The project aerOS aims at transparently utilising the resources on the edge-to-cloud computing continuum for enabling applications in an effective manner, incorporating multiple services deployed on such a path. Therefore, aerOS will establish the missing piece: a common meta operating system that follows a collaborative IoT-edge–cloud architecture supporting flexible deployments (e.g., federated or hierarchical), bringing tremendous benefits as it enables the distribution of intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints. The overarching goal of aerOS is to design and build a virtualized, platform-agnostic meta operating system for the IoT edge-cloud continuum. As a solution, to be executed on any Infrastructure Element within the IoT edge-cloud continuum – hence, independent from underlying hardware and operating system(s) – aerOS will: (i) deliver common virtualized services to enable orchestration, virtual communication (network-related programmable functions), and efficient support for frugal, explainable AI and creation of distributed data-driven applications; (ii) expose an API to be available anywhere and anytime (location-time independent), flexible, resilient and platform-agnostic; and (iii) include a set of infrastructural services and features addressing cybersecurity, trustworthiness and manageability. aerOS will: (a) use context-awareness to distribute software task (application) execution requests; (b) support intelligence as close to the events as possible; (c) support execution of services using “abstract resources” (e.g., virtual machines, containers) connected through a smart network infrastructure; (d) allocate and orchestrate abstract resources, responsible for executing service chain(s) and (e) support for scalable data autonomy.

HEDGE-IoT proposes a novel Digital Framework which aims to deploy IoT assets at different levels of the energy system (from behind-the-meter, up to the TSO level), to add intelligence to the edge and cloud layers through advanced AI/ML tools and to bridge the cloud/edge continuum introducing federated applications governed by advanced computational orchestration solutions. The HEDGE-IoT Framework will upgrade the RES-hosting capacity of the energy systems and will unleash a previously untapped flexibility potential. It will increase the resilience of the grid, create new market opportunities and promote advances in IoT standardization, by introducing and managing a plethora of diversified, interoperable energy services over scalable and highly distributed data platforms and infrastructure. The multi-dimensional framework of HEDGE-IoT comprises the following pillars: (a) the Technology Facilitator Pillar will exploit the computational sharing by offloading applications on the grid edge, towards providing a set AI/ML federated learning and swarm computing applications; (b) the Interoperability Pillar, which leverages on leading-edge interoperable architectures, such as the Data Space architectures; (c) the Standardisation Pillar will enable all involved platforms, systems, tools and actors to seamlessly communicate and exchange data in standardized formats using widely used standards, such as SAREF, etc.; (d) the Digital Energy Ecosystem Enabling Pillar will ensure the creation of an ecosystem facilitating the increased integration of RES and characterised by resilience. Liaisons with EU initiatives for IoT and digitalisation will be established (e.g., the AIOTI) and the engagement of stakeholders will be ensured by addressing IoT ethics and cultivating trust among end-users, thus promoting inclusivity. Scalability and replicability studies will be performed and connections with innovators and SMEs will be established through the Open Call mechanism of the project.

AI-empowered Personalized Medicine promises to find tailored, targeted, nearly “hand-made” cures for patients. Cancer treatment desperately needs boosters to find tailored, targeted cures for patients and Personalized Medicine can play a crucial role. Tailored targeted therapies in cancer treatment are already a reality but the current practice of targeted therapies in cancer treatment has been derived with traditional methods of data analysis. AI-empowered Personalized Medicine may help to bring targeted therapies to the next level. However, no matter how precise it is, no matter how many lives it can save in principle, and no matter if it can utilize the entire medical knowledge. If clinicians do not understand its suggestions and decisions, AI-empowered Personalized Medicine will not be a game changer, clinicians will not use it to make everyday decisions and, thus, it is doomed to fail. Hence, the real challenge is building AI-empowered Personalized Medicine systems that can be accepted by clinicians and clinical researchers. In KATY, we grasp the above challenge and we propose an AI-empowered Personalized Medicine system that can bring medical “AI-empowered knowledge” to the tips of the fingers of clinicians and clinical researchers. The AI-empowered knowledge is a human interpretable knowledge that clinicians and clinical researchers can: understand, trust and effectively use in their everyday working routine. KATY is then a AI-empowered Personalized Medicine system built around two main components: A Distributed Knowledge Graph and A pool of eXplainable Artificial Intelligence predictors. As a stress test and due to the lack of personalized clinical responses, KATY will be experimented in a low prevalence and complex cancer: Clear cell renal cell carcinoma (ccRCC).

The 2021 EPBD recast highlights buildings' central role in the energy transition towards an integrated and renewable energy-based EU energy system. Smarter buildings will support the digitalisation and decarbonisation transition to the benefit of building occupants, owners and the energy system. Improving energy performance and reducing energy demands are even more relevant in the context of the REPowerEU Plan to rapidly reduce dependence on Russian fossil fuels and accelerate the green transition. BuildON seeks to develop a highly replicable, generic solution to deliver intelligent building services, facilitate the integration of heterogeneous systems and technologies, and help Build the next generatiON of Smart buildings. The main objective of BuildON is to develop and demonstrate a Smart Transformer Toolbox at TRL8 to plan and achieve improved energy performance applicable to the broadest range of building typologies and smart readiness levels via a comprehensive demonstration campaign involving five representative real-life use cases. This toolbox will offer affordable, adaptative, close-to-market and easy-to-install SRI-aligned services to continuously Monitor, Assess, Predict and Optimise (MAPO services) a building’s performance. A unified representation of the building, exposed through a Universal Building API, will abstract all field-level communication complexity, allowing the energy devices’ homogeneous real-time monitoring and control. Moreover, the generation of (Maturity Level 3 and 4) Digital Twins will allow simulation and control of a building and its systems. User-friendly decision support and user empowerment tools for facility managers and occupants will be part of the toolbox to explore, understand and expand their buildings' smart capabilities. By providing a unified approach to interacting with buildings, the floor is offered to develop a building app store that significantly accelerates the Smart Transformation of existing and new buildings

The massive increase in device connectivity and generated data has resulted in the proliferation of intelligent processing services to create insights and exploit data in a multi-modal manner. Currently, the most powerful data processing operates in a centralized manner at the cloud, which provides the ability to scale and allocate resources on demand and efficiently. Centralized processing and cloud hosting, bound and limit their services and applications to operate in a resource restricted manner, relying usually on large single entities to provide, i) Authentication, ii) Data storage, iii) Data processing, iv) Connectivity, v) Vendor-locked environments for development and orchestration. This significantly limits the user from its data governance and even identity management. Similarly, existing solutions for edge device authentication require a centralized entity to trust them and authenticate them, rendering a non-portable identification paradigm. OASEES aims to create an open, decentralized, intelligent, programmable edge framework for Swarm architectures and applications, leveraging the Decentralized Autonomous Organization (DAO) paradigm and integrating Human-in-the-Loop (HITL) processes for efficient decision making. The OASEES vision is to provide the open tools and secure environments for swarm programming and orchestration for numerous fields, in a completely decentralized manner. An important aspect in this process is identification and identity management, in which OASEES targets the implementation of a portable and privacy preserving ID federation system, for edge devices and services, with full compliance and compatibility to GAIA-X federation and IDSA trust directives and specifications. This situation solidifies the need for an integrated enabler framework tailored to the edge’s extreme data processing demands, using different edge accelerators, i.e. GPU, NPU, SNN and Quantum.