This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Metaverse has generated significant interest for enabling wireless systems due to its self-sustainability and proactive analytic capabilities. It enables the deployment of meta spaces featuring avatars and digital twins for various real-world applications. However, it has become challenging to efficiently deploy meta spaces on edge/cloud environments while managing communication and computing resources effectively. In this paper, we propose a novel network virtualization framework within a shared system to make the deployment of meta spaces for various metaverse applications cost-efficient. The framework abstracts, isolates, and facilitates the sharing of wireless and computing resources, thereby enhancing flexibility and efficiency for metaverse-driven applications. It involves three key players: network operators (selling resources), metaverse operators (managing transactions), and end-users (purchasing resources). An optimization problem is formulated to optimize the wireless resource allocation, computing resource allocation, association of end-devices with meta spaces deployed at the network operators’ base stations, communication resource cost, and computing resource cost. We address the formulated NP-Hard mixed integer non-linear programming (MINLP) problem using decomposition, convex optimization, and hierarchical matching. Numerical results confirm the validity of the proposed approach.

In this paper, we discuss a general framework, namely Opportunistic Distributed Learning (ODL), which allows any node in the network to initiate a learning task while opportunistically leveraging local, unused distributed resources to collaboratively execute this task.

In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of Medical Things (IoMT) have been used in the healthcare industry to monitor remote users’ health status, such as elderly monitoring, injuries, stress, and patients with chronic diseases. Since IoMT devices have limited resources, mobile edge computing (MEC) has been deployed in 5G networks to enable them to offload their tasks to the nearest computational servers for processing. However, when IoMTs are far from network coverage or the computational servers at the terrestrial MEC are overloaded/emergencies occur, these devices cannot access computing services, potentially risking the lives of patients. In this context, unmanned aerial vehicles (UAVs) are considered a prominent aerial connectivity solution for healthcare systems. In this paper, we propose a multi-agent federated reinforcement learning (MAFRL)-based resource allocation framework for a multi-UAV-enabled healthcare system. We formulate the computation offloading and resource allocation problems as a Markov decision process game in federated learning with multiple participants. Then, we propose a MAFRL algorithm to solve the formulated problem, minimize latency and energy consumption, and ensure the quality of service. Finally, extensive simulation results on a real-world heartbeat dataset prove that the proposed MAFRL algorithm significantly minimizes the cost, preserves privacy, and improves accuracy compared to the baseline learning algorithms.

Recent advances in telecommunication and machine learning (ML) have allowed for new smart and autonomous vehicle applications to improve road safety, environmental conditions, and traffic management through Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication. However, with the rise of advanced cyber-attacks, the authenticity of a message guarantees its source but not its correctness. To mitigate the new sophisticated attacks, new Misbehavior Detection Systems (MDS), that use machine learning algorithms to detect misbehaving vehicles, have been proposed. This work provides first a comprehensive review of recent developments in ML-based MDS technology within a Vehicular Ad-Hoc Network (VANET) context, covering data collection, feature selection, model training, model evaluation and deployment. We survey useful public datasets and summarize recent studies. We report useful pieces of information for every work. In particular, we highlight the considered dataset for ML training, list the selected ML models, indicate the feature selection and dimensionality reduction techniques, recapitulate the main results, report the performance metrics and mention the deployment guidelines when applicable. Then, we compare the surveyed studies discussing not only the strength points but also their limitations. One of the key observations from the surveyed works is the absence of a quantitative analysis of the proposed models’ execution time, which is a crucial performance metric considering the limited on-board and edge computing resources. To develop a feasible ML-based MDS for V2X communication, it is essential to address this issue and propose a deployment strategy that optimizes the allocated resources for this technology. However, achieving this remains a challenge. Moreover, in view of the fact that data generation and analysis are critical phases in this technology. Also, using simulation has many advantages over the real data collection, we provide a tutorial on how realizing a useful dataset collection with popular open-source tools while considering exemplar types of attacks. Last, we demonstrate through the tutorial the use of the collected dataset for ML-based MDS model selection and training. An open source github repository is provided for regenerating the whole explained scenarios and modify according to the given research issue.

To appear in IEEE In recent years, Federated Edge Learning has gained interest from both industry and academia for deployment at the wireless network edge. However, some resource-restricted edge devices (EDs) bear more computation and communication loads due to the heterogeneity of data and resources. Several approaches have been proposed in the literature to reduce energy costs by scheduling only a few EDs to complete training tasks based on their energy budgets. Nevertheless, from a practical perspective, the incongruent data distribution cannot be captured, resulting in a biased model for EDs that are frequently selected. Furthermore, the frequently scheduled devices deplete their energy quickly, making them inaccessible. Thus, this paper proposes a novel scheduling policy based on the historical participation of each ED that ensures an unbiased model while balancing learning tasks so that all EDs consume equivalent energy at the end of the training. We formulate an optimization problem based on Jain’s fairness index, followed by tractable algorithms to solve this problem. Extensive experiments have been conducted, and the results show that the proposed algorithm balances the energy consumption among EDs and accelerates the convergence rate while achieving satisfactory performance.

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associated with AFBS  based on the quality of the data and its demand in order to maximize learning efficiency and accuracy. This work aims to minimize the computation costs of the IoMT devices while considering UAV resources and the fairness of UAV coverage. Simulation results prove that our proposed algorithm outperforms other baseline algorithms in learning accuracy and computational cost.