Skip to main content

A large-scale clustering and 3D trajectory optimization approach for UAV swarms


With the significant development of unmanned aerial vehicles (UAVs) technologies, a rapid increase on the use of UAV swarms in a wide range of civilian and emergency applications has been witnessed. However, how to efficiently network the large-scale UAVs and implement the swarms applications without infrastructure support in remote areas is challenging. In this paper, we investigate a hierarchal large-scale infrastructure-less UAV swarm scenario, where numerous UAVs surveil and collect data from the ground and a ferry UAV (Ferry UAV) is designated to carry back all their collected data. We can divide UAV swarms into different areas based on their geographic locations due to the wide range of surveillance. To improve data collection efficiency of Ferry UAV, we introduce a single super cluster head (Super-CH) UAV in each area which can be selected by the proposed modified k-means clustering algorithm with low latency. Then, we design an iterative approach to optimize the 3-dimensional (3D) trajectory of Ferry UAV such that its data collection mission completion time is minimized. Numerical results show the efficiency and low-latency of the proposed clustering algorithm, and the proposed 3D optimal trajectory design for large-scale UAV swarms data collection admits better performance than that with fixed altitude.

This is a preview of subscription content, access via your institution.


  1. Boccardi F, Heath R W, Lozano A, et al. Five disruptive technology directions for 5G. IEEE Commun Mag, 2014, 52: 74–80

    Article  Google Scholar 

  2. Zhou H B, Wu Y M, Hu Y Q, et al. A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks. Comput Commun, 2010, 33: 1843–1849

    Article  Google Scholar 

  3. Ma T, Qian B, Niu D B, et al. A gradient-based method for robust sensor selection in hypothesis testing. Sensors, 2020, 20: 697

    Article  Google Scholar 

  4. Shi W S, Zhou H B, Li J L, et al. Drone assisted vehicular networks: architecture, challenges and opportunities. IEEE Netw, 2018, 32: 130–137

    Article  Google Scholar 

  5. Cheng N, Xu W C, Shi W S, et al. Air-ground integrated mobile edge networks: architecture, challenges, and opportunities. IEEE Commun Mag, 2018, 56: 26–32

    Article  Google Scholar 

  6. Shi W S, Li J L, Xu W C, et al. Multiple drone-cell deployment analyses and optimization in drone assisted radio access networks. IEEE Access, 2018, 6: 12518–12529

    Article  Google Scholar 

  7. Zhang S H, Zhang H L, Di B Y, et al. Cellular UAV-to-X communications: design and optimization for multi-UAV networks. IEEE Trans Wirel Commun, 2019, 18: 1346–1359

    Article  Google Scholar 

  8. Tuna G, Nefzi B, Conte G. Unmanned aerial vehicle-aided communications system for disaster recovery. J Network Comput Appl, 2014, 41: 27–36

    Article  Google Scholar 

  9. Zeng Y, Wu Q Q, Zhang R. Accessing from the sky: a tutorial on UAV communications for 5G and beyond. Proc IEEE, 2019, 107: 2327–2375

    Article  Google Scholar 

  10. Matolak D W, Sun R Y. Unmanned aircraft systems: air-ground channel characterization for future applications. IEEE Veh Technol Mag, 2015, 10: 79–85

    Article  Google Scholar 

  11. Khawaja W, Guvenc I, Matolak D W, et al. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Commun Surv Tut, 2019, 21: 2361–2391

    Article  Google Scholar 

  12. Shi W S, Li J L, Cheng N, et al. Multi-drone 3-D trajectory planning and scheduling in drone-assisted radio access networks. IEEE Trans Veh Technol, 2019, 68: 8145–8158

    Article  Google Scholar 

  13. Zhao N, Lu W D, Sheng M, et al. UAV-assisted emergency networks in disasters. IEEE Wirel Commun, 2019, 26: 45–51

    Article  Google Scholar 

  14. Hayat S, Yanmaz E, Muzaffar R. Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun Surv Tut, 2016, 18: 2624–2661

    Article  Google Scholar 

  15. Bujari A, Palazzi C E, Ronzani D. FANET application scenarios and mobility models. In: Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, NewYork, 2017. 43–46

  16. Park J H, Choi S-C, Hussen H R, et al. Analysis of dynamic cluster head selection for mission-oriented flying ad hoc network. In: Proceedings of the 9th International Conference on Ubiquitous and Future Networks (ICUFN), Milan, 2017. 21–23

  17. Du J M, You Q D, Zhang Q, et al. A weighted clustering algorithm based on node stability for ad hoc networks. In: Proceedings of the 16th International Conference on Optical Communications and Networks (ICOCN), Wuzhen, 2017

  18. Fahad M, Aadil F, Rehman Z, et al. Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput Electrical Eng, 2018, 70: 853–870

    Article  Google Scholar 

  19. Khan A, Aftab F, Zhang Z. BICSF: bio-inspired clustering scheme for FANETs. IEEE Access, 2019, 7: 31446–31456

    Article  Google Scholar 

  20. Aadil F, Raza A, Khan M, et al. Energy aware cluster-based routing in flying Ad-Hoc networks. Sensors, 2018, 18: 1413

    Article  Google Scholar 

  21. Ali H, Shahzad W, Khan F A. Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Appl Soft Comput, 2012, 12: 1913–1928

    Article  Google Scholar 

  22. Zhu X P, Bian C J, Chen Y, et al. A low latency clustering method for large-scale drone swarms. IEEE Access, 2019, 7: 186260–186267

    Article  Google Scholar 

  23. Zeng Y, Zhang R, Lim T J. Throughput maximization for UAV-enabled mobile relaying systems. IEEE Trans Commun, 2016, 64: 4983–4996

    Article  Google Scholar 

  24. Wu Q Q, Zhang R. Common throughput maximization in UAV-enabled OFDMA systems with delay consideration. IEEE Trans Commun, 2018, 66: 6614–6627

    Article  Google Scholar 

  25. Zhang G C, Wu Q Q, Cui M, et al. Securing UAV communications via joint trajectory and power control. IEEE Trans Wirel Commun, 2019, 18: 1376–1389

    Article  Google Scholar 

  26. Jeong S, Simeone O, Kang J. Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Trans Veh Technol, 2018, 67: 2049–2063

    Article  Google Scholar 

  27. Wu Q Q, Zeng Y, Zhang R. Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans Wirel Commun, 2018, 17: 2109–2121

    Article  Google Scholar 

  28. Zhang G C, Wu Q Q, Cui M, et al. Securing UAV communications via trajectory optimization. In: Proceedings of IEEE Global Communications Conference, Singapore, 2017

  29. Zhang J W, Zeng Y, Zhang R. UAV-enabled radio access network: multi-mode communication and trajectory design. IEEE Trans Signal Process, 2018, 66: 5269–5284

    MathSciNet  Article  Google Scholar 

  30. Zhang J, Zhou L, Zhou F H, et al. Computation-efficient offloading and trajectory scheduling for multi-UAV assisted mobile edge computing. IEEE Trans Veh Technol, 2020, 69: 2114–2125

    Article  Google Scholar 

  31. Mahajan M, Nimbhorkar P, Kasturi R. The planar k-means problem is NP-hard. Theor Comput Sci, 2009, 442: 274–285

    MathSciNet  MATH  Google Scholar 

  32. Matolak D W, Sun R. Air-ground channel characterization for unmanned aircraft systems-part I: methods, measurements, and models for over-water settings. IEEE Trans Veh Technol, 2017, 66: 26–44

    Article  Google Scholar 

  33. Grant M, Boyd S, Ye Y. CVX Toolbox. Redwood City: Stanford University Press, 2009

    Google Scholar 

  34. Qian B, Zhou H B, Lyu F, et al. Toward collision-free and efficient coordination for automated vehicles at unsignalized intersection. IEEE Int Things J, 2019, 6: 10408–10420

    Article  Google Scholar 

  35. Laporte G. The traveling salesman problem: an overview of exact and approximate algorithms. Eur J Oper Res, 1992, 59: 231–247

    Article  Google Scholar 

Download references


This work was supported in part by National Natural Science Foundation of China (Grant No. 61871211), Natural Science Foundation of Jiangsu Province Youth Project (Grant No. BK20180329), Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program, Summit of the Six Top Talents Program of Jiangsu Province.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Haibo Zhou.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, T., Zhou, H., Qian, B. et al. A large-scale clustering and 3D trajectory optimization approach for UAV swarms. Sci. China Inf. Sci. 64, 140306 (2021).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI:


  • large-scale UAV swarms
  • clustering
  • super-CH selection
  • 3D trajectory design