Resilient UAV Path Planning for Data Collection under Adversarial Attacks

Xueyuan Wang, M. Cenk Gursoy

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations


In this paper, we investigate jamming-resilient unmanned aerial vehicle (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 an intelligent UAV jammer, which utilizes reinforcement learning 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 the intelligent jamming attack has great influence on the UAV's performance, and the proposed defense strategy can recover the performance close to that in no-jammer scenarios.

Original languageEnglish (US)
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538683477
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: May 16 2022May 20 2022

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of


  • IoT networks
  • UAV path planning
  • jamming attack
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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