Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity. In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse. Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, UAVs with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes.

Vehicular networks expect to promote the road safety for moving vehicles and improve the driving experience of drivers. In the vehicular networks, secure content delivery becomes significant yet very challenging to protect the vehicles from various threats. To this end, this paper proposes a secure content delivery scheme in vehicular networks when the vehicles request the roadside units(RSUs) for content delivery services. Specifically, we first establish a novel trust evaluation scheme for both the vehicles and RSUs by exploiting other vehicles to monitor the actions during the process of content delivery. Then, a bargaining game based pricing model is proposed to motivate the vehicles and RSUs to act more positively to facilitate content delivery. Next, to achieve the secure content delivery and optimize the players' utilities, we apply the backward induction method in a two-round game and an M-round game, respectively. Finally, simulation results are provided to evaluate the efficiency of the proposed scheme by comparing with several conventional schemes.

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme.

The publish/subscribe system over the vehicular networks enables the contents providers to obtain more profits from a flexible notification system. The publish/subscriber system is with an asynchronous communication protocol, which is suitable for the vehicles in a delay-tolerant network. However, as the trend of vehicles is moving towards autonomous driving vehicles (ADVs) in the future, to the best of our knowledge, none (few) of current works focus on the publish/subscribe system in the autonomous vehicular networks (AVNs). In this paper, we propose a truck platooning aided secure publish/subscribe system based on smart contract in AVNs. Firstly, the truck platoons are utilized to be the brokers of the publish/subscribe system to realize a more efficient and secure publish/subscribe system. Secondly, a vehicular blockchain is established for the publish/subscribe system to record the transactions between brokers and publishers/subscribers to guarantee the complete and safe implementation of the smart contracts. Next, a contract based on the principal-agent problem is designed between the publisher and broker to maximize their utilities and stimulate the broker to be more active in the contents subscribing service. Furthermore, a smart contract is also designed between the subscriber and broker by the cloud server to achieve for them higher utilities and match an appropriate broker for the subscriber by considering its type. Finally, we carry out extensive numerical results to evaluate the performances of the proposed contracts, while the results also demonstrate that the proposal outperforms the conventional schemes.

As a significant component of electric energy trades, retail electric market (REM) can effectively alleviate the pressure of load demand from the power grid. However, the load demand uncertainty of customers becomes a nodus because retail electricity providers (REPs) should predict the load demand when trading with wholesaler electricity provider (WEP) based on the interaction. Therefore, in this paper, we propose an optimal energy scheduling scheme in REM with consideration of the influence of decisions made in pre-purchase stages to situations in real-time stages. Firstly, we present a trading framework to analyze the strategies of REPs in REM, in which REPs conduct both pre-purchase trading with WEP, and real-time trading with customers. Then, to solve the scheduling problem caused by the demand uncertainty of customers, we design a power allocation mechanism based on the charging demand degree, by which REPs can minimize the operating cost while ensuring that each electric vehicle can be charged with sufficient energy. Next, to minimize the cost of REPs in the pre-purchase stage, we adopt deep Q-network (DQN) algorithm to implement the pre-purchase schedule. The charging station adjusts the pre-purchased schedule for each period through Q-learning and utilizes the optimal strategy to design the electricity schedule. Finally, simulation experiments show that the proposal can obtain the optimal strategy to significantly reduce the operating costs of REP.