The push for improved driving experience, including the ultimate goal of driverless vehicles, has fueled the development of Connected and Autonomous Vehicles (CAVs). Connectivity is an important enabler for CAVs, as it allows for autonomous vehicles to directly participate in the intelligent transportation system (ITS) and make collective intelligent decisions. 5G mobile networks play an important role in providing vehicular connectivity as mobile networks support mobility by default. However, the density of vehicles is high in urban areas that poses challenges for the support from mobile networks. Deploying ultra-dense small cells using millimetre wave (mmWave) communication is one promising solution since ultra-dense small cells deployment addresses the network capacity and mmWave addresses the interference arisen from the dense deployment. However, due to the relatively narrow footprint of a mmWave beam, using mmWave communication for fast moving vehicles requires careful allocation to utilise its potential and avoid selfishness in radio resource usage. This project sets an ambitious goal of designing smart and dynamic algorithms to manage mmWave beam allocations in CAV environments. The project first investigates solely on the mmWave beam allocations and proposes smart algorithm using both traditional optimisation technique for benchmarking and modern machine learning algorithms for practical operation. It then studies the radio resource allocation under multi radio access technologies (multiRATs) that is the likely deployment setup before the full mmWave small cell networks become practical in the future. Finally, our study is extended to vehicle-to-vehicle communication using mmWave that can support delay sensitive message exchanges and range extension. As experimentation needs expensive infrastructure, the foreseen secondments offer partners without infrastructure on their premises to test their design on other partners that possess the infrastructure.

Air pollution is a significant global concern causing an estimated 4.2 million deaths annually due to diseases related to poor air quality. Climate change is exacerbating air quality issues and posing unprecedented challenges to the existing air quality monitoring systems, which mainly utilize sparse and expensive terrestrial stations and satellites, leading to limited accuracy and flexibility. To address these challenges, REFINE aims to form an international, multidisciplinary, and cross-sectoral consortium with world-leading researchers to create a novel real-time fine-grained air quality monitoring system empowered by advanced technologies in Unmanned Aerial Vehicles (UAVs), Artificial Intelligence (AI), and Wireless Networking. Specifically, REFINE will pioneer research and innovations (R&I) on ground-breaking technologies including: 1) a robust and scalable system architecture for aerial-terrestrial air quality monitoring; 2) intelligent and efficient multi-UAV cooperation strategies for dynamic and flexible area coverage; 3) ultra-resilient and secure aerial-terrestrial networking schemes for reliable and efficient data transmission; 4) lightweight and robust AI methods for accurate and real-time air quality analysis. REFINE will establish a long-term cross-disciplinary and cross-sectoral knowledge-sharing platform with competent and complementary expertise in Computer Science, Environmental Science, and Communication Engineering. The researchers involved will be trained through substantial R&I actions and well-planned networking activities at both European and global levels to enrich their skills and enhance their career perspectives. REFINE will significantly contribute to achieving the EU’s zero-pollution ambition and enhancing European competitiveness, through transforming the current air quality monitoring systems into a new generation, which is able to provide real-time intelligent monitoring of vast rural areas with higher precision and efficiency.

In recent years, the digital environment and digital transformation of enterprises of all sizes have made AI-based solutions vital to mission-critical. AI-based systems are used in every technical field, including smart cities, self-driving cars, autonomous ships, 5G/6G, and next-generation intrusion detection systems. The industry's significant exploitation of AI systems exposes early adopters to undiscovered vulnerabilities such as data corruption, model theft, and adversarial samples because of their lack of tactical and strategic capabilities to defend, identify, and respond to attacks on their AI-based systems. Adversaries have created a new attack surface to exploit AI-system vulnerabilities, targeting Machine Learning (ML) and Deep Learning (DL) systems to impair their functionality and performance. Adversarial AI is a new threat that might have serious effects in crucial areas like finance and healthcare, where AI is widely used. AIAS project aims to perform in-depth research on adversarial AI to design and develop an innovative AI-based security platform for the protection of AI systems and AI-based operations of organisations, relying on Adversarial AI defence methods (e.g., adversarial training, adversarial AI attack detection), deception mechanisms (e.g., high-interaction honeypots, digital twins, virtual personas) as well as on explainable AI solutions (XAI) that empower security teams to materialise the concept of “AI for Cybersecurity” (i.e., AI/ML-based tools to enhance the detection performance, defence and respond to attacks) and “Cybersecurity for AI” (i.e., protection of AI systems against adversarial AI attacks).

Ever since the cloud-centric service provision started becoming incapable for efficiently supporting the emerging end-user needs, compute functionality has been shifted from the cloud, closer to the edge, or delegated to the user equipment at the far-edge. The resources and computing capabilities residing at those locations have been lately considered to collectively make-up a ‘compute continuum’, albeit its unproven assurance to securely accommodate end-to-end information sharing. The continuum-deployed workloads generate traffic that steers through untrusted HW and SW infrastructure (domains) of continuously changing trust-states. CASTOR develops and evaluates technologies to enable trustworthy continuum-wide communications. It departs from the processing of user-expressed high-level requirements for a continuum service, which are turned-to combinations of security needs and network resource requirements, referred to as CASTOR policies. The policies are subsequently enforced on the continuum HW and SW infrastructure to realise an optimised, trusted communication path delivering innovation-breakthroughs to the so-far unsatisfied need: a) for distributed (composable) attestation of the continuum nodes and subsequent elevation of individual outcomes to an adaptive (to changes) continuum trust quantification; b) for the derivation of the optimal path as a joint computation of the continuum trust properties and resources; c) for continuum infrastructure vendor-agnostic trusted path establishment, seamlessly crossing different administrative domains. The CASTOR will be evaluated in operational environments of 4 use-cases whereby varying types of security/safety-critical information is shared. Project innovations will be exhaustively assessed in 3 diverse application domains utilising the carefully-designed CASTOR testbed core for each case. Our results will provide experimental evidence for the CASTOR's efficiency and feed the incomplete trust-relevant (IETF) standards.

TORNADO will develop an innovative, multifunctional and adaptive cloud robotics platform, supporting advanced navigation of an autonomous mobile robot (AMR) within complex, time-varying, real-world, human-populated indoor environments. The TORNADO AMR will be able to manipulate small, soft or deformable objects (SSDs) to an unprecedented degree of success, as well as to naturally interact with humans via hand gestures or verbal conversation, by exploiting the zero-shot generalization abilities of deep neural Foundation Models (FMs) for robotics. The AMR's intelligence will rely on a pool of pretrained cloud-hosted FMs, which shall be further adjusted on-the-fly to the current situation via Out-of-Distribution Detection, Test-Time Adaptation and Few-Shot Adaptation subsystems. These will exploit human feedback if available, but will also support autonomous and dynamic cognitive adaptation. Additionally, the TORNADO system will be able to automatically select and set-up on-the-fly the most suitable combination of FMs and non-neural robotics algorithms during deployment, depending on the current situation. In cases of failure, on-the-fly skill acquisition will be supported via integrated, novel Learning-from-Demonstration methods facilitated by an innovative Augmented Reality (AR) interface and eXplainable AI (XAI) algorithms. The adaptive TORNADO system will allow the robot to perform difficult, non-repetitive manipulation tasks on previously unseen SSDs that may change shape during handling, as well as to flexibly adjust to SSDs of different sizes during operation. Measurement of human trust to interactive robots and human behavioral modeling will aid optimal integration/acceptance of TORNADO into society. Validation will take place at TRL-5 in 3 different industrial Use-Cases: flexible small gears manipulation and deformable ply-sheets handling (gears factory), palliative patient care (hospital) and product quality sampling/waste collection (dairy processing plant).