数字孪生辅助车联网雾边协同预测性任务卸载框架OA

Vehicular Edge Computing(VEC)supports latency-sensitive and computation-intensive vehicular applications by providing caching and computing services in vehicle proximity.However,VEC faces implementation challenges due to high vehicle mobility,diverse resource needs of different applications,unpredictable network dynamics.These challenges pose difficulties to efficient and balanced task offloading for 6G VEC Networks in the next era.The proposed framework aims to improve VEC network performance by integrating Digital Twin(DT)technology which creates virtual replicas of network nodes to estimate,predict,and evaluate their real-time conditions through realizing a mapping approach between vehicles and fog nodes.We aim to optimize the utilization of edge devices,minimize task completion delay and achieve fair task offloading opportunity among fog nodes by leveraging historical data and workload predictions.Validated via simulations,the proposed scheme shows superiority to the benchmarks in reducing task completion delay,adapting to the virtual-real mapping error,balancing and improving VEC system computation rates.