This paper investigates the secure communication between legitimate users in the presence of eavesdroppers, where the Intelligent Reflective Surface-Unmanned Aerial Vehicle (IRS-UAV) and Buffer-Aided (BA) relaying techniques are utilized to enhance secrecy performance. By jointly optimizing the link selection strategy, the UAV position, and the reflection coefficient of the IRS, we aim to maximize the long-term average secrecy rate. Specifically, we propose a novel buffer in/out stabilization scheme based on the Lyapunov framework, which transforms the long-term average secrecy rate maximization problem into two per-slot drift-plus-penalty minimization problems with different link selection factors. The hybrid Particle Swarm Optimization-Artificial Fish Swarm Algorithm (PSO-AFSA) is adopted to optimize the UAV position, and the IRS reflection coefficient optimization problem is solved by iterative optimization in which auxiliary variables and standard convex optimization algorithms are introduced. Finally, the delay constraint is set to ensure the timeliness of information packets. Simulation results demonstrate that our proposed scheme outperforms the comparison schemes in terms of average secrecy rate. Specifically, the addition of BA improves the average secrecy rate by 1.37 bps/Hz, and the continued optimizations of IRS reflection coefficients and UAV positions improve the average secrecy rate by 2.46 bps/Hz and 3.75 bps/Hz, respectively.

Federated Learning (FL), a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks (VECN), allows a distributed approach to train datasets of nodes locally, e.g., for mobile vehicles, and exchanges model parameters to obtain an accurate model without raw data transmission. However, the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models. Moreover, the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources. In view of this, we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks (FLVN). We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles. Further, to fortify the FL reliability, we adopt the consortium blockchain to oversee the reputation information, which boasts tamper-proof and interference-resistant qualities. Finally, we formulate the resource scheduling problem by jointly optimizing the computation capability, the transmission power, and the number of local training rounds, aiming to minimize the cost of clients while guaranteeing accuracy. To this end, we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy. Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.

Achieving higher accuracy positioning results in urban environments at a lower cost has been an important pursuit in areas such as autonomous driving and intelligent transportation. Low-cost Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) integrated navigation systems have been an important means of fulfilling the above quest due to the complementary error characteristics between INS and GNSS. The complex urban driving environment requires the system sufficiently adaptive to keep up with the time-varying measurement noise and sufficiently robust to cope with measurement outliers and prior uncertainties. However, many efforts lack a balance between adaptability and robustness. In this paper, a novel positioning method with both adaptability and robustness is proposed by coupling the Mahalanobis distance method, the Variational Bayesian method and the student’s t-distribution in one process (M-VBt method). This method is robust against non-Gaussian noise and priori uncertainties, plus adaptive against measurement noise uncertainty and time-varying noise. The field test results show that the M-VBt method (especially the Mahalanobis distance part) has significantly improved the system performance in the complex urban driving environment.

Relative positioning is recognized as an important issue for vehicles in urban environments. Multi-vehicle Cooperative Positioning (CP) techniques which fuse the Global Navigation Satellite System (GNSS) and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles. However, current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines. These interactions are called ‘information coupling’. In this work, we propose a new multi-vehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS (CDGNSS) and inter-vehicle ranging. We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound (CRLB) of the float estimation in CP. To fully use this coupled information, we propose a Whole-Net CP (WN-CP) method which consists of the Whole-Net Extended Kalman Filter (WN-EKF) as the float estimation filter, and the Partial Baseline Fixing (PBF) as the ambiguity resolution part. The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation, and the PBF strategy fixes the ambiguities of the one baseline to be estimated, instead of full ambiguity resolution, to reduce the computation load of ambiguity resolution. Field tests involving four vehicles were conducted in urban environments. The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.