As a promising new paradigm, Vehicular Edge Computing (VEC) can improve the QoS of vehicular applications by computation offloading. However, with more and more computation-intensive vehicular applications, VEC servers face the challenges of limited resources. In this paper, we study how to effectively and economically utilize the idle resources in volunteer vehicles to handle the overloaded tasks in VEC servers. First, we present a model of volunteer assisted vehicular edge computing, in which the cost and utility functions are defined for requesting vehicles and VEC servers, and volunteer vehicles are encouraged to assist the overloaded VEC servers via obtaining rewards from VEC servers. Then, based on Stackelberg game, we analyze the interactions between requesting vehicles and VEC servers, and find the optimal strategies for them. Furthermore, we prove theoretically that the Stackelberg game between requesting vehicles and VEC servers has a unique Stackelberg equilibrium, and propose a fast searching algorithm based on genetic algorithm to find the best pricing strategy for the VEC server. In addition, to maximize the reward of volunteer vehicles, we propose the volunteer task assignment algorithm for optimal mapping between the tasks and volunteer alliances. Finally, the effectiveness of the proposed scheme is demonstrated through a large number of simulations. Compared with other schemes, the proposed scheme can reduce the offloading cost of vehicles and improve the utility of VEC servers.

In vehicular edge computing (VEC), the execution of offloading task needs not only the task data uploaded by the requesting vehicle, but also the additional data to support the task to be executed successfully, and how to efficiently cache and access these supporting data becomes the key issue for task offloading in VEC. In this paper, we study the efficient caching mechanism to minimize the acquisition delay of the supporting data. Firstly, with the software defined network (SDN) based VEC framework, we analyze the acquisition ways of the supporting data and the caching collaboration between VEC servers. Then, according to the density of the requesting vehicles, we divide the VEC coverage into dense and ordinary areas. With the consideration of the similarity of the requested data and the distance between edge servers, the edge servers are clustered into multiple groups based on K-mean++ algorithm. Finally, each server's storage space is divided into three partitions, and the most beneficial data for itself, its group and the whole system are respectively stored in these partitions. Based on service area dividing, server grouping and storage space partitioning, we propose an efficient edge-cloud collaborative caching strategy, which can reduce the delay of data migration while task execution. Simulation results show that, compared with other schemes, the proposed caching strategy has better performance in terms of average data migration delay and application QoS.

Geographic routing is a research hotspot of the internet of vehicle (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between vehicle and position, but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features, vehicle association potential features, and propose a routing algorithm based on vehicle position analysis (RAVP), which can obtain the more accurate vehicle prediction trajectory. Then, vehicle distance is obtained based on vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end to end delay and routing overhead ratio.

Using Google cluster traces, the research presents a task offloading algorithm and a hybrid forecasting model that unites Bidirectional Long Short-Term Memory (BiLSTM) with Gated Recurrent Unit (GRU) layers along an attention mechanism. This model predicts resource usage for flexible task scheduling in Internet of Things (IoT) applications based on edge computing. The suggested algorithm improves task distribution to boost performance and reduce energy consumption. The system’s design includes collecting data, fusing and preparing it for use, training models, and performing simulations with EdgeSimPy. Experimental outcomes show that the method we suggest is better than those used in best-fit, first-fit, and worst-fit basic algorithms. It maintains power stability usage among edge servers while surpassing old-fashioned heuristic techniques. Moreover, we also propose the Deep Deterministic Policy Gradient (D4PG) based on a Federated Learning algorithm for adjusting the participation of dynamic user equipment (UE) according to resource availability and data distribution. This algorithm is compared to DQN, DDQN, Dueling DQN, and Dueling DDQN models using Non-IID EMNIST, IID EMNIST datasets, and with the Crop Prediction dataset. Results indicate that the proposed D4PG method achieves superior performance, with an accuracy of 92.86% on the Crop Prediction dataset, outperforming alternative models. On the Non-IID EMNIST dataset, the proposed approach achieves an F1-score of 0.9192, demonstrating better efficiency and fairness in model updates while preserving privacy. Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. This corroborates that the proposed method can preserve privacy by handling issues related to fairness in model updates and improving efficiency better than state-of-the-art alternatives.

Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible.

In vehicular edge computing, the edge servers may be overloaded once too many vehicles request the task offloading service, which will cause task offloading failure or the high service delay. To provide high-quality VEC service, the processing capabilities of the vehicles, the edge servers and the cloud should be utilized simultaneously in task offloading. In this paper, we focus on task offloading allocation for the requesting vehicle and the pricing schemes for the edge server and the cloud. From a market perspective, we model the competition and cooperation among the requesting vehicle, the edge server, and the cloud as a Stackelberg game. Then, based on the backward induction method, we transform the game problem into a convex optimization problem and theoretically prove that the game has a unique Nash equilibrium, thereby the optimal task allocation for the requesting vehicle can be obtained. Meanwhile, a genetic algorithm based searching algorithm is proposed to find the optimal pricing schemes for the edge server and the cloud, and the proposed algorithm has a rapid convergence due to the convex feature of the objective problem. Simulation results demonstrate that the proposed task allocation strategy has better performance than other solutions in terms of task offloading delay and cost, thus can make the existing resources fully used to undertake more offloading tasks.