The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.

Federated learning (FL), as a distributed machine learning technology, allows large-scale nodes to utilize local datasets for model training and sharing without revealing privacy, which has significant efficiency and advantages in artificial intelligence (AI)-based knowledge sharing of connected and autonomous vehicles (CAVs). However, for FL, there are challenges to ensure the security of knowledge, deal with the lazy behavior of participants, and enforce effective incentives. To bridge the gaps, in this paper, we first propose a hierarchical blockchain-supported FL architecture that utilizes the immutable and transparent properties of blockchain to enable secure storage and sharing of knowledge and transaction information with scalability. Then, considering the cost and laziness of the participants in the FL process, we propose an incentive mechanism combined with the supervision game to attract high-quality participants based on a comprehensive evaluation of model quality and participants’ reputation. Extensive simulation results validate that our proposal can improve learning accuracy and efficiency while ensuring security.

Large Language Models (LLMs), as advanced AI technologies for processing and generating natural language text, bring substantial benefits to the Industrial Internet of Things (IIoT) by enhancing efficiency, decision-making, and automation. Nevertheless, their deployment faces significant obstacles due to high computational and energy demands, which often exceed the capabilities of many industrial devices. To overcome these challenges, edge-cloud collaboration has become increasingly essential, assisting in offloading LLMs tasks to reduce the computational load. However, traditional reinforcement learning (RL)- based strategies for LLMs task offloading encounter difficulties with generalization ability and defining explicit, appropriate reward functions. Therefore, in this paper, we propose a novel framework for offloading LLMs inference tasks in IIoT, utilizing a Decentralized Identifier (DID)-based identity management system for trusted task offloading. Furthermore, we introduce an intelligence-based RL (IRL) approach, which sidesteps the need for defining specific reward functions. Instead, it uses “intelligence” as a metric to evaluate cognitive improvements and adapt to varying environmental preferences, significantly improving generalizability. In our experiments, we employ the GPT-J-6B model and utilize the Human Eval dataset to assess its ability to tackle programming challenges, demonstrating the superior performance of our proposed solution compared to existing methods.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

With the increase of on-board sensors, as a new paradigm of mobile crowdsensing (MCS), vehicular crowdsensing (VCS) shows great potential in realizing low-cost, large-scale sensing tasks. In order to improve the user engagement and task completion quality of VCS, an appropriate incentive mechanism can promote enough users to participate in the sensing activities and further provide high-quality sensing data. However, due to the contradiction between personal interests and user data security protection, the development of the incentive mechanism is seriously affected. To deal with these challenges, this paper aims to propose a security protection incentive mechanism with data quality assurance (SPIM-DQA) for the VCS system. First, we adopt the blockchain-enabled VCS framework, and propose a series of smart contracts to ensure the automatic execution of the incentive mechanism, which solves the user data security issues existing in the traditional incentive mechanism. Then, based on this framework and these smart contracts, a data quality-aware incentive mechanism is proposed from the perspective of data quality. After selecting low-cost and high-quality users to perform the crowdsensing task, user reputation is updated by evaluating the quality of the provided data. In particular, there is a correlation between user reputation and reward distribution, which incentivizes users to consistently provide high-quality data to increase their rewards. Finally, extensive simulation results show that SPIM-DQA can effectively improve data quality while meeting security requirements.

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.