Routing Recovery for UAV Networks with Deliberate Attacks: A
  Reinforcement Learning based Approach
Sijie He Ziye Jia Chao Dong Wei Wang Yilu Cao Yang Yang Qihui Wu
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.