The rise of real-time networking demands has driven the IEEE Time-Sensitive Networking (TSN) task group to develop new standards that ensure high bandwidth and lowlatency Ethernet communication. TSN is an essential component of next-generation 6G networks. It offers features that ensure deterministic data transmission and alleviate network congestion. These features are crucial in time-sensitive applications and systems, whereby both precision and reliability are paramount. While early TSN implementations relied heavily on synchronous communication, newer standards, such as IEEE 802.1Qcr, have introduced asynchronous mechanisms via Urgency-Based Scheduler (UBS). UBS employs advanced queuing and traffic shaping strategies, to guarantee minimal delay for real-time applications. Within the scope of 6G, this study evaluates the queuing and shaping strategies applied to multiple flows within the UBS framework. Moreover, we assess their impact on frame transmission rates at the shaper level, highlighting the optimal use case for each strategy.

The Internet of Things (IoT) is rapidly expanding globally, but the limited size of IoT devices restricts their battery capacity, computational resources, and wireless bandwidth, making it difficult to handle resource-intensive tasks. Edge Computing addresses these challenges by enabling task offloading to more capable edge servers. However, optimal task offloading in Edge-IoT networks is complex due to dynamic conditions, such as varying server loads and wireless fluctuations. Traditional and some machine learning-based offloading methods often fall short in adaptability or efficiency. This paper introduces a bi-level optimization approach using Deep Reinforcement Learning (DRL) agents for IoT-level offloading and a priority-aware greedy heuristic for resource allocation on edge servers. The proposed method effectively improves QoS by balancing task execution latency and power consumption, as demonstrated by simulation results.

Recent advancements in serverless computing and the cloud-edge continuum have increased interest in WebAssembly (WASM). This technology enables portability and interoperability across diverse computing environments while achieving near-native execution speeds. Currently, WASM supports Single Instruction Multiple Data (SIMD), which allows for data-level parallelism that is particularly beneficial for vectorizable operations such as general matrix-matrix multiplication (GEMM) and convolutional layers. However, WASM lacks native integration with specialized hardware accelerators like GPUs, TPUs, and NPUs, as well as the ability to benefit from multi-core processing capabilities, which are critical for efficiently running Deep-Learning (DL) workloads. In contrast, despite these gains, WASM still lacks native support for heterogeneous accelerators such as GPUs, TPUs, and NPUs, as well as full multi-core parallelism capabilities that are critical for meeting the latency and throughput requirements of modern DL inference services. To bridge this gap, WASI-NN was developed, enabling WASM to integrate with external runtimes such as OpenVINO and ONNX Runtime, which leverage hardware acceleration. However, these current integrations often introduce performance overhead on certain devices, restricting their usability across the CECC. To address these challenges, we propose a new integration focusing on TVM as an external runtime for WASI-NN to enhance WASM’s performance and expand support to a broader range of devices. Additionally, we integrate this solution into Knative, a serverless framework, to provide a scalable and flexible platform for DL deployment. Using WASM technology, we evaluate our TVM-based solution through comparative studies. Results on AMD CPUs demonstrate the effectiveness of our approach, achieving 58% overall gain over other WASI-NN integrations (e.g., ONNX Runtime and OpenVINO) for CNN-based models while also achieving optimal performance on different platforms, such as Intel GPUs. These findings highlight the effectiveness of our solution.

The emergence of the Beyond Visual Line of Sight (BVLOS) operations for Unmanned Aerial Vehicles (UAVs) unlocked a wide range of new applications across various domains, such as urban transportation, package delivery, and aerial surveillance. However, due to the possibility of losing control and collisions, BVLOS operations present several risks to people on the ground. Therefore, it is crucial to minimize safety risks by flying UAVs along paths that traverse less populated areas. Nevertheless, implementing such a solution requires access to real-time data on the population density distribution across UAV operational areas. Consequently, in this paper, we harness the network exposure capabilities of 5G mobile networks, proposing a framework that integrates the UAV Traffic Management (UTM) system with the 5G Core (5GC). The proposed framework can collect real-time information about the density of mobile users in Areas of Interest (AoI), leveraging this data to estimate ground risks and subsequently devise optimized flight paths. Moreover, we propose a Deep Reinforcement Learning (DRL) solution to compute optimized flight paths. The simulation results show the efficiency of our proposed solution to achieve the designed goals in terms of reducing the experienced ground risk and total flight distance.

Software-Defined Networking (SDN) has emerged as a transformative technology that revolutionizes network management and architecture by providing unparalleled flexibility and control over data traffic flows. This flexibility is increasingly crucial in managing the complex demands of modern networks, whereby efficient traffic management is essential for mitigating congestion and enhancing operational efficiency. This paper introduces a novel traffic management model that employs Deep Reinforcement Learning (DRL) to transcend the conventional limitations typically associated with routing strategies that prioritize the shortest path or make non-optimal decisions when forwarding the traffic between different peers. Our model not only reduces overall network congestion but also aims to minimize bandwidth usage and enhance routing mechanisms within SDN environments. By incorporating DRL-based load balancing mechanisms, the model intelligently redistributes traffic across multiple pathways, shifting the focus from proximity to efficiency. This strategic redistribution prioritizes routes that optimize both, transmission time and network performance, rather than merely the shortest path. Moreover, the integration of DRL allows for real-time decision-making, enabling our system to dynamically adapt to changing traffic conditions and user demands. This capability is instrumental in significantly reducing transmission times and improving the overall efficiency of traffic flow across the network. Our findings highlight the substantial benefits of integrating SDN with advanced DRL techniques, offering a pioneering perspective on traffic routing within SDN networks. We evaluated the proposed framework via simulations and the obtained results demonstrated the efficiency of our solution compared to the baseline approaches.

Multi-Access Edge Computing (MEC) is considered as one of the key enablers of Unmanned Aerial Vehicles (UAVs) use cases. However, the envisioned MEC deployments introduce new challenges related to the management of the mobility of services across the distributed MEC hosts, following the UAVs movements and possible handovers to ensure sustainable Quality-of-Service (QoS). A major challenge for MEC service mobility is the decision-making on where and when to relocate services. In this paper, we motivate the use of the predefined flight plans of UAVs for devising proactive relocation strategies that can deal efficiently with realistic asynchronous relocation processes. Moreover, we formulate the Proactive Service Relocation for UAV (PSRU) problem using linear programming, and we validate the gains introduced by the proactive relocation strategy and the use of the predefined flight plans of UAVs.

The increased use of Unmanned Aerial Vehicles (UAVs) in numerous domains, will result in high traffic densities in the low-altitude airspace. Consequently, UAVs Traffic Management (UTM) systems that allow the integration of UAVs in the low-altitude airspace are gaining a lot of momentum. Furthermore, the 5 h generation of mobile networks (5G) will most likely provide the underlying support for UTM systems by providing connectivity to UAVs, enabling the control, tracking and communication with remote applications and services. However, UAVs may need to communicate with services with different communication Quality of Service (QoS) requirements, ranging form best-effort services to Ultra-Reliable Low-Latency Communications (URLLC) services. Indeed, 5G can ensure efficient Quality of Service (QoS) enhancements using new technologies, such as network slicing and Multi-access Edge Computing (MEC). In this context, Network Functions Virtualization (NFV) is considered as one of the pillars of 5G systems, by providing a QoS-aware Management and Orchestration (MANO) of softwarized services across cloud and MEC platforms. The MANO process of UAV's services can be enhanced further using the information provided by the UTM system, such as the UAVs' flight plans. In this paper, we propose an extended framework for the management and orchestration of UAVs' services in MECNFV environment by combining the functionalities provided by the MEC-NFV management and orchestration framework with the functionalities of a UTM system. Moreover, we propose an Integer Linear Programming (ILP) model of the placement scheme of our framework and we evaluate its performances. The obtained results demonstrate the effectiveness of the proposed solutions in achieving its design goals.

Unmanned Aerial Vehicles (UAVs) can offer a plethora of applications, provided that the appropriate ground control and complementary computing and storage services are available in close proximity. To accomplish this, edge cloud platforms, deployed at or close to the base stations, are essential. However, current UAV travel planning does not take into account the resource constraints of such edge cloud platforms. This paper introduces an aligned process for UAV flight planning and networking resource allocation, minimizing the total traveled distance. It proposes two solutions, namely (i) a Multi-access Edge Computing (MEC)-Aware UAVs' Path planning (MAUP) based on integer linear programming and (ii) an Accelerated MAUP (AMAUP), i.e., a heuristic and scalable approach that adopts the shortest weighted path algorithm considering directed graphs. The performance of the two solutions are evaluated using computer-based simulations and the obtained results demonstrate the effectiveness of the two solutions in achieving their design goals.

Given the continuously increasing use of Unmanned Aerial Vehicles (UAVs) in different domains, their management in the uncontrolled airspace has become a necessity. This has given rise to new systems called UAVs Traffic Management (UTM) systems. Nevertheless, currently, there is a lack of communication infrastructures that can support the requirements of UTM systems. Luckily, the envisioned 5G mobile network has introduced the concept of Multi-access Edge Computing (MEC) in its architecture to support mission-critical applications by decreasing the end-to-end latency and the unreliability of communication. In this paper, we evaluate the impact of the network latency and reliability on the control of UAVs' flights. The obtained results show that a UAV can deviate from its intended path with more than 5m if the network latency exceeds 400ms and with more than 2m if the packet loss probability exceeds 0.2. To overcome these limitations, we have leveraged MEC to provide a new UTM framework that enables an efficient traffic management. Moreover, due to MEC resource-limited nature and in order to give an insight about the resource provisioning, we have evaluated the scalability of the proposed solution in terms of the number of UAVs that can be handled without affecting the efficiency of the proposed UTM framework.