Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
This addresses routing vulnerabilities in UAV networks for applications like surveillance or communication, but it is incremental as it builds on existing RL approaches.
The paper tackles routing recovery in UAV networks under deliberate attacks by proposing a reinforcement learning-based algorithm, which simulations show outperforms other methods.
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.