Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\% and improves the TSR by 29\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.

翻译：由于可扩展性和便携性优势，低空智能网络（LAINs）在监控与灾难救援等领域具有重要作用。然而，LAINs中的无人机（UAVs）具有分布式拓扑与高移动性特征，易受安全威胁影响，可能导致数据传输的路由性能下降。因此，如何保障LAINs的路由稳定性与安全性成为关键挑战。本文聚焦于LAINs中多无人机集群的路由问题。为最小化潜在威胁造成的损害，我们提出融合软件定义边界与区块链技术的零信任架构，以管理无人机的身份与移动性。此外，我们将路由问题建模为同时优化端到端（E2E）时延与传输成功率（TSR）的整数非线性规划问题，该问题求解困难。为此，我们将问题重构为去中心化部分可观测马尔可夫决策过程，设计了基于多智能体双深度Q网络的路由算法，并采用软分层经验回放缓冲区与优先经验回放机制进行增强。最终，大量仿真实验表明：相较于基准方案，所提框架平均降低59%的E2E时延并提升29%的TSR，同时能更快速、更鲁棒地识别低可信度无人机。