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

Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach