Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANET routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive Q-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAV nodes via the queuing theory, and then the closed-form solutions of neighbors' change rate (NCR) and neighbors' change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive Q-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time at a low cost. The simulation results verify the accuracy of the topology dynamic analysis model and also prove that our TARRAQ outperforms the Q-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

翻译：飞行自组网（FANETs）在众多军事与民用应用中发挥着关键作用，因为与单个无人机（UAV）相比，它显著缩短了任务持续时间并提升了覆盖范围。然而，由于动态拓扑变化，设计一种兼具高分组投递率（PDR）和低延迟的节能型FANET路由协议颇具挑战。本文提出一种基于自适应Q学习的拓扑感知弹性路由策略（TARRAQ），以低开销准确捕获拓扑变化，并以分布式自主方式做出路由决策。首先，我们通过排队论分析无人机节点的动态行为，进而推导出邻居变化率（NCR）和邻居变化到达间隔时间（NCIT）分布的闭式解。基于实时的NCR和NCIT，通过定义网络事件的期望感知延迟来确定弹性感知间隔（SI）。此外，我们还提出一种自适应Q学习方法，使无人机能够做出分布式、自主且自适应的路由决策，其中上述SI确保了动作空间能够以低成本及时更新。仿真结果验证了拓扑动态分析模型的准确性，并证明我们的TARRAQ相比基于Q学习的拓扑感知路由（QTAR）、基于移动预测的虚拟路由（MPVR）和基于节能Hello的贪婪周边无状态路由（EE-Hello），在开销上分别降低25.23%、20.24%和13.73%，PDR分别提高9.41%、14.77%和16.70%，能量消耗分别降低5.12%、15.65%和11.31%。