In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

Providing reliable and flexible emergency communications for the Internet of Vehicles (IoV) is challenging in post-disaster scenarios, especially when communications infrastructures are unavailable or seriously damaged. Under such circumstances, it is highly demanded to deploy the air base station (ABS) to restore connectivity in disaster areas. Although the ABS can offer improved performance, the massive access of communications devices and the rising demand for many emergency-related services will bring new challenges to the ABS-aided IoV. In this paper, we propose a vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) collaboration framework to support emergency communications. Specifically, we incorporate non-orthogonal multiple access (NOMA) into the ABS-aided IoV. By using the successive interference cancellation (SIC) technique, V2I and V2V links can operate in four spectrum reuse modes, which are the extension of the conventional underlaying reuse mode. To fully exploit the advantages of the NOMA enhanced collaboration framework, we formulate a total power consumption minimization problem by jointly considering the mode selection, power control, and the channel state information (CSI) latency. The formulated problem is constrained by the reliability and rate requirements, and the decoding threshold of the SIC technique. Due to the NP-hard characteristics of the formulated problem, we decouple it first, and then adopt the graphical method to derive the analytical solution of optimal power control policies. Afterwards, we can get every feasible spectrum reuse mode. Among the feasible modes, we select the mode with minimum power consumption as the optimal mode. Finally, simulation results demonstrate that our proposed algorithm outperforms the current works in term of the total power consumption, e.g., by adopting the ABS and the NOMA technology, the total power consumption can be reduced by 22.3% and 40.9%, respectively.

Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple access (NOMA) transmission scheme with the high-altitude platform (HAP) and low-altitude platforms (LAPs) cooperated to securely provide delay-sensitive medical-care services. In the proposed scheme, we first design a novel HAP–LAP secure transmission framework to provide NOMA communication services to multiple hotspots. Constrained by the limited power and spectrum, we formulate an optimization problem, such that the privacy information delay is minimized. For thisnonconvex optimization problem, we design an alternating optimization framework, where the power, spectrum, and LAPs’ location are tackled in turn. In addition, we theoretically analyze the performance superiority compared with the orthogonal multiple access scheme and derive the secrecy outage probability closed-form expression. Finally, numerical results show the performance superiority of the proposed scheme compared with the current works with respect to the secure information delay.

This paper proposes an innovative Integrated Sensing and Communication (ISAC) framework for moving target detection by leveraging beyond-diagonal RIS (BD-RIS) to improve beamforming performance and control wireless propagation. In this framework, we first design a novel target-tracking method for moving target detection based on Extended Kalman Filtering (EKF) with accurate cooperative localization. In addition, to further improve the sensing accuracy, we minimize the joint posterior Cramér-Rao bound (PCRB) for both target position and velocity constrained by the communication performance requirements, and maintain the orthogonality and symmetry constraints of BD-RIS. Given the non-convex nature of the problem, we break it into two subproblems, which are solved iteratively using the proposed alternating optimization (AO) algorithm. The AO algorithm incorporates a semidefinite relaxation (SDR) method for beamforming and a penalty dual decomposition (PDD) approach for BD-RIS optimization. The simulation results demonstrate that: (1) the proposed prediction method accurately tracks the position and velocity of the target in dynamic environments; (2) the proposed AO algorithm is efficient and effective, exhibiting fast convergence and achieving a performance improvement of 6.7 % compared to conventional diagonal RIS.

Unmanned aerial vehicle mounted base stations (UAV-BSs) are expected to become an integral component of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructures. Motivated by the above, this paper proposes an aerial-ground integrated vehicular networking architecture, based on which a UAV-vehicle collaboration perspective is proposed. Specifically, an emerging vehicle-to-UAV (V2U) and vehicle-to-vehicle (V2V) collaboration framework is first presented to facilitate diverse vehicular applications. Next, we investigate the coverage radius maximization problem by optimizing the UAV-BS altitude. Meanwhile, by taking the channel state information (CSI) feedback delay into account, we formulate a V2U communication sum rate maximization problem by optimizing the power control and spectrum allocation, which is constrained by the capacity and reliability requirements. Then, we derive the closed-form expression of optimal UAV-BS altitude. Afterwards, we decouple the formulated sum rate maximization problem, and devise an efficient algorithm with polynomial complexity, where the optimal power control and spectrum sharing are solved. Finally, simulation results demonstrate that the maximum coverage radius and optimal UAV-BS altitude can be achieved by our proposed scheme in different urban environments. In addition, our designed scheme can effectively improve the V2U communication sum rate in comparison with the current works.