European farming is strongly affected by the high-impact of climate change emerging as a major threat on agriculture in Europe and across the world. Even though the digital transformation of agriculture promises to solve numerous environmental, economic and societal problems, and important steps have taken place towards this goal, agriculture sector is the least digitized of all major productive activities. By increasing digitalisation and adopting climate-smart practices in agricultural makes it is possible to produce products with ever higher efficiency and ever lower environmental impact. However, adoption of a climate-smart agriculture is not only a farmers’ concern. It is an interdisciplinary issue for agronomists, ICT experts, farmers, breeders etc. that Higher Education Institutions need to respond by developing new skills and technologies in their curricula.SmartROOT focuses on the preparation of a new Joint Master Degree program in the field of Mixed Farming Systems (MFS) by introducing user-friendly ICT tools to improve the resilience of agriculture subject to climate change. The rational is not only to prepare an international master degree program. It is also to actively involve students in the preparation process, receive their assessment and feedback on the ICT tools and material developed in terms of their operability, the user-friendly environment, the knowledge gained, the extent of international cooperation and the extent of satisfaction of their expectations. It will be a dynamic, inter-active process which ends to a Joint Master Degree program ready to run after the completion of the project.SmartROOT not only aims to prepare the future professionals in the agricultural sector, but also promotes ways for small and medium-sized farms to benefit from the new technologies by introducing and familiarising farmers to digital technologies. Farmers will gain knowledge on methodologies to foster the synergies between agricultural production, climate change mitigation and adaptation. SmartROOT expected results can be summarised in the following lines:1) Build an international Joint Master Degree (JMD) program in Mixed Farming Systems ready to run after the completion of the project2) Actively involve students to the preparation, test and assessment of a JMD prior its official launch3) Enforce students’ insight to perceive the global trends on the agriculture domain in combination to climate change mitigation4) Increase students’ and stakeholders (e.g. farmers) awareness on environment friendly agricultural trends to minimise the climate change consequences5) Bridge the needs of agriculture experts and ICT scientists and familiarise farmers with ICT technological advancements6) Develop open educational and management platforms available to be used by individual farmers and agriculture professionals7) Act as a stepping stone towards the new challenge for HEIs in Europe as described under the Erasmus+ program: to create a network in the frame of the European Universities initiative SmartROOT’s results will be achieved through the following outputs:- O1 MFS e-book - O2 Open Hub for Knowledge Exchange - O3 SmartROOT Virtual Farm Hub- O4 MFS Educational Management Platform - O5 Augmented Reality Learning Environment - O6 SmartROOT Assessment Toolkit and Curriculum preparation1.Introduce the context of CSA and Mixed Farming Systems to HEIs involved through co-design of their curricula2.Educate students to new technological solutions for climate smart agriculture such as such as smart sensors, robots, UAVs, advanced tracking systems, long-range IoT-enabled sensors, middleware and gateways for extending the size of the potential monitoring area, facilitating, thus, data collection and processing of different farming systems at the same time, enabling optimal decisions,3.Develop an Open Access Climate-Smart agriculture platform to foster, support and promote Mixed Farming Systems (MFS), by facilitating the design, the deployment, and the management of crop-livestock-forestry combinations towards sustainable, efficient, and climate-aware MFS systems.4.Develop a continuous training platform, in the national language of each partner’s country, designed for the socio-professional network and based on the content and results of the 3-year project 5.Foster a European network between academia, experienced farmers and stakeholders, to engage end-users and allow exchange of knowledge on best practices on various combinations of mixed farming systems

The smart energy ecosystem constitutes the next technological leap of the conventional electrical grid, providing multiple benefits such as increased reliability, better service quality and efficient utilization of the existing infrastructures. However, despite the fact that it brings beneficial environmental, economic and social changes, it also generates significant security and privacy challenges, as it includes a combination of heterogeneous, co-existing smart and legacy technologies. Based on this reality, the SDN-microSENSE project intends to provide a set of secure, privacy-enabled and resilient to cyberattacks tools, thus ensuring the normal operation of EPES as well as the integrity and the confidentiality of communications. In particular, adopting an SDN-based technology, SDN-microSENSE will develop a three-layer security architecture, by deploying and implementing risk assessment processes, self-healing capabilities, large-scale distributed detection and prevention mechanisms, as well as an overlay privacy protection framework. Firstly, the risk assessment framework will identify the risk level of each component of EPES, identifying the possible threats and vulnerabilities. Accordingly, in the context of self-healing, islanding schemes and energy management processes will be deployed, isolating the critical parts of the network in the case of emergency. Furthermore, collaborative intrusion detection tools will be capable of detecting and preventing possible threats and anomalies timely. Finally, the overlay privacy protection framework will focus on the privacy issues, including homomorphic encryption and anonymity processes

With the evolving threat of climate change and the consequences of industrial accidents to becoming more severe, there is an increasing need for First Responders to access reliable and agile information management systems that offer as higher Situational Awareness and better Common Operational Picture. To match with current trends, the RESPOND-A project aims at developing holistic and easy-to-use solutions for First Responders by bringing together the complementary strengths of its Investigators in 5G wireless communications, Augmented and Virtual Reality, autonomous robot and unmanned aerial vehicle coordination, intelligent wearable sensors and smart monitoring, geovisual analytics and immersive geospatial data analysis, passive and active localisation and tracking, and interactive multi-view 360o video streaming. The synergy of such cutting-edge technological advancements is likely to provide high-end and continuous flows of data, voice and video information to First Responders and their Command & Control Centres for predicting and assessing the various incidents readily and reliably, and saving lives more efficiently and effectively, while maximising the safeguarding of themselves, before, during and after disasters. To this end, RESPOND-A envisions at exercising First Responders for getting familiar with the project technological outcomes, and demonstrating their real-world performance and effectiveness in the classified training facilities of our Responder Partners under hydrometeorological, geophysical and technological disaster scenarios.

The overall aim of NANCY is to introduce a secure and intelligent architecture for the beyond the fifth generation (B5G) wireless network. Leveraging AI and blockchain, NANCY enables secure and intelligent resource management, flexible networking, and orchestration. In this direction, novel architectures, namely point-to-point (P2P) connectivity for device-to-device connectivity, mesh networking, and relay-based communications, as well as protocols for medium access, mobility management, and resource allocation will be designed. These architectures and protocols will make the most by jointly optimizing the midhaul, and fronthaul. This is expected to enable truly distributed intelligence and transform the network to a low-power computer. Likewise, by following a holistic optimization approach and leveraging the developments in blockchain, NANCY aims at supporting E2E personalized, multi-tenant and perpetual protection.

Next-generation industrial systems promise to deliver unprecedented excellence not only in terms of performance, but also explainability, trustworthiness and transparency. To achieve this new objectives, state-of-the-art concepts of artificial intelligence (AI), edge-to-cloud (E2C) computing, blockchain, and visualisation need to be de-risked and applied. Motivated by this, TALON aims at sculpturing the road towards the next Industrial revolution by developing a fully-automated AI architecture capable of bringing intelligence near the edge in a flexible, adaptable, explainable, energy and data efficient manner. TALON architecture consists of three fundamental pillars: a) an AI orchestrator that coordinates the network and service orchestrators in order to optimise the edge vs cloud relationship, while boosting reusability of datasets, algorithms and models by deciding where each one should be placed; b) a lightweight hierarchical blockchain schemes that introduce new service models and applications under a privacy and security umbrella; and c) new digital-twin empowered transfer learning and visualization approaches that enhance AI trustworthiness and transparency. It combines the benefits of AI, edge and cloud networking, as well as blockchain and DTs, optimized by means of a) new key performance indicators that translate the AI benefits into insightful metrics; b) novel theoretical framework for the characterisation of the AI; c) blockchain used to deliver personalised & perpetual protection based on security, privacy and trust mechanisms; d) AI approaches for automatically and co-optimising edge and cloud resources as well as the AI execution nodes; e) semantic AI to reduce the learning latency and enhance reusability; and f) digital twins that visualize the AI outputs and together with human-in-the-loop approaches. All the technological breakthroughs are demonstrated, validated and evaluated by means of proof-of-concept simulation and four real-world pilots.