计算机技术与发展2026，Vol.36Issue(3)：18-27,10.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0262
数字孪生辅助车联网雾边协同预测性任务卸载框架
Digital Twin-assisted Predictive Task Offloading Framework for Vehicular Fog/Edge Collaborative Computing
摘要
Abstract
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.关键词
数字孪生/任务卸载/任务预测/雾节点/车辆边缘计算Key words
digital twin/task offloading/task prediction/fog node/vehicular edge computing分类
信息技术与安全科学引用本文复制引用
周启钊,石中煜..数字孪生辅助车联网雾边协同预测性任务卸载框架[J].计算机技术与发展,2026,36(3):18-27,10.基金项目
成都信息工程大学科研启动项目(KYTZ202269) （KYTZ202269）
重载快捷大功率电力机车全国重点实验室开放课题(QZKFKT2025-06) （QZKFKT2025-06）
山区河流保护与治理全国重点实验室开放课题(SKHL2413) （SKHL2413）
