Vehicular fog computing (VFC) could provide fast task processing services for vehicles. To make vehicles/fog nodes willing to buy/sell resources, a double auction mechanism considering the interests of all parties is needed. However, few works study the auction issue in VFC. Different from the existing edge-related auction which only considers the price, some non-price attributes (location, reputation, and computing power) are also important for providing fair resource allocation in VFC. In this paper, we propose a multi-attribute based double auction mechanism in VFC, which considers both the price and non-price attributes for constructing reasonable matching. To the best of our knowledge, this is the first work to consider multi-attribute based auction in VFC. Our auction mechanism could satisfy computational efficiency, individual rationality, budget balance, and truthfulness. To verify the proposed mechanism, we simulate VFC using VISSIM and extract the driving data. Experimental results show the effectiveness and efficiency of this mechanism.

In the Internet of Autonomous Vehicles (IoAV), task offloading is a method to address computationally intensive tasks and ensure the safe operation of vehicles. However, under extreme weather conditions, the number of these tasks significantly increases, posing higher risks and challenges. Therefore, to mitigate risks and ensure the safe operation of vehicles, it is crucial to make quick and effective decisions during the task offloading process. Currently, most methods in this domain utilize Deep Reinforcement Learning (DRL). However, the large number of parameters in deep networks results in the characteristics of long decision time and large consumption of computational resources. In order to solve this problem, this paper proposes a task offloading scheme named Dynamic Pricing Driven Double Broad Reinforcement Learning (DP-DBRL), which utilizes Double Broad Reinforcement Learning (DBRL) to reduce model memory consumption and decision time. Additionally, it considers the high-speed mobility and resource variability to devise a more efficient dynamic pricing scheme that minimizes the overall delay in task processing for vehicles. To validate the proposed scheme, we conduct simulations using the VISSIM platform, meanwhile, we simulate task offloading scenarios under extreme weather conditions by randomly reducing factors such as the transmission rate and task execution efficiency of infrastructure and vehicles on the road. Finally, we deployed the proposed scheme both locally and on the Huawei Atlas 500 device to demonstrate its effectiveness and lightweight nature.

In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.

Overtaking and lane-changing are essential maneuvers in autonomous driving. A correct and timely operation can reduce the probability of collisions, enable vehicles to avoid potential hazards on the road, and enhance overall safety. In addition, they also play a vital role in ensuring the efficient flow of traffic and timely arrival at destinations. Currently, the dominant research on this problem is based on the Deep Q-Network (DQN) approach and some of its improved variants to solve it. However, limited by deep networks, there are inevitably problems such as complex network structures and numerous parameters to be optimized, which will prolong the response time for self-driving vehicles to make decisions, and further increase the safety risks for drivers. The emergence of the Broad Learning System (BLS) offers a new scheme, which can overcome the drawbacks of the deep network mentioned above. Considering the importance of response time for vehicle safety decisions, we proposed the Double Broad Q-Network (DBQN) algorithm, which replaces the deep network inside Double Deep Q-Network (DDQN) with BLS, to solve lane-changing and overtaking problems. Finally, we designed a new reward function based on three scenarios, and experiments have shown that our method can reduce response times and collision rates, thereby improving vehicle driving safety.

Through offloading tasks to surrounding edge nodes, the edge-assistant vehicular network (EAVN) could provide faster and more efficient services. To promote the deployment of EAVN, we need a reasonable resource allocation scheme. Nowadays, there are few auction mechanisms in EAVN scenario. Moreover, the auction mechanism in other edge computing scenario has following limitations. Firstly, current works only focus on the static/offline auction and do not consider that users will join and leave the auction system at any time in EAVN. Secondly, current works that determine the winning buyers/sellers only based on the price do not consider the unique attributes of EAVN, such as poor communication quality and various task demands. We design an online auction scheme for EAVN, which could satisfy the dynamics of users in the auction system. Moreover, the proposed auction mechanism also considers the non-price attributes, such as location, reputation, and computing ability, when constructing the matching between buyers and sellers. To verify our work, we simulate EAVN using a vehicular network simulator. Experimental results show that the proposed mechanism could meet the properties of computational efficiency, individual rationality, budget balance, and truthfulness.

High-altitude airships (HAS) and unmanned aerial vehicles (UAVs) equipped with Multiaccess Edge Computing (MEC) servers have emerged as promising aerial MEC nodes for providing task offloading (TO) services to intelligent mobile devices (IMDs) in post-disaster scenarios. HAS offers robust computing and energy resources, while UAVs provide flexible, low-altitude coverage for rapid deployment. However, direct task offloading from IMDs to HAS often leads to task failures due to high transmission delays. UAVs with limited onboard resources require to minimize resource waste. Additionally, IMDs in sparse areas face insufficient TO services due to unfair UAV coverage. This paper defines these challenges as a joint optimization problem involving TO, RA, and UAV coverage fairness. It proposes a cooperative aerial Multiaccess Edge Computing (AMEC) framework integrating HAS and UAVs to address the issue. Within this framework, a hybrid TO scheme is first developed to mitigate the high transmission delay between IMDs and HAS. Second, a Distance, Resource, Urgency-based Decision Mechanism (DRUDM) is designed to enhance the accuracy of UAVs in selecting target IMDs for TO services. Third, a Coverage Fairness Guarantee (CFG) strategy is proposed to optimize UAV flight trajectories, ensuring IMDs in sparse areas receive fair TO services. Finally, the joint optimization problem is modeled as a Multi-Agent Partially Observable Markov Decision Process (MA-POMDP), and a DRUDM–CFG algorithm is presented to efficiently solve this complex non convex optimization problem. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms in task completion rate and average delay, benefiting from the DRUDM mechanism. Meanwhile, the CFG strategy effectively improves TO service fairness for IMDs in sparse areas.