By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized structure, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

The rapid development of communication and computing techniques enables unmanned aerial vehicles (UAVs) to provide reliable and cost-effective wireless communication and computing services from the air. Compared to the conventional fixed infrastructure, UAVs have attractive attributes, such as high flexibility and operability, and, as a result, on-demand line-of-sight connection links. Therefore, UAV-assisted wireless networks have been envisioned as a promising paradigm to achieve enhanced coverage and connectivity for future wireless communications. Meanwhile, achieving high levels of energy efficiency, sensing, communication, and computing capacities, and security and privacy are critical to the success of UAV-assisted wireless networks. In order to improve the performance of UAV-assisted wireless networks, some frameworks and mechanisms have been developed in the past few years. In this article, we provide a comprehensive survey of these developments. Specifically, we conduct a brief overview for the architecture of UAV-assisted wireless networks from four domains (i.e., framework-related, technology-related, challenge-related, and solution-related) and four aspects (i.e., sensing-related, communication-related, computing-related, and application-related). Then, the integrated sensing, communication, and computing for UAV-assisted wireless networks is introduced, followed by the characteristics and requirements. We also provide the implementation and applications of UAV-assisted wireless networks. Next, we discuss the challenges and the state-of-the-art solutions for UAV-assisted wireless networks. Finally, the advanced technologies for UAV-assisted communication and computing networks are exploited, followed by the potential research directions.

With the rapid development of marine networks, there have been growing demands for computation-intensive and delay-sensitive marine applications and services. However, the limited underwater energy supply and the acoustic channels result in the low efficiency for computing tasks and high transmission delay. In this paper, we investigate the unmanned aerial vehicles (UAVs)-assisted multi-access computation offloading in marine networks, with the objective of minimizing the energy consumption of ocean devices. Specifically, for the underwater segment, we consider the scenario that multiple underwater sensor nodes (USNs) covered by the unmanned surface vehicle (USV) upload their sensing data via non-orthogonal multiple access (NOMA) for improving the channel utilization. For the radio frequency segment, we consider the scenario that multiple UAVs hovering in the air act as the aerial edge servers for providing computing services, in which the USV offloads the workloads to UAVs via frequency division multiple access (FDMA) for avoiding their co-channel interference, while taking into account that a malicious node overhears the USV's offloading transmission. To improve the computation offloading efficiency, we formulate an optimization problem for USNs and USV to minimize the total energy consumption by jointly optimizing the USN's uploading time, USV's computation offloading, USV's offloading time, and the secrecy provisioning. Despite the non-convexity of the formulated joint optimization problem, we exploit a layered structure to decompose the problem, and propose efficient algorithms to obtain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms. Numerical results demonstrate that our algorithms can significantly reduce the energy consumption in comparison with the benchmark schemes.

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, due to the diverse behaviours of mobile users, unreliable or malicious users and platforms could provide untrusted data, which affects the quality of sensing service. Besides, mobile users are reluctant to participate in sensing tasks without sufficient incentives. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality and stimulates mobile users to join sensing tasks. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.

The unmanned surface vehicles (USVs) have been regarded as a promising paradigm to automatically perform emergency tasks in dynamic maritime traffic environment. However, the performance of maritime communication between USVs and offshore platforms becomes the critical challenge, and the efficiency of task allocation for USVs in smart ocean is low. In this paper, a novel task allocation scheme for USVs in smart ocean internet of things (IoT) is proposed to improve the efficiency of task allocation. Firstly, the offshore platform is developed to provide maritime communication for USVs in smart ocean IoT. Secondly, the network resource allocation process between USVs and offshore platforms is modeled as the second price sealed auction game, where the optimal bidding strategy of USV is derived by the Q-learning to maximize the utilities of USVs and offshore platforms. Thirdly, the task allocation scheme is proposed to improve the number of allocated tasks. Finally, the performance of the proposed scheme is conducted based on extensive simulations. Simulation results show that the proposed scheme can significantly improve the number of allocated tasks compared with the conventional schemes.

As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.