The metaverse aims to build an immersive virtual reality world to support the daily life, work, and recreation of people. In this survey, the status quo of the metaverse is investigated, and the technical framework of the metaverse is introduced from three aspects: 1) the generation of virtual worlds; 2) the connection of virtual and real objects; and 3) the transmission of data. Specifically, this survey first discusses the development and challenges of the related technologies for virtual world generation methods from three aspects: 1) the 3-D world generation; 2) immersive human–computer interaction experience; and 3) ecosystem. Second, we investigate the status quo of extended reality (XR), motion capture, and brain–computer interface technologies and evaluate the potential and research directions of these entrance technologies for the metaverse. Finally, network and data transmission technologies for the metaverse are reviewed from the Internet of Things (IoT), 5G/6G wireless, and edge computing aspects, the demand side of the metaverse in virtual-reality interpromotion, big data processing, and low-latency networking is discussed, and promising research hotspots are identified.

In order to mitigate the impact of explosively increasing data traffic on content request services in the Internet of Vehicles (IoV), edge caching technology is implemented in IoV to accelerate the response process of content requests and release the backhaul burden of the base station. However, the content popularity obtained by the traditional content popularity method cannot capture the requests of vehicles accurately due to the time-varying characteristics of the content popularity, which results in a relatively low cache hit ratio. Thus, this article proposes a cooperative caching strategy with content request prediction (CCCRP) in IoV, which precaches the contents requested by vehicles with greater probability in other vehicles or the roadside unit (RSU) to reduce the content acquisition delay. Specifically, vehicles are first clustered using the K-means method to simplify the process of vehicle requesting and content transmission. Then, content requests from vehicles are predicted using the long short-term memory (LSTM) networks according to the historical content request information. Finally, reinforcement learning method is adopted to solve the objective function to obtain the optimal caching decision, which improves the Quality of Service (QoS) of vehicle requests. Simulation results demonstrate that CCCRP can improve the cache hit ratio and reduce content acquisition delay effectively. For example, the cache hit ratio of CCCRP can be increased by 5% and 7% compared to the traditional LFU and LRU caching strategies when the Zipf parameter equals 0.7, respectively.

In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

To support diverse Internet of vehicles (IoV) services with different quality of service (QoS) requirements, network slicing is applied in vehicular networks to establish multiple logically isolated networks on common physical network infrastructure. However, dynamic and efficient radio access network (RAN) slicing adapting to the dynamics of vehicular networks remains challenging. The diverse applications make multi-dimensional resource requirements, which will result in the resource allocation more complicated. In addition, the system needs to frequently adjust the resources of slices, which will cause additional slicing overhead. Thus, to solve the above problems, we propose a resource allocation strategy by using multi-agent reinforcement learning to allocate resources in vehicular networks. Firstly, the cost composition of RAN slicing is analyzed, and the optimization problem is formulated to minimize the long-term system cost. Then, we transform the resource allocation problem into a partially observable Markov decision process. Finally, we propose a multi-agent deep deterministic policy gradient based resource allocation algorithm to solve it. All base stations are treated as independent agents, and they cooperatively allocate spectrum and computing resources. Simulation results show that the proposed strategy reduces the system cost effectively compared to the benchmarks, and the average QoS satisfaction rate achieves 96.5%.

The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this article proposes a trust management model enabled by blockchain to ensure the traceability, nontampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world data sets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.

To support the diversified Quality of Service (QoS) requirements of application scenarios, network slicing has been introduced in the mobile cellular network. It allows mobile cellular network operators to accomplish the creation of multiple logically isolated networks on common network infrastructure flexibly depending on specified demands. Meanwhile, in Internet of Vehicles (IoV), it is very intractable to supply a stable QoS for the vehicles, especially for the dynamic vehicular environments. Thus, we investigate the IoV slicing problem in this article, and propose a QoS guaranteed network slicing orchestration, namely, the long short-term memory-based deep deterministic policy gradient algorithm (LSTM-DDPG), to ensure the stable performance for the slices. Specifically, we first decouple the resource allocation problem into two subproblems. After that, the deep learning and reinforcement learning (RL) are used to allocate resources collaboratively to solve these two questions. We use deep learning LSTM to track the characteristic of the long-term vehicular environment changing, and the RL algorithm DDPG is utilized for online resource tuning. Extensive simulations have proved the effectiveness of the LSTM-DDPG, which can offer stable QoS to the vehicles with a probability greater than 92%. We also demonstrated the adaptiveness of the proposed orchestration with different slicing environments, and the performance is always optimal compared to that of other algorithms.