Skip to main content

Advertisement

Log in

Broad Learning System Routing to Mitigate the Impact of Dynamic Changing Topology for 3D Flying Ad Hoc Networks

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

As unmanned aerial vehicles (UAVs) achieve technological breakthroughs, it is an inevitable trend for UAV swarms to engage in increasingly larger-scale, prolonged, and collaborative missions. Nevertheless, the maneuverability of UAVs leads to highly dynamic networks. Therefore, we have to consider the practical requirements for these 3D application scenarios. To mitigate the impact of dynamic changing topology for 3D flying ad hoc networks (FANETs), we propose broad learning system routing (BLSR). As the kernel of BLSR, the broad learning system is used to choose forwarding nodes because it can better adapt to dynamic environments due to its incremental learning. On the other hand, the residual multi-hop-link lifetime (RML) as an input of the broad learning system is defined by us as the maximum duration for all multi-hop links to the base station. Worth noting that, for each UAV, the multi-hop topologies in 3D dynamic scenarios may be analogous to static topologies during the RML. Lastly, some stable-link nodes near the base station are selected as backbone networks, which are usually chosen by other UAVs to forward data packets, thus expanding the stable-link topology of the base station. Compared to four protocols, BLSR demonstrates superior performance in terms of both packet delivery rate and energy efficiency ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Algorithm 2
Fig. 5
Fig. 6
Algorithm 3
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Chriki A, Touati H, Snoussi H, Kamoun F (2019) FANET: communication, mobility models and security issues. Compute Netw 163:106877. https://doi.org/10.1016/j.comnet.2019.106877

    Article  MATH  Google Scholar 

  2. Pasandideh F, da Costa JPJ, Kunst R, Islam N, Hardjawana W, Pignaton de Freitas E (2022) A review of flying ad hoc networks: key characteristics, applications, and wireless technologies. Remote Sens 14(18):4459. https://doi.org/10.3390/rs14184459

    Article  Google Scholar 

  3. Lu Y, Wen W, Igorevich KK, Ren P, Zhang H, Duan Y, Zhu H, Zhang P (2023) UAV ad hoc network routing algorithms in space–air–ground integrated networks: challenges and directions. Drones 7(7):448. https://doi.org/10.3390/drones7070448

    Article  MATH  Google Scholar 

  4. Srivastava A, Prakash J (2021) Future FANET with application and enabling techniques: anatomization and sustainability issues. Comput Sci Rev 39:100359. https://doi.org/10.1016/j.cosrev.2020.100359

    Article  MathSciNet  MATH  Google Scholar 

  5. Mansoor N, Hossain MI, Rozario A, Zareei M, Arreola AR (2023) A fresh look at routing protocols in unmanned aerial vehicular networks: a survey. IEEE Access 11:66289–66308. https://doi.org/10.1109/access.2023.3290871

    Article  Google Scholar 

  6. Bekmezci Í, Sahingoz OK, Temel Ş (2013) Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw 11(3):1254–1270. https://doi.org/10.1016/j.adhoc.2012.12.004

    Article  Google Scholar 

  7. Tan Y, Zheng ZY (2013) Research advance in swarm robotics. Def Technol 9(1):18–39. https://doi.org/10.1016/j.dt.2013.03.001

    Article  MATH  Google Scholar 

  8. Bujari A, Palazzi CE, Ronzani D (2018) A comparison of stateless position-based packet routing algorithms for FANETs. IEEE Trans Mob Comput 17(11):2468–2482. https://doi.org/10.1109/tmc.2018.2811490

    Article  MATH  Google Scholar 

  9. Khan MA, Safi A, Qureshi IM, Khan IU (2017) Flying ad-hoc networks (FANETs): a review of communication architectures, and routing protocols. In: 2017 First International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), pp 1–9. https://doi.org/10.1109/intellect.2017.8277614

  10. Malhotra A, Kaur S (2019) A comprehensive review on recent advancements in routing protocols for flying ad hoc networks. Trans Emerg Telecommun Technol 33(3):e3688. https://doi.org/10.1002/ett.3688

    Article  MATH  Google Scholar 

  11. Tortonesi M, Stefanelli C, Benvegnu E, Ford K, Suri N, Linderman M (2012) Multiple-UAV coordination and communications in tactical edge networks. IEEE Commmun Mag 50(10):48–55. https://doi.org/10.1109/mcom.2012.6316775

    Article  Google Scholar 

  12. Shakhatreh H, Sawalmeh A, Al-Fuqaha A, Dou ZC, Almaita E, Khalil I, Othman NS, Khreishah A, Guizani M (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572–48634. https://doi.org/10.1109/access.2019.2909530

    Article  Google Scholar 

  13. Lakew DS, Sa’ad U, Dao NN, Na W, Cho S (2020) Routing in flying ad hoc networks: a comprehensive survey. IEEE Commun Surv Tutorials 22(2):1071–1120. https://doi.org/10.1109/comst.2020.2982452

    Article  Google Scholar 

  14. Zhang H, You M, Liu T, Zhang Q, Liu Y (2024) Variable cluster routing for large-scale human-robot collaboration systems-a top-down cluster routing. IEEE Trans Veh Technol. https://doi.org/10.1109/tvt.2024.3353799

    Article  MATH  Google Scholar 

  15. Zhang Y, Liu T, Zhang H, Liu Y (2020) LEACH-R: LEACH relay with cache strategy for mobile robot swarms. IEEE Wirel Commun Lett 10(2):406–410. https://doi.org/10.1109/lwc.2020.3033039

    Article  MATH  Google Scholar 

  16. Cai H, Zhang Y, Yan H, Shen F, Zhou K, Zhang C (2016) A delay-aware wireless sensor network routing protocol for industrial applications. Mob Netw Appl 21(5):879–889. https://doi.org/10.1007/s11036-016-0707-7

    Article  MATH  Google Scholar 

  17. Yoshihiro T, Araki D, Sakaguchi H, Shibata N (2018) Providing reliable communications over static-node-assisted vehicular networks using distance-vector routing. Mob Netw Appl 23:1376–1393. https://doi.org/10.1007/s11036-018-1025-z

    Article  Google Scholar 

  18. Jung WS, Yim J, Ko YB (2017) QGeo: Q-learning-based geographic ad hoc routing protocol for unmanned robotic networks. IEEE Commun Lett 21(10):2258–2261. https://doi.org/10.1109/lcomm.2017.2656879

    Article  MATH  Google Scholar 

  19. Jin W, Gu R, Ji Y (2019) Reward function learning for Q-learning-based geographic routing protocol. IEEE Commun Lett 23(7):1236–1239. https://doi.org/10.1109/lcomm.2019.2913360

    Article  MATH  Google Scholar 

  20. Ghasemi M, Abdolahi M, Bag-Mohammadi M, Bohlooli A (2015) Adaptive multi-flow opportunistic routing using learning automata. Ad Hoc Netw 25:472–479. https://doi.org/10.1016/j.adhoc.2014.08.013

    Article  MATH  Google Scholar 

  21. Ghasemnezhad S, Ghaffari A (2018) Fuzzy logic based reliable and real-time routing protocol for mobile ad hoc networks. Wirel Pers Commun 98(1):593–611. https://doi.org/10.1007/s11277-017-4885-9

    Article  MATH  Google Scholar 

  22. Fu J, Cui B, Wang N, Liu X (2019) A distributed position-based routing algorithm in 3-D wireless industrial internet of things. IEEE Trans Ind Inf 15(10):5664–5673. https://doi.org/10.1109/tll.2019.2908439

    Article  MATH  Google Scholar 

  23. Abdallah AE, Fevens T, Opatrny J (2008) High delivery rate position-based routing algorithms for 3D ad hoc networks. Comput Commun 31(4):807–817. https://doi.org/10.1016/j.comcom.2007.10.037

    Article  MATH  Google Scholar 

  24. Abdallah AE (2018) Low overhead hybrid geographic-based routing algorithms with smart partial flooding for 3D ad hoc networks. J Amb Intell Hum Comput 9(1):85–94. https://doi.org/10.1007/s12652-017-0528-y

    Article  MATH  Google Scholar 

  25. Sun Y, Zheng M, Han X, Li S, Yin J (2022) Adaptive clustering routing protocol for underwater sensor networks. Ad Hoc Netw 136:102953. https://doi.org/10.1016/j.adhoc.2022.102953

    Article  MATH  Google Scholar 

  26. Hao K, Shen H, Liu Y, Wang B, Du X (2018) Integrating localization and energy-awareness: a novel geographic routing protocol for underwater wireless sensor networks. Mob Netw Appl 23:1427–1435. https://doi.org/10.1007/s11036-018-1093-0

    Article  MATH  Google Scholar 

  27. Swidan A, Khattab S, Abouelseoud Y, Elkamchouchi H (2015) A secure geographical routing protocol for highly-dynamic aeronautical networks. In: 2015 IEEE Military Communications Conference, pp 708–713. https://doi.org/10.1109/milcom.2015.7357527

  28. Zheng Z, Sangaiah AK, Wang T (2018) Adaptive communication protocols in flying ad hoc network. IEEE Commun Mag 56(1):136–142. https://doi.org/10.1109/mcom.2017.1700323

    Article  MATH  Google Scholar 

  29. Hussen HR, Choi SC, Park JH, Kim J (2019) Predictive geographic multicast routing protocol in flying ad hoc networks. Int J Distrib Sens Netw 15(7):1–20. https://doi.org/10.1177/1550147719843879

    Article  Google Scholar 

  30. Yang S, Li TL, Wu D, Hu T, Deng W, Gong H (2024) Bio-inspired multi-hop clustering algorithm for FANET. Ad Hoc Netw 154:103355. https://doi.org/10.1016/j.adhoc.2023.103355

    Article  Google Scholar 

  31. Usman Q, Chughtai O, Nawaz N, Kaleem Z, Khaliq KA, Nguyen LD (2021) A reliable link-adaptive position-based routing protocol for flying ad hoc network. Mob Netw Appl 26:1801–1820. https://doi.org/10.1007/s11036-021-01758-w

    Article  Google Scholar 

  32. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670. https://doi.org/10.1109/twc.2002.804190

    Article  MATH  Google Scholar 

  33. Chen CLP, Liu Z (2018) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst 29(1):10–24. https://doi.org/10.1109/tnnls.2017.2716952

    Article  MathSciNet  MATH  Google Scholar 

  34. Gong X, Zhang T, Chen CLP, Liu Z (2022) Research review for broad learning system: algorithms, theory, and applications. IEEE Trans Cybern 52(9):8922–8950. https://doi.org/10.1109/TCYB.2021.3061094

    Article  MATH  Google Scholar 

  35. Gankhuyag G, Shrestha AP, Yoo SJ (2017) Robust and reliable predictive routing strategy for flying ad-hoc networks. IEEE Access 5:643–654. https://doi.org/10.1109/access.2017.2647817

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 62376036) and Research on Construction and Implementation of the Integrated Model for Artificial Intelligence Ethics Education in Universities, Middle schools and Primary schools of the Capital (Grant No. CDAA23046)

Author information

Authors and Affiliations

Authors

Contributions

H.Z.: Conceptualization, Methodology, Funding acquisition, Writing-review & editing. L.C.: Software, Visualization, Writing - original draft. S.M.: Software, Investigation, Writing - original draft. P.Z.: Investigation, Visualization, Writing - original draft. H.Z.: Investigation, Visualization. Y.L.: Validation.

Corresponding author

Correspondence to Hongguang Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

Not applicable.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Chen, L., Ma, S. et al. Broad Learning System Routing to Mitigate the Impact of Dynamic Changing Topology for 3D Flying Ad Hoc Networks. Mobile Netw Appl 29, 841–855 (2024). https://doi.org/10.1007/s11036-024-02325-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-024-02325-9

Keywords

Navigation