The upsurge of mobile service demands underscores the inevitability of the emergence of 6G technology. Amidst the challenges that accompany the 6G networks, the exponential growth of computation-intensive and delay-sensitive applications in vehicular networks poses a significant hurdle. Vehicular edge computing relying solely on terrestrial infrastructures cannot meet the performance requirements due to insufficient resources in edge servers. To address this challenge, we introduce a novel three-layer hierarchical architecture called the air-ground-end aerial computing (AGEAC) architecture, which consists of three types of resource providers, namely high-altitude platform stations, roadside units, and volunteer vehicles willing to share their underutilized resources. We select service vehicles based on the utilization rate of their underutilized resources to ensure fairness. Our objective is to minimize the average latency of all tasks in the long term, and it is formulated as a mixed-integer nonlinear programming problem for task offloading and resource allocation. The complicated intertwined problem is decomposed into four sub-problems by leveraging Lyapunov optimization method. A multi-agent reinforcement learning-based algorithm is designed to make offloading decisions, and convex optimization is introduced to allocate resources iteratively. Extensive simulations have been conducted to demonstrate the superiority of AGEAC in terms of convergence speed, average latency, and resource utilization.

Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections.

With the recent development of intelligent transportation systems, vehicles are getting more and more powerful and involving huge number of real-time applications including computation-intensive and delay-sensitive applications in vehicle-to-vehicle communication networks, which are challenging to be processed by an individual vehicle, especially for a legacy one. These resource-consuming computation tasks can be offloaded to another vehicle with idle computing resources for processing. However, it is difficult to select an optimal resource vehicle for each computational task in a dynamic vehicular network where the wireless communication links are lossy and the network topology is time-varying due to the high mobility of vehicles. In this work, we investigate the resource vehicle selection problem, which determines one of vehicles as the most suitable resource vehicle for the current computational task. To this end, a novel fuzzy logic based binary offloading scheme is proposed in vehicle-to-everything (V2X) communication networks. The proposed scheme evaluates the vehicles within the proximity by jointly considering the computational power, mobility, and link quality between neighboring vehicles based on fuzzy logic to ensure a high task completion rate for binary offloading applications. In order to validate the performance of the proposed offloading scheme, we conducted extensively computer simulations with the real traffic network in Xi’an city in China. As the results show, the offloading scheme expeditiously makes optimal decision in various environments, and outperforms other baseline proposals in terms of average task completion time and task completion rate. The average task completion time is reduced by 37.5%, whereas the best reduction of other baselines is 21.1% in our simulations.