The unmanned aerial vehicle (UAV) network is popular these years due to its
various applications. In the UAV network, routing is significantly affected by
the distributed network topology, leading to the issue that UAVs are vulnerable
to deliberate damage. Hence, this paper focuses on the routing plan and
recovery for UAV networks with attacks. In detail, a deliberate attack model
based on the importance of nodes is designed to represent enemy attacks. Then,
a node importance ranking mechanism is presented, considering the degree of
nodes and link importance. However, it is intractable to handle the routing
problem by traditional methods for UAV networks, since link connections change
with the UAV availability. Hence, an intelligent algorithm based on
reinforcement learning is proposed to recover the routing path when UAVs are
attacked. Simulations are conducted and numerical results verify the proposed
mechanism performs better than other referred methods.

该研究论文重点研究了受到攻击的无人机网络的路由方案和恢复问题，通过设计基于节点重要性的攻击模型和节点重要性排名机制，利用基于强化学习的智能算法来处理当无人机受到攻击时的路由路径恢复问题，并通过模拟实验验证了该机制优于其他参考方法。