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Resilient Path Planning for UAVs in Data Collection under Adversarial Attacks (2401.08634v1)

Published 11 Dec 2023 in cs.NI and eess.SP

Abstract: 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.

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References (33)
  1. Y. Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UAV communications for 5G and beyond,” Proceedings of the IEEE, vol. 107, no. 12, pp. 2327–2375, 2019.
  2. F. Syed, S. K. Gupta, S. Hamood Alsamhi, M. Rashid, and X. Liu, “A survey on recent optimal techniques for securing unmanned aerial vehicles applications,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, p. e4133, 2021.
  3. M. Mozaffari, W. Saad, M. Bennis, Y. Nam, and M. Debbah, “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys Tutorials, pp. 1–1, 2019.
  4. S. Goudarzi, N. Kama, M. H. Anisi, S. Zeadally, and S. Mumtaz, “Data collection using unmanned aerial vehicles for Internet of Things platforms,” Computers & Electrical Engineering, vol. 75, pp. 1–15, 2019.
  5. S. H. Alsamhi, O. Ma, M. S. Ansari, and F. A. Almalki, “Survey on collaborative smart drones and Internet of Things for improving smartness of smart cities,” IEEE Access, vol. 7, pp. 128 125–128 152, 2019.
  6. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications,” IEEE Transactions on Wireless Communications, vol. 16, no. 11, pp. 7574–7589, 2017.
  7. N. C. Coops, T. R. Goodbody, and L. Cao, “Four steps to extend drone use in research,” 2019.
  8. D. Wang, B. Bai, W. Zhao, and Z. Han, “A survey of optimization approaches for wireless physical layer security,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1878–1911, 2018.
  9. B. Duan, D. Yin, Y. Cong, H. Zhou, X. Xiang, and L. Shen, “Anti-jamming path planning for unmanned aerial vehicles with imperfect jammer information,” in 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2018, pp. 729–735.
  10. B. Duo, Q. Wu, X. Yuan, and R. Zhang, “Anti-jamming 3D trajectory design for UAV-enabled wireless sensor networks under probabilistic LoS channel,” IEEE Transactions on Vehicular Technology, 2020.
  11. D. Darsena, G. Gelli, I. Iudice, and F. Verde, “Detection and blind channel estimation for UAV-aided wireless sensor networks in smart cities under mobile jamming attack,” IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11 932–11 950, 2022.
  12. Y. Wu, W. Fan, W. Yang, X. Sun, and X. Guan, “Robust trajectory and communication design for multi-UAV enabled wireless networks in the presence of jammers,” IEEE Access, vol. 8, pp. 2893–2905, 2019.
  13. Y. Gao, Y. Wu, Z. Cui, H. Chen, and W. Yang, “Robust design for turning and climbing angle-constrained UAV communication under malicious jamming,” IEEE Communications Letters, vol. 25, no. 2, pp. 584–588, 2020.
  14. Y. Wu, W. Yang, X. Guan, and Q. Wu, “UAV-enabled relay communication under malicious jamming: Joint trajectory and transmit power optimization,” IEEE Transactions on Vehicular Technology, 2021.
  15. H. Wang, J. Chen, G. Ding, and J. Sun, “Trajectory planning in UAV communication with jamming,” in 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP).   IEEE, 2018, pp. 1–6.
  16. Y. Wu, X. Guan, W. Yang, and Q. Wu, “UAV swarm communication under malicious jamming: Joint trajectory and clustering design,” IEEE Wireless Communications Letters, vol. 10, no. 10, pp. 2264–2268, 2021.
  17. C. Han, A. Liu, K. An, H. Wang, G. Zheng, S. Chatzinotas, L. Huo, and X. Tong, “Satellite-assisted UAV trajectory control in hostile jamming environments,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3760–3775, 2021.
  18. Z. Lin, X. Lu, C. Dai, G. Sheng, and L. Xiao, “Reinforcement learning based UAV trajectory and power control against jamming,” in International Conference on Machine Learning for Cyber Security.   Springer, 2019, pp. 336–347.
  19. S. Bhattacharya and T. Başar, “Game-theoretic analysis of an aerial jamming attack on a UAV communication network,” in Proceedings of the 2010 American Control Conference.   IEEE, 2010, pp. 818–823.
  20. Y. Xu, G. Ren, J. Chen, Y. Luo, L. Jia, X. Liu, Y. Yang, and Y. Xu, “A one-leader multi-follower Bayesian-Stackelberg game for anti-jamming transmission in UAV communication networks,” IEEE Access, vol. 6, pp. 21 697–21 709, 2018.
  21. C. Li, Y. Xu, J. Xia, and J. Zhao, “Protecting secure communication under UAV smart attack with imperfect channel estimation,” IEEE Access, vol. 6, pp. 76 395–76 401, 2018.
  22. L. Xiao, C. Xie, M. Min, and W. Zhuang, “User-centric view of unmanned aerial vehicle transmission against smart attacks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3420–3430, 2017.
  23. N. Gao, Z. Qin, X. Jing, Q. Ni, and S. Jin, “Anti-intelligent UAV jamming strategy via deep Q-networks,” IEEE Transactions on Communications, vol. 68, no. 1, pp. 569–581, 2019.
  24. Z. Li, Y. Lu, X. Li, Z. Wang, W. Qiao, and Y. Liu, “UAV networks against multiple maneuvering smart jamming with knowledge-based reinforcement learning,” IEEE Internet of Things Journal, 2021.
  25. K. Liu, P. Li, C. Liu, L. Xiao, and L. Jia, “UAV-aided anti-jamming maritime communications: a deep reinforcement learning approach,” in 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP).   IEEE, 2021, pp. 1–6.
  26. J. Peng, Z. Zhang, Q. Wu, and B. Zhang, “Anti-jamming communications in UAV swarms: A reinforcement learning approach,” IEEE Access, vol. 7, pp. 180 532–180 543, 2019.
  27. Z. Ji, J. Tu, X. Guan, W. Yang, W. Yang, and Q. Wu, “Energy efficient design in IRS-assisted UAV data collection system under malicious jamming,” arXiv preprint arXiv:2208.14751, 2022.
  28. X. Wang, M. C. Gursoy, T. Erpek, and Y. E. Sagduyu, “Learning-based UAV path planning for data collection with integrated collision avoidance,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16 663–16 676, 2022.
  29. J. Chen, D. Raye, W. Khawaja, P. Sinha, and I. Guvenc, “Impact of 3D UWB antenna radiation pattern on air-to-ground drone connectivity,” in 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Aug 2018, pp. 1–5.
  30. Y. F. Chen, M. Liu, M. Everett, and J. P. How, “Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning,” in 2017 IEEE international conference on robotics and automation (ICRA).   IEEE, 2017, pp. 285–292.
  31. M. Everett, Y. F. Chen, and J. P. How, “Collision avoidance in pedestrian-rich environments with deep reinforcement learning,” IEEE Access, vol. 9, pp. 10 357–10 377, 2021.
  32. J. Van Den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Robotics research.   Springer, 2011, pp. 3–19.
  33. Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, “Dueling network architectures for deep reinforcement learning,” in International conference on machine learning.   PMLR, 2016, pp. 1995–2003.
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