Abstract
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.
Abstract (translated)
无人机（UAVs）在农业、监视和物流等各个领域中至关重要，得益于5G技术的进步。然而，现有的研究缺乏一种全面的方法来解决数据新鲜度和安全问题。在本文中，我们解决了数据新鲜度和安全问题，尤其是在现代UAV网络中被窃听和干扰的背景下。我们的框架引入了指数化的自适应优化度指标，并着重于保密率来解决窃听和干扰威胁。我们引入了一种基于Transformer的深度强化学习（DRL）方法来优化任务卸载过程。与现有算法进行的比较分析显示，我们的方案具有优越性，表明其在UAV网络管理方面的潜在进展。
URL
https://arxiv.org/abs/2404.04692
