The development of novel applications causes increased demands on the computational capabilities of Vehicular Edge Computing (VEC). Current works have introduced Unmanned Aerial Vehicles (UAVs) into VEC to solve the resource-constrained problem. However, given the limited storage of UAVs, the key question is to design the offloading strategy and determine which service programs should be cached. In this article, we propose a three-stage game model that aims at providing a precise analysis of the interaction among the Base Station (BS), the UAV, and the User Vehicle (UV). In stage I, the BS is responsible for determining the cache strategy of the UAV and communicating the price strategy to the UVs. In stage II, the UAV communicates the price strategy to the UVs. In stage III, each UV determines its offloading decision based on the price strategy, to minimize the task execution delay and cost. Compared with current approaches, we cache the frequently requested services in the UAV to satisfy the real-time requirements and use game theory to solve the decision-making, which achieves the effect of reducing the delay and cost. The experiment results are performed to assess the convergence and effectiveness of the proposed algorithm.

In Vehicular Edge Computing (VEC), the high mobility of vehicles and periodic of traffic flow present challenges to the effectiveness of roadside units. Unmanned Aerial Vehicles (UAVs) can serve as aerial base stations to address this issue. Federated Learning (FL) is employed to reduce backhaul load. However, the limited battery and bandwidth of UAVs constrain long-term training capabilities. We propose a collaborative deployment of multiple UAVs to maximize communication coverage, utilizing a Particle Swarm Optimization (PSO) algorithm for optimal deployment decisions. We take into account the mobility of vehicles during vehicle selection to prevent network interruptions. Furthermore, knowledge distillation is used to compress the local model without sacrificing accuracy, thereby reducing transmission overhead and accelerating model convergence. Finally, the Deep Deterministic Policy Gradient - Double Dueling Deep Q-Network (DDPG-D3QN) algorithm addresses optimal vehicle selection and resource allocation in dynamic scenarios. Experimental results demonstrate that our approach effectively meets communication needs in urban areas while enhancing training efficiency and accuracy.

Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the generalization of models. Therefore, we propose a federated meta-learning (FML) framework to solve computation offloading for UAV-assisted VEC. In this paper, we are concerned with computation offloading of temporary hotspot regions due to traffic congestion. Firstly, we construct a computation offloading problem with energy-delay tradeoffs and convert the problem to a Markov Decision Process (MDP). Then, we use FML to train personalized models for different vehicles while enhancing the generalization, we propose a Graph neural network-based FL Probabilistic Embedding for Actor-critic RL (GFL-PEARL) algorithm. We model the context as a Directed Acyclic Graph (DAG) and use GNN to reconstruct the inference network of the PEARL algorithm to extract the correlation between contexts fully. We dynamically adjust the task priority during the FML training process to improve the sampling efficiency. Finally, we verify the performance of the algorithm through simulation and physical experiments. Experimental results show that our algorithm can reduce average cost and task overtime rate by 31% and 56% respectively compared with the benchmarks.

As autonomous driving technology advances, the intelligence levels of vehicles continue to increase. However, meeting the demands of autonomous driving in various scenarios requires improved wireless communication and vehicle perception capabilities. Integrated sensing and vehicular edge computing (VEC) technology can provide collaborative perception and computing resources for vehicles. Nevertheless, the high-speed mobility of vehicles leads to frequent changes in channel state information and distances between vehicles and roadside units (RSUs), which poses challenges for low-latency perception processing. Additionally, most research overlooks the impact of vehicle mobility on perception accuracy and lacks effective resource allocation strategies for multi-source perception data fusion tasks. Addressing existing research shortcomings, this paper proposes a deep reinforcement learning(DRL)-based resource allocation method. It first adopts Integrated Sensing and Communication (ISAC) technology in the same frequency band to improve spectrum efficiency and integration. Secondly, it constructs a data fusion model to enhance vehicle perception capabilities and describes the data fusion process between vehicle terminals and RSU terminals. Furthermore, this paper designs a resource allocation algorithm for multi-source perception data fusion tasks with the optimization goal of minimizing task completion delay and system average energy consumption. Considering the mobility of vehicles and the frequent changes in communication channel states, this paper transforms the constructed problem into a Markov decision process (MDP). It solves it using the Improved Dueling Twin Delayed Deep Deterministic policy gradient (ID-TD3) algorithm. Experiment results demonstrate that the proposed strategy can reasonably allocate system resources, effectively reducing task completion delay and system average energy consumption.

Intelligent Connected Vehicles (ICVs) need to obtain real-time traffic data from nearby ICVs or remote content providers to ensure safe driving. However, providers are hesitant to share their data due to privacy and benefits concerns. To ensure privacy while improving efficiency of obtaining data, we proposed smart contract-based data sharing among ICVs, and content delivery between ICVs and remote content provider. To solve low willingness to vehicles due to untrustworthy third-party platforms, we use smart contracts to implement access control during data upload and transaction. Then, we propose a one-to-many sharing model based on Stackelberg game to model the interaction between consumers and owners. Consumers adjust their reward strategies with the owners’ optimal strategies to maximize its utility, thus obtaining the nash equilibrium solution. To provide reliable quality of service (QoS) and security guarantee for content delivery, smart contracts regulate the delivery process, facilitating automatic execution under specific conditions. Transaction records audited and stored on blockchain enhance transparency and trustworthiness. Utilizing a delivery utility model that considers benefits, costs, and mining profits, proposed quantum particle swarm optimization (QPSO) algorithm is used to find the optimal solution. We built an EdgeChain testbed, and used BDD-100K dataset to evaluate the performance in utility, access delay, etc. Compared to CTM and MFPA, proposed data sharing algorithm achieves maximum consumer utility. Compared to LRU, PCCM and MARL, when content is 400, proposed content delivery algorithm reduces average access delay by 30.88%, 18.92% and 4.86%, and reduce backhaul load by 50.04%, 47.23% and 3.16%.

In this paper, we plan to use ground-based stations in mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) to provide communication and computation offloading services in disaster areas. However, optimizing the initial number and three-dimensional position of deployed UAVs is a prerequisite for providing computing services to users. Additionally, due to the limited battery and computing power of UAVs, it is a major challenge to rationally design the UAV trajectory during the computational offloading period to ensure communication quality for mobile users and reduce the energy consumption for completing tasks. Thus, we propose a cooperative computation offloading strategy with on-demand deployment of multi-UAV in UAV-aided MEC. The strategy utilizes the predicted user trajectory for UAV deployment on the premise of the minimum path loss of users. Then, to minimize total energy consumption for completing tasks, a joint optimization problem comprising user association strategy, computing resource allocation strategy, and UAV trajectory is proposed, which is a mixed-integer nonlinear program (MINLP). Therefore, to find the suboptimal solution, we use the block coordinate descent method to solve the problem. Numerical results show that the proposed algorithm can efficiently reduce the path loss by up to 18.55% and the total energy consumption by 18.28% compared to the benchmarks.

The convergence of unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks and blockchain transforms the existing mobile networking paradigm. However, in the temporary hotspot scenario for intelligent connected vehicles (ICVs) in UAV-aided MEC networks, deploying blockchain-based services and applications in vehicles is generally impossible due to its high computational resource and storage requirements. One possible solution is to offload part of all the computational tasks to MEC servers wherever possible. Unfortunately, due to the limited availability and high mobility of the vehicles, there is still lacking simple solutions that can support low-latency and higher reliability networking services for ICVs. In this article, we study the task offloading problem of minimizing the total system latency and the optimal task offloading scheme, subject to constraints on the hover position coordinates of the UAV, the fixed bonuses, flexible transaction fees, transaction rates, mining difficulty, costs and battery energy consumption of the UAV. The problem is confirmed to be a challenging linear integer planning problem, we formulate the problem as a constrained Markov decision process. Deep Reinforcement Learning (DRL) has excellently solved sequential decision-making problems in dynamic ICVs environment, therefore, we propose a novel distributed DRL-based P-D3QN approach by using Prioritized Experience Replay strategy and the dueling double deep Q-network (D3QN) algorithm to solve the optimal task offloading policy effectively. Finally, experiment results show that compared with the benchmark scheme, the P-D3QN algorithm can bring about 26.24% latency improvement and increase about 42.26% offloading utility.

In vehicular edge computing, Unmanned Aerial Vehicles (UAVs) have become a feasible solution for addressing high deployment costs faced by base stations in congested roads during peak hours. However, UAVs cannot cache all requested content due to limited storage. Hence, we propose a content caching strategy based on user preference predictions. To address resource consumption and user privacy concerns during the training process, we propose a user preference prediction model based on hierarchical federated learning training. Specifically, we employ a hierarchical clustering approach to partition user vehicles and UAVs into multiple clusters and utilize hierarchical federated learning to train prediction models within each cluster. Furthermore, to tackle the joint optimization problem of content caching and bandwidth allocation, we propose I-MADDPG, an improved multi-agent deep deterministic policy gradient algorithm. It determines the next continuous action based on the reward value at the current moment and the average reward value in the iteration period as reference parameters. The experimental results demonstrate that the proposed algorithm has significantly enhanced training efficiency compared to the baselines. Additionally, it has improved cache hit rate and reduced content request delay through effective resource allocation.

Massive map data transmission and the strict demand for the privacy of high-precision maps have brought significant challenges to the cache of high-precision maps in intelligent connected vehicles (ICV). Federal learning (FL) was introduced to reduce the pressure on the edge network and protect privacy. But the high dynamics of cars and limited resources lead to low accuracy and high training delay. We propose a joint optimization scheme of participant selection and resource allocation for federated learning. In each time slice, vehicles are determined whether to participate in training, which minimizes long-term training delay with limited energy consumption. To meet the delay and privacy requirements of high-precision map caching, we present an edge cooperative caching scheme based on federated deep reinforcement learning (F-DRL), which aims to achieve dynamic adaptive edge caching while protecting user privacy. The collaborative caching model is formulated as a Markov decision process (MDP). Dueling Deep Q Network (Dueling-DQN) is used to solve the optimal strategy, and federal learning is used for training. Enough comparative experiments to evaluate the performance of the proposed schemes. The aspects of reliability, cache hit rate, and training accuracy prove that the method effectively improves the training parameters of federated learning while meeting a high-precision map cache’s delay and reliability requirements.