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.
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Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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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)
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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.
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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
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DOI: https://doi.org/10.1007/s11036-024-02325-9