AI 中文总结
针对车载消费电子VEC系统的安全与性能挑战，提出无人机辅助的协作VEC架构，设计TAGO框架实现威胁感知任务卸载与时空缓存优化，仿真验证其可降低任务延迟、提升缓存效率。
AI 中文摘要
车载消费电子日益支持计算密集型和延迟敏感型服务，对车载边缘计算（VEC）系统提出了严格的效率、可靠性和安全要求。在动态车载环境中，基于推理的信息泄露和异常通信行为进一步威胁系统性能与数据隐私。为应对这些挑战，本文提出一种无人机辅助的协作VEC架构，将威胁感知任务卸载与路侧单元（RSU）及无人机边缘节点的智能时空缓存相融合。研究人员开发了一种安全感知上行传输模型，用于捕捉潜在的信息泄露风险与异常通信模式，从而支持自适应卸载决策。研究人员构建了一个联合优化问题，旨在在有限的计算与存储资源下，最小化端到端任务执行延迟并提升缓存利用率。为高效求解该问题，研究人员设计了威胁感知联合优化（TAGO）框架，该框架结合了用于自适应任务卸载的近端策略优化算法，以及由Frank-Wolfe算法推导的基于梯度的缓存更新方法，以捕捉时空服务流行度。仿真结果表明，与多种基准策略相比，所提方法可显著降低任务延迟并提升缓存效率，展现出其在安全高效的无人机辅助车载消费电子系统中的有效性。
英文摘要
Vehicular consumer electronics increasingly support computation-intensive and latency-sensitive services, imposing stringent efficiency, reliability, and security requirements on vehicular edge computing (VEC) systems. In dynamic vehicular environments, inference-based information leakage and anomalous communication behaviors further threaten system performance and data privacy. To address these challenges, this paper proposes a UAV-assisted cooperative VEC architecture that integrates threat-aware task offloading with intelligent spatiotemporal caching across roadside units (RSUs) and UAV edge nodes. A security-aware uplink transmission model is developed to capture potential information leakage risks and abnormal communication patterns, enabling adaptive offloading decisions. We formulate a joint optimization problem to minimize end-to-end task execution delay while improving cache utilization under limited computing and storage resources. To efficiently solve this problem, a Threat-Aware Joint Optimization (TAGO) framework is designed by combining proximal policy optimization for adaptive task offloading and a gradient-based caching update derived from the Frank-Wolfe algorithm to capture spatiotemporal service popularity. Simulation results demonstrate that the proposed approach significantly reduces task delay and improves cache efficiency compared with several baseline strategies, showing its effectiveness for secure and efficient UAV-assisted vehicular consumer electronics systems.
Comments12 pages, 8 figures
