The rapid motorization of cities has led to the increasingly serious contradiction between supply and demand of road resources, and intersections have become the main bottleneck of traffic congestion. In general, capacity and delay are often used as indicators to improve intersection efficiency, but auxiliary indicators such as vehicle emissions that contribute to sustainable traffic development also need to be considered. It is necessary to evaluate intersection traffic efficiency through multiple measures to reflect different aspects of traffic, and these measures may conflict with each other. Therefore, this paper studies a multi-objective urban traffic signal timing problem, which requires a reasonable signal timing parameter under a given traffic flow condition, to better take into account the traffic capacity, delay and exhaust emission index of the intersection. Firstly, based on the traffic flow as the basic data, combined with the traffic flow description theory and exhaust emission estimation rules, a multi-objective model of signal timing problem is established. Secondly, the target model is solved and tested by the genetic algorithm of non-dominated sorting framework. It is found that the Pareto solution set of traffic indicators obtained by NSGA-III has a larger domain. Finally, the search mechanism of evolutionary algorithm is essentially unconstrained, while the actual traffic signal timing problem is constrained by traffic environment. In order to obtain a better signal timing scheme, this paper introduces the method of combining hybrid constraint strategy and NSGA-III framework, abbreviated as HCNSGA-III. The simulation experiment was carried out based on the same target model. The simulated results were compared with the actual scheme and the timing scheme obtained in recent research. The results show that the indices of traffic capacity, delay and exhaust emission obtained by the proposed method have more obvious advantages.

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.

Cooperative communication and resource limitation are two main characteristics of mobile ad hoc networks (MANETs). On one hand, communication among the nodes in MANETs highly depends on the cooperation among nodes because of the limited transmission range of the nodes, and multi-hop communications are needed in most cases. On the other hand, every node in MANETs has stringent resource constraints on computations, communications, memory, and energy. These two characteristics lead to the existence of selfish nodes in MANETs, which affects the network performance in various aspects. In this paper, we quantitatively investigate the impacts of node selfishness caused by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. We conducted extensive measurements on a proper simulation platform incorporating an OMNeT++ and INET Framework. Our experimental results quantitatively indicate the impact of node selfishness on the network performance in MANETs. The results also imply that it is important to evaluate the impact of node selfishness by jointly considering selfish nodes’ mobility models, densities, proportions, and combinations.

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.

Vehicular ad hoc network is a kind of mobile ad hoc networks which provides wireless communication between vehicles. In most cases, multi-hop communication is needed, because of the limited range of wireless transmission. The multi-hop communication among nodes strictly relies on the forwarding functionality of intermediate nodes. Due to resource limitation, the intermediate nodes may exhibit selfishness and refuse to bear forwarding tasks for others. In this article, we defined two types of selfish nodes, namely static selfish nodes and dynamic selfish nodes. The impact of the two types of selfish nodes are quantitatively investigated from various aspects including mobilities, proportions, densities, and combinations. We conducted exhaustive simulations on an integrated simulation platform which consists of OMNeT++, SUMO, INET, and Veins. The experimental results indicate that the static selfish nodes have more harmful impacts on the performance of vehicular ad hoc networks in terms of average packet delivery ratios and end-to-end delays. Moreover, the results also imply that the impact of node selfishness should be evaluated by a comprehensive consideration of mobilities, proportions, densities, and combinations of selfish nodes.

Vehicular network applications heavily rely on multi-hop communication technologies to ensure efficient and reliable data transmissions because of the limited wireless transmission range of vehicles, road side units, and some other mobile devices in the network. There is a common assumption in most of the applications that all network nodes are cooperative during the entire network procedure. However, this is not practical in reality, because rational nodes are reluctant to assist others with consuming own precious resources. Hence, incentive mechanisms are inevitably required to encourage the intermediate nodes to forward data packets for multi-hop communications. Nevertheless, the high mobility of nodes and the intermittent connectivity among them make the estimation of the environment extremely challenging in vehicular networks. Moreover, in some traffic scenarios where there is a lack of fixed infrastructures (rural area, disaster areas, or during maintenance), the incentive schemes should be self-organized in a distributed manner. In this paper, we propose an incentive scheme for multi-hop communications in vehicular networks. The proposed scheme estimates the complicated dynamic environment based on a fuzzy logic approach, then further stimulates intermediate nodes to maximize the expected utility from a long-term perspective based on a reinforcement learning method in a fully distributed manner. To evaluate the performance of our proposed scheme, we conducted extensive computer simulations based on realistic traffic networks in Xi’an city of China. The results of the simulations demonstrate that the proposed scheme has the superiority of incentive capabilities over other baseline approaches in terms of packet delivery ratio, end-to-end delay within vehicular networks.