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.

To avoid random and blindfold forwarding decision in spray and wait routing in delay tolerant network, Binary Spray and Wait routing based on average delivery probability is proposed in this paper. Based on delivery predictability of PROPHET, average delivery predictability is proposed and used in Binary Spray and Wait routing to make forwarding decision. Furthermore, we evaluate it through simulations versus Spray and Wait routing protocol. The experimental results show there has slight higher average delivery rates and shorter average delays in Binary Spray and Wait routing based on average delivery probability.

Augmented intelligence (AuI) is a concept that combines human intelligence (HI) and artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to replace humans, AuI integrates humans into machines, recognizing their irreplaceable role. Meanwhile, human-in-the-loop reinforcement learning (HITL-RL) is a semisupervised algorithm that integrates humans into the traditional reinforcement learning (RL) algorithm, enabling autonomous agents to gather inputs from both humans and environments, learn, and select optimal actions across various environments. Both AuI and HITL-RL are still in their infancy. Based on AuI, we propose and investigate three separate concept designs for HITL-RL: HI-AI , AI-HI , and parallel-HI-and-AI approaches, each differing in the order of HI and AI involvement in decision making. The literature on AuI and HITL-RL offers insights into integrating HI into existing concept designs. A preliminary study in an Atari game offers insights for future research directions. Simulation results show that human involvement maintains RL convergence and improves system stability, while achieving approximately similar average scores to traditional $Q$ -learning in the game. Future research directions are proposed to encourage further investigation in this area.

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.