With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.

Network Slicing (NS) is one of the pillars of the fifth/sixth generation (5G/6G) of mobile networks. It provides the means for Mobile Network Operators (MNOs) to leverage physical infrastructure across different technological domains to support different applications. This survey analyzes the progress made on NS resource management across these domains, with a focus on the interdependence between domains and unique issues that arise in cross-domain and End-to-End (E2E) settings. Based on a generic problem formulation, NS resource management functionalities (e.g., resource allocation and orchestration) are examined across domains, revealing their limits when applied separately per domain. The appropriateness of different problem-solving methodologies is critically analyzed, and practical insights are provided, explaining how resource management should be rethought in cross-domain and E2E contexts. Furthermore, the latest advancements are reported through a detailed analysis of the most relevant research projects and experimental testbeds. Finally, the core issues facing NS resource management are dissected, and the most pertinent research directions are identified, providing practical guidelines for new researchers.

The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed.

The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis.

This paper introduces a quantitative analytical framework for developing radio resource management (RRM) strategies tailored to 5G services of enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC). By leveraging the Open Radio Access Network (RAN) architecture, the framework enables flexible and efficient management of radio resources to meet the competing demands of services offered by the fifth/sixth generation (5G/6G) of wireless networks. The proposed RRM methodology incorporates formal verification capabilities to generate vast Pareto optimality datasets for specific RAN design parameters, establishing a foundation for rigorous RRM strategy selection. Additionally, the proposed approach enhances data-driven RRM decision-making through the application of unsupervised machine-learning techniques. Our proposed RRM methodology outperforms other baseline (i.e., stochastic and resource-proportional) RRM schemes, achieving up to 30% improvement in the 5G service reward.

With the rapid development of Internet of Vehicles (IoVs), the imperative for data security within IoVs has increased, especially regarding data tampering and spreading false data. To address this issue, a secure blockchain-based trust management is employed to detect malicious behaviors. For instance, legitimate vehicles may be exploited to disseminate incorrect data, while blockchain technology maintains the transparency and reliability of trust value. However, existing schemes are challenged by the slow generation of trust opinions (lack of trust opinions due to insufficient interactions) and high latency. To solve such problems, we propose a Blockchain-based Trust Management Scheme for Social Routing in IoVs (BTSR). Specifically, the trust management for vehicles we proposed then considers four types of trust: direct trust, indirect trust, global trust, and roadside unit (RSU) trust. The direct trust is evaluated by the data-sending vehicle, indirect trust is provided by other vehicles, global trust originates from RSUs, and RSU trust is used to evaluate RSUs by Trusted Authority (TA). Furthermore, to speed up the generation of trust opinions, a verifiable active detection mechanism is employed via the RSU to increase the frequency of interactions. Here, the sender transmits detection data to the receiver, which then forward it to the RSU for validation, thereby enhancing the interaction between the vehicle and the RSU. Meanwhile, to prevent active detection from being exploited by malicious RSUs and vehicles, the Trust Outlier Factor (TOF) algorithm is introduced to identify outlier RSUs whose trust opinions significantly deviate from those of other RSUs. Finally, we employ a dual-layer Hyperledger Fabric (HF) to reduce latency by lowering the consensus requirement at the edge layer, and to ensure data security by combining trust management at the core layer. A multitude of experiments confirms that our scheme surpasses baseline schemes, showcasing high precision, recall, and $F$ -measure.