Search

Metaverse is the concept of a fully immersive and universal virtual space for multiuser interaction, collaboration, and socializing; forming the next evolution of the Internet. Metaverse depends on the convergence of multiple broad technologies that enable eXtended Reality (XR), which is an umbrella term for technologies that lie on the reality-virtuality continuum, namely Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Compared to streaming volumetric content to a single user, the XR applications that involve multiple users who simultaneously watch the same volumetric content (e.g., VR-based online training/ education and multi-user online gaming) are much more attractive. Multi-user XR/Metaverse puts additional demand on the underlying networks, making it more difficult to offer high-quality immersive material in real-time. To cope with this, in this article, we present a comprehensive system and component design for immersive and seamless multi-user XR experiences. To satisfy the Quality of Experience/Quality of Service (QoE/ QoS) requirements and especially stream synchronization requirement in XR collaboration scenarios, we propose an AI-powered deterministic multi-user extended reality resource orchestrator (PRECISENESS) that aims to solve the multi-user XR service provisioning problem. Finally, we demonstrate the performance of our proposed solution in a single-site multi-user XR use case. The obtained results demonstrate that our solution can deliver high-quality immersive XR services.

The management of remote services, such as remote surgery, remote sensing, or remote driving, has become increasingly important, especially with the emerging 5G and Beyond 5G technologies. However, the strict network requirements of these remote services represent one of the major challenges that hinder their fast and large-scale deployment in critical infrastructures. This article addresses certain issues inherent in remote and immersive control of virtual reality (VR)-based unmanned aerial vehicles (UAVs), whereby a user remotely controls UAVs, equipped with 360° cameras, using their head-mounted devices (HMD) and their respective controllers. Remote and immersive control services, using 360° video streams, require much lower latency and higher throughput for true immersion and high service reliability. To assess and analyze these requirements, this article introduces a real-life testbed system that leverages different technologies (e.g., VR, 360° video streaming over 4G/5G, and edge computing). In the performance evaluation, different latency types are considered. They are namely: 1) glass-to-glass latency between the 360° camera of a remote UAV and the HMD display; 2) user/pilot’s reaction latency; and 3) the command/execution latency. The obtained results indicate that the responsiveness (dubbed Glass-to-Reaction-to-Execution—GRE–latency) of a pilot, using our system, to a sudden event is within an acceptable range, i.e., around 900 ms.

Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

The vital importance of securing 5G and beyond networks while meeting their stringent performance requirements has promoted the recent shift towards fully automated and smart security management. In this paper, we introduce a novel autonomic and cognitive security management framework that empowers fine-grained zero-touch security management through different levels (i.e., network functions, sub-slice, and slice) and different administrative and technological domains. We showcase the compliance of the proposed framework with the ongoing standards (e.g., ZSM, 3GPP, and NFV) and demonstrate its feasibility by advocating for potential open source solutions to implement its functional blocks in a cloud-native service-based environment.

This article provides an overview on extremely interactive and low-latency immersive services as well as the relevant industry and standardization activities. Immersive services immerse a viewer or the viewed digital objects into an environment that is either real, virtual, or a mixture of both. The applications are accordingly named virtual reality, augmented reality, extended reality, and holography applications. These applications benefit from the ongoing advances in user interfaces, computing technologies, and networking technologies. Such applications are expected to generate most of the traffic in the next generation networks, particularly 6G networks. In this article, the main relevant use cases are introduced along with their respective requirements. The article also provides insights on the relevant architectures and solutions, and highlights some research challenges and directions.

The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging and conflicting requirements of the verticals make the orchestration and management of networks complicated and challenging. Recent advances in network automation and artificial intelligence (AI) create enthusiasm from industries and academia toward applying these concepts and techniques to tackle these challenges. With these techniques, the network can be autonomously optimized and configured. This article suggests a collaborative cross-system AI that leverages diverse data from different segments involved in the end-to-end communication of a service, diverse AI techniques, and diverse network automation tools to create a self-optimized and self-orchestrated network that can adapt according to the network state. We align the proposed framework with the ongoing network standardization.

Digital Twin is a new concept that consists of creating an up-to-date virtual asset in cyberspace which mimics the original physical asset in most of its aspects, ultimately to monitor, analyze, test, and optimize the physical asset. In this article, we investigate and discuss the use of the digital twin concept of the roads as a step toward realizing the dream of smart cities. To this end, we propose the deployment of a Digital Twin Box to the roads that is composed of a 360° camera and a set of IoT devices connected to a Single Onboard Computer. The Digital Twin Box creates a digital twin of the physical road asset by constantly sending real-time data to the edge/cloud, including the 360° live stream, GPS location, and measurements of the temperature and humidity. This data will be used for realtime monitoring and other purposes by displaying the live stream via head-mounted devices or using a 360° web-based player. Additionally, we perform an object detection process to extract all possible objects from the captured stream. For some specific objects (person and vehicle), an identification module and a tracking module are employed to identify the corresponding objects and keep track of all video frames where these objects appeared. The outcome of the latter step would be of utmost importance to many other services and domains such as national security. To show the viability of the proposed solution, we have implemented and conducted real-world experiments where we focus more on the detection and recognition processes. The achieved results show the effectiveness of the proposed solution in creating a digital twin of the roads, a step forward to enable self-driving vehicles as a crucial component of smart mobility, using the Digital Twin Box.

There is an ever-increasing demand for network technologies supporting Ultra-Reliable Low Latency Communications (URLLC) services and their co-existence with best-effort traffic. By way of example, reference can be made to the emerging 5G mobile networks. In this vein, this article investigates the Software-Defined Networking (SDN) technology capabilities for providing Quality of Service (QoS) guarantees. Specifically, we present a testbed, under development, dubbed Soft-ware-Defined Queueing (SDQ). This framework leverages QoS provision functionalities of SDN. SDQ can be regarded as a framework for testing traffic engineering solutions in networks with deterministic QoS support. By using SDQ, we develop and test a specific solution that chooses the optimal queue and path for each incoming flow in order to reduce the workload imbalances in the network. For the experimental setup, we consider a generic SDN network whose bridges include three priority queues at every output port. Furthermore, we compare the aforementioned solution with a best-effort network and an SDN-enabled network with QoS support configured by default. The obtained results show that the envisioned solution outperforms the baseline one of SDN and the best-effort solution in terms of the average latency recorded.

The first generation of IoT was developed and deployed all over the world by connecting devices with common functionalities that were not sufficiently efficient or reliable for use in dynamic situations that require adaptive solutions. However, these fundamental IoT functions and services mainly targeted stable environments; there is consequently a strong need for the next generation of IoT services to be smarter, faster, and more reliable. We believe that the hyper-connected IoT ecosystem on fog platforms with contextual AI technologies is a promising solution. In this work, we introduce the EiF, a flexible fog computing framework that runs on IoT gateways with adaptive AI services fostered on the cloud. Our approach can be viewed as an integration of three emerging technologies, namely IoT, fog, and AI. Generally, EiF virtualizes an IoT service layer platform for fog nodes, and provides functions to manage and orchestrate various fog nodes; upon service virtualization and orchestration, AI services are fostered within both the federated cloud and distributed edge side and are deployed on fog nodes. We demonstrate the feasibility of EiF via the example of intelligent traffic flow monitoring and management.