Unmanned Aerial Vehicles (UAVs) are integral in various sectors like agriculture, surveillance, and logistics, driven by advancements in 5G. However, existing research lacks a comprehensive approach addressing both data freshness and security concerns. In this paper, we address the intricate challenges of data freshness, and security, especially in the context of eavesdropping and jamming in modern UAV networks. Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats. We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes. Comparative analysis with existing algorithms showcases the superiority of our scheme, indicating its promising advancements in UAV network management.

翻译：无人机在农业、监控和物流等领域因5G技术的进步而发挥着重要作用。然而，现有研究缺乏同时兼顾数据新鲜度与安全性的综合方案。本文针对现代无人机网络中窃听与干扰场景下的数据新鲜度及安全挑战展开研究。所提框架引入指数型信息年龄指标，并着重通过保密率应对窃听与干扰威胁。我们提出一种基于Transformer增强的深度强化学习方法以优化任务卸载流程。与现有算法的对比分析表明，本方案具有显著优势，为无人机网络管理展现了前景可期的发展方向。