The AHEAD (AI-informed Holistic Electric Vehicles Integration Approaches for Distribution Grids) project will create a simulation environment capable of predicting the most convenient location to place the electric vehicle (EV) charging stations and optimise both the usage of the power grid resources, and the charging stations located in urban and rural areas. This simulation environment will exploit the unique features of currently available AI models and include two layers: the spatial mapping one (placing the chargers where the people need them to be), and the power grid one (placing the chargers where the grid can support them). Innovative smart charging algorithms will be designed and tested in the model, to minimise the impact of EV charging pools on the network, and ensure the consumers have economic benefits. Moreover, these smart charging algorithms will be tested in three demonstration sites, dedicated to assessing the technical and economic feasibility of smart charging light and heavy-duty EVs, and boats. To this end, AHEAD gathered relevant partners from all the EV value-chain: technology providers who want to test their equipment in the real world, grid operators, who want to optimise the usage of the grid resources and mitigate the EV charging impact, and research institutions, who aim at advancing the knowledge on the topic and producing value for society. Particular attention is going to be placed on the user experience and cybersecurity part of the demonstrators, with specific partners who focus their efforts on understanding how to minimize the impact of smart charging on the user experience and on creating a model to represent cyber-attacks on the chargers to suggest efficient defensive mechanisms for system protection.

MLSysOps will achieve substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. MLSysOps will design, implement and evaluate a complete framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Adaptivity will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. Energy efficiency, performance, low latency, efficient, resilient and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust and security, are key elements of MLSysOps addressed using ML models. The framework architecture disassociates management from control and seamlessly interfaces with popular control frameworks for different layers of the continuum. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.

TANGO will establish a stronger cross-sector data sharing, in a citizen-centric, secure and trustworthy manner, by developing innovative solutions while addressing environmental degradation and climate change challenges. The overall outcome is a novel platform exhibiting the following capabilities: user-friendly, secure, trustworthy, compliant, fair, transparent, accountable and environmentally sustainable data management, having at its core technology components for distributed, privacy preserving and environmentally sustainable data collection, processing, analysis, sharing and storage. This platform will promote trustworthy and digitally enabled interactions across society, for people as well as for businesses. TANGO will leverage the power of emerging digital technologies to strengthen the privacy for citizens and private/public organisations, reduce costs and improve productivity. It will unlock the innovation potential of digital technologies for decentralised, privacy-preserving applications, while making accessible and demonstrating this potential within the GAIA-X and EOSC ecosystem. With 37 key partners from 13 countries, TANGO, is uniquely positioned to provide a high impact solution within the transport, e-commerce, finance, public administration, tourism and industrial domains supporting numerous beneficiaries across Europe. Through the provision of TANGO technologies, a trustworthy environment will be designed acting as a gatekeeper to information and data flows. Citizens and public/private organisations will be empowered to act and interact providing data both online and offline. TANGO will focus its activities on 3 main pillars: (i) the deployment of trustworthy, accountable and privacy-preserving data-sharing technologies and platforms; (ii) the creation of data governance models and frameworks; (iii) the improvement of data availability, quality and interoperability – both in domain-specific settings and across sectors.

As its surroundings changes radically and climate conditions deteriorates, Europe and its Members adapt to these current challenges. To this end and in order to maximise their usability, the EC established a framework of a common policy (EU Security Market study) by categorizing potential technologies per security domain, including Critical Infrastructure (CI) Protection. This trend is indicative for the importance and significancy that the EC gives to these matters. Any potential disruption, either intentional as a terrorist attack or a natural disaster, may risk smooth operations of such structures that may have a severe impact on a local society and its daily activities or well-being. Current advancements in various technologies can be particularly beneficial in CI protection especially when they can provide a timely support without the necessity of a human in the loop. TESTUDO, aligned with the need of a holistic and autonomous security approach in CI protection domain and with the European Commission’s objectives, will utilize advanced unmanned vehicles along with existing equipment to deliver a highly mature platform for continuous monitoring even at harsh environments and remote territories. Tailored to the needs of the domain and targeting to maximize the platform’s autonomy capabilities, TESTUDO intends to incorporate state-of-the-art technologies for detection, prevention and prediction increasing their cognitive capabilities for different types of hazardous events. In addition, optimization techniques will identify the resources needed for the execution of high-level missions contributing to the total autonomy of the deployable system. A multidisciplinary group of technical innovators for AI-based models, CBRN, cyber-security detection, Digital twins and XAI along with the CI-related experts will collaborate to deliver an innovative action and solution to protect various CIs during a long operational period and completely autonomously.