The main goal of TraDE-Opt is the education of 15 experts in optimization for data science, with a solid multidisciplinary background, able to advance the state-of-the-art. This field is fast-developing and its reach on our life is growing both in pervasiveness and impact. The central task in data science is to extract meaningful information from huge amounts of collected observations. Optimization appears as the cornerstone of most of the theoretical and algorithmic methods employed in this area. Indeed, recent results in optimization, but also in related areas such as functional analysis, machine learning, statistics, linear algebra, signal processing, systems and control theory, graph theory, data mining, etc. already provide powerful tools for exploring the mathematical properties of the proposed models and devising effective algorithms. Despite these advances, the nature of the data to be analyzed, that are “big”, heterogeneous, uncertain, or partially observed, still poses challenges and opportunities to modern optimization. The key aspect of the TraDE-Opt research is the exploitation of structure, in the data, in the model, or in the computational platform, to derive new and more efficient algorithms with guarantees on their computational performance, based on decomposition and incremental/stochastic strategies, allowing parallel and distributed implementations. Advances in these directions will determine impressive scalability benefits to the class of the considered optimization methods, that will allow the solution of real world problems. To achieve this goal, we will offer an innovative training program, giving a solid technical background combined with employability skills: management, fund raising, communication, and career planning skills. Integrated training of the fellows takes place at the host institute and by secondments, workshops, and schools. As a result, TraDE-Opt fellows will be prepared for outstanding careers in academia or industry.

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.

INTER-IoT project is aiming at the design, implementation and experimentation of an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous Internet of Things (IoT) platforms. The proposal will allow developing effectively and efficiently smart IoT applications, atop different heterogeneous IoT platforms, spanning single and/or multiple application domains. The overall goal of the INTER-IoT project is to provide a interoperable framework architecture for seamless integration of different IoT architectures present in different application domains. Interoperability will be provided at different levels: device, network, middleware, services and data. The two application domains and use cases addressed in the project and in which the IoT framework will be applied are m-health and port transportation and logistics. The project outcome may optimize different operations (e.g. increasing efficiency in transportation time; reducing CO2 emission in a port environment; improving access control and safety; improving remote patient attendance and increase the number of subject that surgery units can assist using the mobile devices with the same resources; reducing time spent in hospitals premises or reduce the time dedicated to the assistance activities carried out directly at the surgery with advantage for subjects in charge and also benefits those waiting, i.e. reduction of the waiting list) in the two addresses domains, but it may be extended to other application domains in which there is a need to interconnect different IoT architectures already deployed. The project may deal with interoperability at different layers.

Increasingly challenging global and environmental requirements have resulted in agricultural systems coming under increasing pressure to enhance their resilience capabilities in order to respond to the abrupt changes in resource quality, quantity and availability, especially during unexpected environmental circumstances, such as uncertain weather, pests and diseases, volatile market conditions and commodity prices. Therefore, integrated solutions are necessary to support the whole food agricultural life-cycle value chain. Solutions necessarily must consider the products’ cycle, as well as each of the value chain stages. Thus, managing risks and the uncertain availability of information will lead farmers to take advantage of these managerial, technical and social based-solutions. This implies the need for innovative technology-based knowledge management system to capture the agricultural information, at a variety of regional locations, in terms of collecting, storing, processing, and disseminating information about uncertain environmental conditions that affect agricultural decision-making production systems. Hence, from the genetic design of the seed, till their planting and harvest processes, RUCAPS provides knowledge of the full agricultural life-cycle based-decision making process to realise the key impacts of every stage of the agriculture-related processes. Therefore, RUCAPS implies the development of a high impact research project in order to integrate real-life based agriculture requirements, alternative land management scenarios, unexpected weather and environmental conditions as well as supporting innovation in the development of agriculture production systems, operations, logistics and supply chain management and the impact of these systems and processes over the end-users and customers. This is to be conceived through the integration of standard and customised solutions for facilitating the collaborative engagement within the agriculture value chain.

ASSIST-IoT will provide an innovative reference architecture, envisioned as a decentralized ecosystem, where intelligence is distributed among nodes by implementing AI/ML close to data generation and actuation, and hyperconnecting nodes, in the edge-cloud continuum, over softwarized smart network. Smart network will be realised by means of virtualized functions, with clear separation of control and data planes, facilitating efficient infrastructure programmability. Moreover, the action will follow a DevSecOps methodology to ensure the integration of security, privacy, and trust, by design, in all aspects of the envisioned ecosystems. ASSIST-IoT will be supported by several pillars: (i) innovative IoT architecture, to adapt to the NGI paradigm, with three-dimensional approach, including intelligence, security and privacy by design, supporting decentralized collaborative decision-making; (ii) moving from semantic interoperability to semantically-enabled cross-platform, cross-domain data transactions, within decentralized governance, DLT-anchoring transaction security, privacy and trust; (iii) development and integration of innovative devices, supporting context-aware computing, to enable effective decision making close to events; (iv) introduction of self-* mechanisms, supporting self-awareness and (semi-)autonomous behaviours across IoT deployments, and (v) Tactile Internet support for latency applications, like AR/VR/MR, and human-centric interaction with IoT components. Results of the action will provide foundation for a comprehensive practice-based methodology, for future designers and implementers of smart IoT ecosystems. Finally, to validate research results, and developed solutions, and to ensure their wide applicability, extended pilot deployments with strong end-user participation will take place in: (i) port automation; (ii) smart safety of workers, and (iii) cohesive vehicle monitoring and diagnostics, bringing about domain-agnostic aspect of the approach.