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Unmanned aerial vehicle (UAV)-based networks and Internet of Things (IoT) arebeing considered as integral components of current and next-generation wirelessnetworks.  In particular, UAVs can provide IoT devices with seamless connectivity andhigh coverage and this can be accomplished with effective UAV path planning.  In this article, we study robust and decentralized UAV path planning for datacollection in IoT networks in the presence of other noncooperative UAVs andadversarial jamming attacks. We address three different practical scenarios,including single UAV path planning, UAV swarm path planning, and single UAVpath planning in the presence of an intelligent mobile UAV jammer. We advocatea reinforcement learning framework for UAV path planning in these threescenarios under practical constraints. The simulation results demonstrate thatwith learning-based path planning, the UAVs can complete their missions withhigh success rates and data collection rates. In addition, the UAVs can adaptand execute different trajectories as a defensive measure against theintelligent jammer.

Robust and Decentralized Reinforcement Learning for UAV Path Planning in IoT Networks