ENACT develops cutting-edge techniques and technology solutions to realise a Cognitive Computing Continuum (CCC) that can address the needs for optimal (edge and Cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. At infrastructure level, the project brings visibility to distributed edge and Cloud resources by developing Dynamic Graph Models capable of capturing and visualising the real-time and historic status information, connectivity types, dependencies, energy consumption etc. from diverse edge and Cloud resources. The graph models are used by AI (Graph Neural Networks - GNN) models and Deep Reinforcement Learning (DRL) agents to suggest the optimal deployment configurations for hyper distributed applications considering their specific needs. The AI (GNN and DRL) models are packaged as an intelligent decision-making engine that can replace the scheduling component of open-source solutions such as KubeEdge. This will enable real-time and predictive management of distributed infrastructure and applications. To take full advantage of the potential (compute, storage, energy efficiency etc) opportunities in the CCC, ENACT will develop an innovative Application Programming Model (APM). The APM will support the development of distributed platform agnostic applications, capable of self-determining their optimal deployment and optimal execution configurations while taking advantage of diverse resources in the CCC. An SDK to develop APM-based distributed applications will be developed. Moreover, services for automatic (zero-touch provisioning-based) resource configuration and (telemetry) data collections are developed to help design and update dynamic graph models. ENACT CCC solutions will be validated in 3 use-cases with challenging resource and application requirements. International collaboration is planned as Japan Productivity Center has committed to support with knowledge sharing.

RobetArme will deliver a human-robot collaborative construction system for the automation of shotcrete (i.e. concrete spray casting), which is an emerging technology in construction domain. A multitasking Inspection-Reconnaissance mobile manipulator-IRR (ROB) will fuse the latest Geotechnical models (BIM/CIM) (DTT), high-density visual data and semantic SLAM (CERTH) representations to automate modelling and fast reconstruction (DTU) of the surface to be shotcreted. IRR will facilitate rebar reinforcement through metal additive manufacturing (ANIMA) capitalizing on its precise repair skills (EPFL). The Shotcrete and Finishing mobile manipulator ?SFR (COBOD) will apply dexterous concrete placement through closed-loop visual guidance methods, suitable for turbid conditions (KUL). Concrete mix-design and study on innovative reinforced cementitious materials (TITAN) will contribute to the reduction of materials? and water waste during shotcrete. The SFR robot equipped with universal tool changing (ROB) will also automate the surface finishing step through delicate and human-like robot manipulations, enabled from a safety operation toolkit that includes physical human-robot collaboration and human-aware navigation (CERTH). A Digital Twin (DTT) coupled with simulation tools (SDU), advanced decision making (ICE) and task planning (CERTH) skills will facilitate fast and greener shotcrete automation. RobetArme will be evaluated (DS4) on four diverse construction sites, i.e. tunnels/culverts (BYCN), bridges posttensioned boxes (ARUP), beams & piles of buildings (CEAS) and ground support walls (BYCN), assessing its autonomous shotcreting abilities. RobetArme will substantially increase the repair/maintenance automation, will foster adoption of robots in Construction 4.0 era (EFF), while it will increase construction productivity providing flexibility to the maintenance personnel, contribution to standards (UNI) and better quality to their workplaces (MORE).

Horizon project 101070118 ΝΕΜΟ (Next Generation Meta OS) builds an IoT-Edge-Cloud continuum, in the form of an open-source, flexible, adaptable, and multi-technology meta-Operating System. NEMO aims to unleash the power of Artificial Intelligence IoT to increase European autonomy in data processing and lower CO2 footprint. Leveraging on consortium partners technological excellence, along with clear business and exploitation strategies, CyberNEMO builds on top of NEMO to add end-to-end cybersecurity and trust on IoT-Edge-Cloud-Data Computing Continuum. CyberNEMO will establish itself as a paradigm-shift to support resilience, risk preparedness, awareness, detection and mitigation within Critical Infrastructures deployments and across supply chains. To achieve technology maturity and massive adoption, CyberNEMO will not “reinvent the wheel”, but leverage on existing by-design, by-innovation, and by-collaboration zero-trust cybersecurity and privacy protection systems, and introduce novel concepts, methods, tools, testing facilities and engagement campaigns to go beyond today’s state of the art and create sustainable innovation, already evident within the project lifetime. CyberNEMO will offer end-to-end and full stack protection, ranging from a low level Zero-Trust Network Access layer up to a human AI explainable Situation Perception, Comprehension & Protection (SPCP) framework and tools, collaborative micro-cervices Auditing, Certification & Accreditation and a pan-European Knowledge Sharing, risk Assessment, threat Analysis and incidents Mitigation (SAAM) collaborative platform. Validation and penetration testing will take place in 6 pilots including OneLab for integration, various Critical Infrastructures (Energy, Water, Healthcare), media distribution, agrifood and fintech supply chain, along with their cross-domain, cross-border federation. Sustainability and adoption will be offered via the de-facto European Open source Eclipse Foundation ecosystem.

Mobility in the urban and maritime domains hugely impacts the global economy, generating data at high rates from an increasing number of moving objects. Management of the complete lifecycle of such data implies that trustworthy and privacy-preserving infrastructures need to be put in place, so that reliable and secure data operations can be provided. Meanwhile, the mobility data exploitation has still a wide potential due to the emerging applications and the environmental footprint caused by mobility (e.g., carbon emissions, energy consumption). Motivated by these pressing needs, MobiSpaces delivers an end-to-end mobility-aware and mobility-optimized data governance platform with key differentiating factor that the outcomes of mobility analytics will be utilized to optimize the complete data path, in terms of efficient, reliable, secure, fair and trustworthy data processing. MobiSpaces promises the extraction of actionable insights from ubiquitous mobile sensor data and IoT devices in a decentralized way, offering intelligent transportation services, enforcing privacy constraints at the expected point of action. XAI techniques will be applied at the level of data management and machine learning, supporting the creation of comprehensive and interpretable prediction models, while all the research outcomes will be validated through five use cases: 1) intelligent public transportation services in urban environments, 2) intelligent infrastructure traffic sensing for smart cities, 3) vessel tracking for non-cooperative vessels, 4) decentralized processing on-board of vessels, and 5) enhanced nautical maps via crowdsourced bathymetry vessels data. These actions will be continuously adapted and monitored for their environmental sustainability, whereas MobiSpaces will create a widely accepted standard of data processing and analytics, alongside a data-rich ecosystem providing trustworthy and actionable data that is vital for enabling the growth of the EU digital economy.