Authentication is an important guarantee for vehicle to everything (V2X) commercial deployment. Currently, V2X security often use identity authentication schemes based on public key infrastructure (PKI). These schemes need to transmit certificates and signatures when sending safety-related information such as basic safety messages (BSMs), which need to occupy extra bandwidth and reduce the available channel capacity. So, V2X communication efficiency will be seriously affected in traffic congestion. In this paper, we propose a V2X authentication model based on physical layer characteristics. Then we use the Kalman filter to refine the iterative model and threshold model. The iterative model mainly realizes the priori and posteriori estimation of the current time based on the physical layer characteristics of the previous time, which provides the basis for the entire authentication process. The threshold model analyzes the mathematical characteristics of the priori estimation, and gives the calculation method of the authentication threshold. Since the conventional Kalman filter can only be used for linear discrete system, we use extended Kalman filter and unscented Kalman filter to extend the characteristics used for authentication to non-linearity. At the same time, iterative model and threshold model are improved according to these two algorithms. In terms of security and performance, we compare the proposed schemes with the conventional V2X authentication scheme and physical layer authentication scheme, and the effects of these schemes are analyzed by experiment.We select three characteristics for simulation: received signal strength indication (RSSI), the distance between the two vehicles, and the relative speed between the two vehicles. Then we analyze the process and effect of these two filters, and the factors that affect the threshold. Through experiments, the proposed authentication schemes can effectively take the responsibility of identity authentication in the V2X environment, and have high security level and low overhead, which can reduce the consumption of communication resources by security.

In the Internet of Vehicle (IoV), the limited computing capacity of vehicles hardly processes the intensive computation tasks locally. The computation tasks can be offloaded to multiaccess edge computing (MEC) servers for processing, where MEC provides the required computing capacity to the nearby vehicles. In this paper, we consider a scenario where there are cooperation and competition between vehicles, the offloading decision of any vehicle will affect the decisions of the others, and the computing resource allocation strategies by MEC will dynamically change. Therefore, we propose a joint optimization scheme for computation offloading decisions and computing resource allocation based on decentralized multiagent deep reinforcement learning. The proposed scheme learns the optimal actions to minimize the total weighted cost which is designed as the vehicles’ satisfaction based on the type of stochastic arrival tasks and dynamic interaction between MEC server and vehicles within different RSUs coverages. The numerical results show that the proposed algorithms based on decentralized multiagent deep deterministic policy gradient (DDPG) which is named De-DDPG can autonomously learn the optimal computation offloading and resource allocation policy without a priori knowledge and outperform the other three baseline algorithms in terms of the rewards.

Autonomous intelligent agricultural vehicles significantly reduce labor requirements in large-scale farming operations. While real-time precise positioning of agricultural vehicles is prerequisite for autonomous operation, the global positioning system (GPS)-based positioning systems demonstrate insufficient accuracy in large-scale farming scenarios. Moreover, the limited infrastructure deployment and compromised network quality in extensive agricultural fields render conventional assistance positioning frameworks inadequate. Unmanned aerial vehicles (UAVs), as integral components of vehicular networks, facilitate positioning and navigation within space–air–ground integrated networks (SAGINs). Based on this, this article proposes a multi-UAV rapid positioning assistance architecture for large-scale farming operations. Under this architecture, local area networks (LANs) are established between UAVs and agricultural vehicles, with each vehicle equipped with a uniform linear array (ULA). The system requires only signal transmission from UAVs, while the agricultural vehicles’ local processing systems perform direction of arrival (DOA) estimation, followed by joint estimation to determine vehicle positioning coordinates. However, limited signal snapshots in practical DOA estimation environments compromise estimation accuracy. While compressed sensing (CS) algorithms demonstrate significant potential for DOA estimation applications, conventional CS methodologies employing uniform overcomplete dictionariess (UODs) in sparse representation phase incur substantial computational complexity. Additionally, the orthogonal matching pursuit (OMP) algorithm and its derivatives exhibit susceptibility to local optima convergence during signal reconstruction. Consequently, this article proposes the NODGA-CS model, integrating nonuniform overcomplete dictionary (NOD) with genetic algorithm (GA) optimization for enhanced CS-DOA estimation. Experimental results demonstrate that the proposed assistance architecture achieves superior positioning accuracy when benchmarked against state-of-the-art positioning frameworks.

Robot perception difficulties in complex environments seriously affect robot operational efficiency. The application of generative big model (GBM) technology on robots through the Agriculture Internet of Things (AIoT) can solve the difficulty of low-operational efficiency. Therefore, this article proposes an automatic decision-making localization algorithm for robots fused with GBMs in AIoT. First, in the AIoT, the complex agricultural scene information is acquired in real-time by the Realsense D435i equipped on the robot, which is accessed in the GBM through a proprietary network to realize real-time intelligent sensing and control between the environmental information and the robot. Then, the innovative reinforcement learning method based on human solid feedback (S-RLHF) and the automatic generation method of weakly supervised fine-tuning data (WS-FT DAGM) are designed in the generative large model. At the same time, by combining the characteristics of the robot operation, two generative significant model recommendation methods are designed, which solves the problem of the difficulty of the target perception in the complex agricultural scene. Finally, by integrating the AIoT and generative large models, the critical method of real-time analysis of crop shading characteristics by the large model is innovatively proposed to solve the problem of low efficiency in robot operation. In the robot test experiments, the operation efficiency using the fusion of AIoT and generative large model reaches more than 92%, significantly improving the operation efficiency compared to the small model (CNN) method without AIoT and generative large model in the traditional agricultural robot.

Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources of VEC servers, the highly dynamic vehicular networks (VNs), different priorities of vehicular applications, and the threat of privacy disclosure. In this work, we propose a cooperative optimization for privacy-preserving and priority-aware offloading and resource allocation in VEC network (VECN) based on deep reinforcement learning (DRL). Firstly, we employed a privacy-preserving framework where the certificate authority (CA) is integrated into the VEC architecture. Furthermore, we formulated the dynamic optimization problem as a Markov decision process (MDP) by constructing a weighted cost function that integrates the priority of stochastic arrival tasks, privacy-preserving of offloading, and dynamic interaction between the edge servers and intelligent connected vehicles (ICVs). To solve this problem, a cooperative optimization for privacy and priority based on deep deterministic policy gradient (COPP-DDPG) is proposed by learning the optimal actions to minimize the weighted cost function. The simulation results show that COPP-DDPG has good convergence and outperforms the other four comparison algorithms in many aspects.

Currently, there are mature test methods for specific sensing devices or processing devices in the Internet of Vehicles (IoV). However, when a system is combined with these different types of devices and algorithms for real scenarios, the existing device-level test results cannot reflect the comprehensive functional or performance requirements of the IoV applications at the system level. Therefore, novel application-oriented system-level evaluation indexes and test methods are needed. To this end, we extract the data processing functional entities into specific and quantifiable evaluation indexes by considering the IoV application functions and performance requirements. Then, we build a roadside sensing and processing test system in a real test zone to collect and process these evaluation indexes into accurate multidimensional ground-truth. According to the actual test results of multiple manufacturers solutions, our proposed test method is verified to effectively evaluate the performance of the system-level solutions in real IoV application scenarios. The unprecedented evaluation indexes, system-level test method, and the actual test results in this paper can provide an advanced reference for academics and industry.