  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.
