The advent of advanced mobile communication systems like 5G and beyond holds promise for vertical sectors, particularly in transport & logistics (T&L), by enhancing network performance and ensuring high levels of quality of service (QoS), crucial for automating and optimizing T&L processes. However, assessing the impact of 5G requires investments in network infrastructure, posing time, and return on investment uncertainties. To address this, the European project VITAL-5G offers an open, virtualized, and flexible experimentation platform that provides 3rd party vertical stakeholders the opportunity to test and validate 5G-enabled services under realistic conditions. This paper explores the VITAL-5G platform’s capabilities, detailing the infrastructure that supports 5G standalone (SA) testing across multiple trial sites, including port and logistics environments. We highlight a subset of performed activities and experimentation campaigns, technical and operational challenges faced by 3rd party experimenters, and the potential business opportunities that emerge from 5G-enhanced services in the T&L sector. Finally, we present insights into future developments and opportunities for extending these capabilities to other vertical sectors.

Purpose: To describe and validate a 3D-printed adapter tool which could be used with either a slit lamp or a condensing lens, interchangeable between devices through magnetic fastening, in order to provide physicians a quick, easy and effective method of obtaining clinical photos. Materials and Methods: Three specialists, with at least 4-year experience in ophthalmology, gave a rate of image quality obtained by our device and the diagnostic confidence grade. The 3 specialists conducted each 13 or 14 examinations with the smartphone and magnetic adapter. At the end of evaluation, they rated with the Likert scale the ease of use of the device in obtaining clinical images of the anterior segment and ocular fundus respectively. Results: Data of quality perception and confidence demonstrated high values not dissimilar to the “de visu” eye examination. Moreover the instrument we designed turned out to be very user friendly. Conclusion: Our adapter coupled with a modern smartphone was able to obtain 4k images and videos of anterior segment, central and peripheral fundus, in an easy and inexpensive way.

In the last few years, Virtual Reality (VR) is assuming a prominent role and is recognized as a pivotal technology in various sectors. However, transmission of immersive videos produced by real-time 360° cameras or stored on remote servers to reproduce 3D environments, or streamed by video games accessible through headsets, would require a lot of network bandwidth that, in many cases, is not available or too expensive to be obtained. In this paper, we leverage on network softwarization provided by new 5G&B networks, and introduce an Adaptive Closed-loop Encoding VNF named 360-ST for adaptive compression of 360° video streaming. This VNF is able to apply a hierarchical compression that takes into account both the bandwidth currently available in the network, and the user viewport. The agent that is in charge of deciding the different compression ratio at runtime uses Deep Reinforcement Learning to optimize a reward function and adapt to the changes of the network bandwidth, the end-to-end latency, the user movements within the scene and the video content. The results indicate that our proposed method consistently outperforms state-of-the-art algorithms by an average of 8% to 46% in terms of achieved Peak Signal-to-Noise Ratio (PSNR).

Providing Service Chains consisting of different virtual functions in dynamic and infrastructure-less environments, such as disaster-stricken regions or remote rural areas, requires specialized orchestration strategies. A promising approach is to leverage Unmanned Aerial Vehicles (UAVs) organized into Flying Ad-hoc Networks (FANETs) to provide Edge Computing capabilities. These UAVs act as distributed computing nodes, hosting Cloud-Native Functions (CNFs) that process and store data for ground users. However, the constraints of UAVs necessitate optimized resource allocation strategies to balance utility and performance. This paper presents a marketplace approach for Service Chain deployment in a multilayer FANET Edge Computing architecture. The proposed framework introduces multiple layers, where service requests are aggregated and processed by an Orchestration Layer that optimally distributes application traffic among CNF Providers and Resource Providers. By dynamically allocating computing resources based on demand, this architecture optimizes service performance while minimizing overall costs. The proposed solution contributes to the efficient orchestration of SCs in FANETs, supporting applications in challenging environments.

In Smart Cities (SCs), efficient traffic flow management in congested urban areas is crucial to promote sustainability, liveability, and accessibility. Big data can potentially provide valuable information to be used for mobility management; accordingly, integrating traditional traffic data collection methods to enrich the type of available data is to be pursued. In this perspective, recently the idea of using Unmanned Aerial Vehicles (UAVs) for traffic monitoring due to their flexibility, cost-efficiency, and reliability, emerged. This paper explores the potential of using UAV collected data - such as the one related to vehicle detection, tracking, and trajectory analysis - in the aim of calibrating microsimulation models for transport planning. A case study conducted in Catania, Italy, involved exploiting UAVs for deployment of various traffic detection technologies. The results highlighted the effectiveness of using UAVs, achieving high accuracy and efficiency in monitoring traffic conditions where traditional sensors often face limitations. Use of UAVs also allows continuous trajectory analysis, so enabling for example the detection of prohibited manoeuvres and identifying design strategies to improve road safety. Furthermore, the study demonstrated that a single UAV can effectively monitor large intersection areas, reducing the need for use of multiple more traditional sensors. This research lays the groundwork for a prototype that integrates diverse data sources to create harmonized traffic datasets, contributing to the development of a Digital Twin of Mobility (DTmob). The latter will be a valuable tool to improve traffic monitoring and control systems, supporting resilience, efficiency, and better service provision in SCs.