Efficient task offloading decision is a crucial technology in vehicular edge computing, which aims to fulfill the computational performance demands of complex vehicular tasks with respect to delay and energy consumption while minimizing network resource competition and consumption. Conventional distributed task offloading decisions rely solely on the local state of the vehicle, failing to optimize the utilization of the server’s resources to its fullest potential. In addition, the mobility aspect of vehicles is often neglected in these decisions. In this paper, a cloud-edge-vehicle three-tier vehicular edge computing (VEC) system is proposed, where vehicles partially offload their computing tasks to edge or cloud servers while keeping the remaining tasks local to the vehicle terminals. Under the restrictions of vehicle mobility and discrete variables, task scheduling and task offloading proportion are jointly optimized with the objective of minimizing the total system cost. Considering the non-convexity, high-dimensional complex state and continuous action space requirements of the optimization problem, we propose a task offloading decision-making algorithm based on deep deterministic policy gradient (TODM_DDPG). TODM_DDPG algorithm adopts the actor–critic framework in which the actor network outputs floating point numbers to represent deterministic policy, while the critic network evaluates the action output by the actor network, and adjusts the network evaluation policy according to the rewards with the environment to maximize the long-term reward. To explore the algorithm performance, this conduct parameter setting experiments to correct the algorithm core hyper-parameters and select the optimal combination of parameters. In addition, in order to verify algorithm performance, we also carry out a series of comparative experiments with baseline algorithms. The results demonstrate that in terms of reducing system costs, the proposed algorithm outperforms the compared baseline algorithm, such as the deep Q network (DQN) and the actor–critic (AC), and the performance is improved by about 13% on average.

The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines.

The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.