Recently, the research community has recently shown overwhelming interest in metaverse-enabled wireless devices, due to their compelling proactive learning and selfsustainability attributes. Proactive learning enables machine learning models to be trained before user requests, while selfsustainability allows a system to function with the least amount of assistance from network administrators/users. Because of these features, one can use metaverse to enable various applications (e.g., entertainment and collision avoidance) in intelligent transportation systems. However, the limitations of computing processing power (e.g., in autonomous cars) and communication resources make implementing metaverse-empowered vehicular networks challenging. Motivated by these facts, we present a new framework for cooperative sensing, communication, learning, and task offloading for vehicular networks enabled by the metaverse. Subsequently, we formulate a cost-function minimization problem that accounts for transmission energy and transmission latency. The cost is minimized by optimizing task offloading, wireless resource distribution, transmit power allocation, and sensing interval. We employ a decomposition-based strategy for simultaneous resource allocation, task offloading, sensing interval optimization, and transmit power allocation. Due to the combinatorial nature of the resource allocation and task offloading problems, matching-based solutions are used. For sensing interval optimization, convex optimization is used. On the other hand, due to the non-convex and continuous nature of the transmit power allocation problem, a proximal term is introduced into the objective function to approximate it as convex objective function, which is then solved using a convex optimizer. To gain further insights, the proposed scheme is supported by extensive numerical results.

Recently, metaverse-empowered wireless systems have gained significant interest in the research community because of the appealing features of self-sustainability and proactive learning. Self-sustainability allows a system to run with the least amount of assistance from network administrators, whereas proactive learning allows for the development of machine learning models prior to user requests. As a result, the idea of the metaverse in vehicular networks can be used to enable a variety of applications (e.g., infotainment and collision avoidance) for a massive number of autonomous vehicles. However, metaverseempowered vehicular networks are challenging to implement due to computing (i.e., at autonomous cars and network edges) and communication resource constraints. We present a novel framework for joint sensing, communication, and task offloading for vehicular networks empowered by the metaverse in order to address these issues. We formulate a problem to minimize a cost function that takes into account transmission latency, sensing, and transmission energy. Sensing interval, resource distribution, and task offloading are all optimized for minimizing the cost. We use convex optimization for the sensing problem while a decomposition-relaxation-based approach is used for joint resource allocation and task offloading. Finally, numerical results are provided to support the proposed scheme.

Recent trends in emerging applications have motivated researchers to design advanced wireless systems to meet their evolving requirements. These emerging applications include digital healthcare, intelligent transportation systems, Industry 5.0, and more. To address the evolving requirements, leveraging a metaverse to empower $6$G wireless systems is a viable solution. A metaverse-empowered $6$G wireless system can offer numerous benefits, but it may also be vulnerable to a wide variety of security attacks. In this survey, we discuss potential security attacks in metaverse-empowered $6$G wireless systems. We introduce an architecture designed to enhance security within metaverse-empowered $6$G wireless systems. This architecture comprises two key spaces: the meta space and the physical space. We present physical space attacks and outline effective solutions to secure metaverse-empowered $6$G wireless systems. We provide invaluable insights and discussions on meta space attacks, along with promising solutions. Finally, we discuss open challenges and provide future recommendations.

In contrast to methods relying on a centralized training, emerging Internet of Things (IoT) applications can employ federated learning (FL) to train a variety of models for performance improvement and improved privacy preservation. FL calls for the distributed training of local models at end-devices, which uses a lot of processing power (i.e., CPU cycles/sec). Most end-devices have computing power limitations, such as IoT temperature sensors. One solution for this problem is split FL. However, split FL has its problems including a single point of failure, issues with fairness, and a poor convergence rate. We provide a novel framework, called hierarchical split FL (HSFL), to overcome these issues. On grouping, our HSFL framework is built. Partial models are constructed within each group at the devices, with the remaining work done at the edge servers. Each group then performs local aggregation at the edge following the computation of local models. End devices are given access to such an edge aggregated model so they can update their models. For each group, a unique edge aggregated HSFL model is produced by this procedure after a set number of rounds. Shared among edge servers, these edge aggregated HSFL models are then aggregated to produce a global model. Additionally, we propose an optimization problem that takes into account the RLA of devices, transmission latency, transmission energy, and edge servers’ compute latency in order to reduce the cost of HSFL. The formulated problem is a mixed-integer non-linear programming (MINLP) problem and cannot be solved easily. To tackle this challenge, we perform decomposition of the formulated problem to yield sub-problems. These sub-problems are edge computing resource allocation problem and joint relative local accuracy (RLA) minimization, wireless resource allocation, task offloading, and transmit power allocation sub-problem. Due to the convex nature of edge computing, resource allocation is done so utilizing a convex optimizer, as opposed to a block successive upper-bound minimization (BSUM) based approach for joint relative local accuracy (RLA) minimization, resource allocation, job offloading, and transmit power allocation. Finally, we present the performance evaluation findings for the proposed HSFL scheme.

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.