Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks
Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks
复制标题
DOI：
10.1109/tifs.2023.3266699
复制
发表时间：
2023-12
影响因子：
6.8
通讯作者：
Xueyuan Wang;M. C. Gursoy
中科院分区：
文献类型：
--
作者：
Xueyuan Wang;M. C. Gursoy
在本文中，我们研究了干扰弹性无人机路径规划策略，用于物联网（IoT）网络中的数据收集，其中典型的无人机可以学习最佳轨迹来躲避这种干扰攻击。具体而言，典型的无人机需要在碰撞避免、使命完成期限和存在干扰攻击的运动学约束下从多个分布式物联网节点收集数据。首先设计了一种固定地面干扰机，采用连续干扰攻击和周期性干扰攻击策略，对典型无人机与物联网节点之间的链路进行干扰。提出了涉及基于强化学习（RL）的虚拟干扰器和采用更高的SINR阈值的防御策略来对抗此类攻击。其次，设计了一种智能无人机干扰机，该干扰机利用RL算法进行基于观测的干扰机动作选择。针对这种攻击，构造了一种智能无人机抗干扰策略，并通过决斗双深度Q网络（D3QN）得到典型无人机的最优航迹。仿真结果表明，非智能干扰和智能干扰对无人机的性能都有显著影响，所提出的防御策略可以使无人机的性能恢复到接近无干扰时的水平。
In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.