free transportation system. OPTIMUM will establish a largely scalable, distributed architecture for the management and processing of multisource big-data, enabling continuous monitoring of transportation systems needs and proposing proactive decisions and actions in an (semi-) automatic way. OPTIMUM follows a cognitive approach based on the Observe, Orient, Decide, Act loop of the big data supply chain for continuous situational awareness. OPTIMUM's goals will be achieved by incorporating and advancing state of the art in transport and traffic modeling, travel behavior analysis, sentiment analysis, big data processing, predictive analysis and real-time event-based processing, persuasive technologies and proactive recommenders. The proposed solution will be deployed in real-life pilots in order to realise challenging use cases in the domains of proactive improvement of transport systems quality and efficiency, proactive charging for freight transport and Car2X communication integration.

The objective of MARKET4.0 is to define, develop and validate an open multi-sided marketplace, based on a trusted P2P data sharing infrastructure for Industry 4.0, that brings together Industrial Product Service Systems (IPSS) providers (supply side) and OEMs as their customers (demand side). It will allow direct interaction among the different sides to improve the sales power of production equipment SMEs. MARKET4.0 will offer advanced web-presence of production equipment SMEs extended by functionalities such as simulations, VR/AR capabilities and a P2P (Industrial Data Space) offering smart user-services and a secure API to try and test the Digital Twin of the production equipment, on top supplier and customer data. This will facilitate direct transactions between market peers (supplier-to-supplier, supplier-to-customer, customer-to-suppliers and more) during the entire B2B lifecycle, i.e. from equipment search to procurement and commissioning. MARKET4.0 will create trust in the business transactions between SME production equipment manufacturers and their customers, as an integral element of the Industrial Data Space (IDS) reference architecture. It includes technological trust (simulation before purchasing), financial trust (Blockchain distributed ledger technology for secure payment), delivery and (anonymized) feedback. In MARKET4.0 the supply side (mainly SMEs) may offer a) production equipment, b) services that extend the capabilities of the production equipment, c) production equipment as a service and d) collaborative engineering services. The marketplace will generate additional value by providing engineering services and acting as a mediator between suppliers and customers.

Europe has a lack of intellectual property in integrating artificial intelligence (AI) into digital applications. This is critical since the automatization reached saturated levels and AI in digitisation is an accepted approach for the upcoming transformation of the European industry. The potential of AI in economy and society is by far not enough exploited. Potential users of AI are not sufficiently supported to facilitate the integration of AI into their applications. Enabling of performance, industry and humanity by AI for digitising industry is the key to push the AI revolution in Europe and step into the digital age. Existing services providing state of the art machine learning (ML) and artificial intelligence solutions are currently available in the cloud. In this project, we aim to transfer machine learning and AI from the cloud to the edge in manufacturing, mobility and robotics. To achieve these targets we connect factories, processes, and devices within digitised industry by utilizing ML and AI for human machine collaboration, change detection, and detection of abnormalities. Hence, we gain knowledge by using existing data and arrange them into a processable representation or collect new data. We use this knowledge to change the semantics and the logical layer with a distributed system intelligence for e.g. quality control, production optimization. In AI4DI, we define a 7-key-target-approach to evaluate the relevance of AI methods within digitised industry. Each key target represents a field of activity and the corresponding target at the same time, dividing systems into heterogenous and homogenous systems, and evolving a common AI method understanding for these systems as well as for human machine collaboration. Furthermore, we investigate, develop and apply AI tools for change detection and distributed system intelligence, and develop hardware and software modules as internet of things (IoT) devices for sensing, actuating, and connectivity processing.

In the era of hyper-connected digital economies, the smart technologies play a vital role in the operation of the Electrical Power and Energy Systems (EPES), transforming it into a new, decentralised model with multiple benefits, such as distributed generation, pervasive control, remote monitoring, and self-healing. However, the growing number of cybersecurity incidents in EPES promotes the need for shielding against a variety of threats, ranging from cyberattacks, dynamic and evolving Advanced Persistent Threats (APTs), and privacy violations, to electricity disturbances and severe human errors caused by lack of relevant training. The diverse threats that modern EPES are facing require novel and holistic solutions that employ cutting-edge technologies to detect and mitigate threats, while continuously assessing the dynamic EPES environment, ensuring compliance with the latest cybersecurity standards and training the EPES personnel to appropriately respond to cybersecurity incidents and mitigate the human-error factor. Considering these, ELECTRON aims at delivering a new-generation EPES platform, capable of empowering the resilience of energy systems against cyber, privacy, and data attacks through four main pillars (risk assessment and certification, anomaly detection and prevention, failure mitigation and energy restoration, and addressing internal threats and gaps through AR-VR-based personnel training and certification), while fostering the cyber protection standardisation and certification via three novel authorities, namely the cybersecurity lighthouse, the cybersecurity training and certification authority, and the energy trading centre.