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

TL;DR

This paper proposes a reinforcement learning-based routing recovery method for UAV networks under deliberate attacks, utilizing a node importance ranking to adapt to dynamic topologies and improve resilience.

Contribution

It introduces a novel attack model and a reinforcement learning approach for routing recovery in UAV networks, addressing the challenge of dynamic link changes due to attacks.

Findings

Reinforcement learning outperforms traditional routing methods in attacked UAV networks.

Node importance ranking improves attack resilience.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

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.