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Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework (2405.11281v1)

Published 18 May 2024 in cs.DC and cs.AI

Abstract: As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals. However, how to manage such complex systems to ensure real-time adaptability lack sufficient researches. Hence, in this paper, we propose the cooperative cognitive dynamic system (CCDS), to optimize the management for UAV swarms. CCDS leverages a hierarchical and cooperative control structure that enables real-time data processing and decision. Accordingly, CCDS optimizes the UAV swarm management via dynamic reconfigurability and adaptive intelligent optimization. In addition, CCDS can be integrated with the biomimetic mechanism to efficiently allocate tasks for UAV swarms. Further, the distributed coordination of CCDS ensures reliable and resilient control, thus enhancing the adaptability and robustness. Finally, the potential challenges and future directions are analyzed, to provide insights into managing UAV swarms in dynamic heterogeneous networking.

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References (15)
  1. T. Bouzid, N. Chaib, M. L. Bensaad, and O. S. Oubbati, “5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions,” T EMERG TELECOMMUN T, vol. 34, no. 3, p. 4721, Dec. 2023.
  2. K. Messaoudi, O. S. Oubbati, A. Rachedi, A. Lakas, T. Bendouma, and N. Chaib, “A survey of UAV-based data collection: Challenges, solutions and future perspectives,” J NETW COMPUT APPL, vol. 216, p. 103670, May 2023.
  3. T. Do-Duy, L. D. Nguyen, T. Q. Duong, S. R. Khosravirad, and H. Claussen, “Joint optimisation of real-time deployment and resource allocation for UAV-aided disaster emergency communications,” IEEE J. Sel. Areas Commun., vol. 39, no. 11, pp. 3411–3424, Dec. 2021.
  4. W. Wu, F. Zhou, B. Wang, Q. Wu, C. Dong, and R. Q. Hu, “Unmanned aerial vehicle swarm-enabled edge computing: Potentials, promising technologies, and challenges,” IEEE Wireless Commun., vol. 29, no. 4, pp. 78–85, Aug. 2022.
  5. B. Fei, W. Bao, X. Zhu, D. Liu, T. Men, and Z. Xiao, “Autonomous cooperative search model for multi-UAV with limited communication network,” IEEE Internet Things J., vol. 9, no. 19, pp. 19 346–19 361, Oct. 2022.
  6. N. Zhao, Z. Ye, Y. Pei, Y.-C. Liang, and D. Niyato, “Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing,” IEEE Trans. Wirel. Commun., vol. 21, no. 9, pp. 6949–6960, Sep. 2022.
  7. J. Wu, C. Luo, Y. Luo, and K. Li, “Distributed UAV swarm formation and collision avoidance strategies over fixed and switching topologies,” IEEE Trans Cybern, vol. 52, no. 10, pp. 1525–1539, Oct. 2022.
  8. Y. Bai, H. Zhao, X. Zhang, Z. Chang, R. Jäntti, and K. Yang, “Towards autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches,” IEEE Commun. Surv. Tutor., Oct. 2023, early access.
  9. L. Lei, G. Shen, L. Zhang, and Z. Li, “Toward intelligent cooperation of UAV swarms: When machine learning meets digital twin,” IEEE Netw., vol. 35, no. 1, pp. 386–392, Feb. 2021.
  10. J. Chen, C. Du, Y. Zhang, P. Han, and W. Wei, “A clustering-based coverage path planning method for autonomous heterogeneous UAVs,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 25 546–25 556, Dec. 2022.
  11. W. Xiao, M. Li, B. Alzahrani, R. Alotaibi, A. Barnawi, and Q. Ai, “A blockchain-based secure crowd monitoring system using UAV swarm,” IEEE Netw., vol. 35, no. 1, pp. 108–115, Feb. 2021.
  12. G. Bansal, V. Chamola, N. Ansari, and B. Sikdar, “Scalable topologies for time-optimal authentication of UAV swarms,” IEEE Netw., vol. 36, no. 6, pp. 126–132, Nov. 2022.
  13. B. Xin, Y. Kang, and W. Liu, “Tracking-oriented formation control for unmanned aerial vehicle swarm through output feedback under jointly-connected topologies,” IEEE Trans. Veh. Technol., Sep. 2023, early access.
  14. W. Hilal, S. A. Gadsden, and J. Yawney, “Cognitive dynamic systems: A review of theory, applications, and recent advances,” Proc IEEE Inst Electr Electron Eng, vol. 111, no. 6, pp. 575–622, Jun. 2023.
  15. S. Haykin, P. Setoodeh, S. Feng, and D. Findlay, “Cognitive dynamic system as the brain of complex networks,” IEEE J. Sel. Areas Commun., vol. 34, no. 10, pp. 2791–2800, Sep. 2016.
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Authors (7)
  1. Ziye Jia (28 papers)
  2. Jiahao You (6 papers)
  3. Chao Dong (168 papers)
  4. Qihui Wu (91 papers)
  5. Fuhui Zhou (72 papers)
  6. Dusit Niyato (671 papers)
  7. Zhu Han (431 papers)
Citations (1)

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