Recently Unmanned Aerial Vehicles (UAVs) have evolved considerably towards real world applications, going beyond entertaining activities and use. With the advent of Fifth Generation (5G) cellular networks and the number of UAVs to be increased significantly, it is created the opportunity for UAVs to participate in the realisation of 5G opportunistic networks by carrying 5G Base-Stations to under-served areas, allowing the provision of bandwidth demanding services, such as Ultra High Definition (UHD) video streaming, as well as other multimedia services. Among the various improvements that will drive this evolution of UAVs, energy efficiency is considered of primary importance since will prolong the flight time and will extend the mission territory. Although this problem has been studied in the literature as an offline resource optimisation problem, the diverse conditions of a real UAV flight does not allow any of the existing offline optimisation models to be applied in real flight conditions. To this end, this paper discusses the amalgamation of UAVs and 5G cellular networks as an auspicious solution for realising energy efficiency of UAVs by offloading at the edge of the network the Flight Control System (FCS), which will allow the optimisation of the UAV energy resources by processing in real time the flight data that have been collected by onboard sensors. By exploiting the Multi-access Edge Computing (MEC) architectural feature of 5G as a technology enabler for realising this offloading, the paper presents a proof-of-concept implementation of such a 5G-enabled UAV with softwarized FCS component at the edge of the 5G network (i.e., the MEC), allowing by this way the autonomous flight of the UAV over the 5G network by following control commands mandated by the FCS that has been deployed at the MEC. This proof-of-concept 5G-enabled UAV can support the execution of real-time resource optimisation techniques, a step-forward from the currently offline-ones, enabling in the future the execution of energy-efficient and advanced missions.

As B5G systems are evolving toward 6G, their coordination increasingly relies on AI-driven automation and orchestration actions, a process that is characterized as cognition. Therefore, a 6G system, through this cognitive process, acts as an intent-handling entity that comprehends sophisticated intent semantics from the users/tenants and calculates the ideal goal state for the specific intent, organizing the necessary adaptation actions that are needed for the transition of the system into that state. However, the use of cognitive-driven AI models to coordinate the purposes of a 6G system creates new risks, as a new surface of attack is born, where the whole 6G system operation may be maliciously affected by adversarial attacks within the user-intents. Focusing on this challenge, this paper realizes a prototype cognitive coordinator for 6G trustworthiness provision and investigates its adversarial robustness for different BERT-based quantification models, which are used for realizing the 6G cognitive system.

As B5G systems are evolving towards 6G, their coordination increasingly relies on AI-driven automation and orchestration actions, which is a process that is characterized as cognition. Therefore, a 6G system through this cognitive process, acts as an in-tent-handling entity that comprehends sophisticated intent semantics from the us-ers/tenants and calculates the ideal goal state for the specific intent, organizing the neces-sary adaptation actions that are needed for the transition of the system into that state. However, the use of cognitive-driven AI models for coordinating purposes of a 6G system creates new risks, since a new surface of attack is born, where the whole 6G system opera-tion may be maliciously affected by adversarial attacks within the user-intents. Focusing on this challenge, this paper realizes a protype cognitive coordinator for 6G trustworthi-ness provision and investigates its adversarial robustness for different BERT-based quan-tification models, which are used for realizing the 6G cognitive system.