  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.
