面向无人机自组网的多信道自适应节点度差分簇算法OA
Multi-channel clustering algorithm with adaptive node degree difference for UAV ad hoc networks
为解决在频谱竞争激烈或者电磁环境多变的场景下,无人机节点因可用信道差异面临在同一信道上组网困难的问题,提出一种基于自适应节点度差的多信道无人机自组网分簇算法.该算法在基于模块度优化的层次聚类算法基础上,将面向多信道的自适应节点度差引入节点相似度计算中,通过最大化网络模块度函数对大规模无人机节点进行分簇,并基于Bianchi模型对网络吞吐量进行分析.仿真结果表明,所提算法相比Fast Unfolding、JS_CNC和HVC_MCNC等算法能形成分簇更均衡的拓扑结构,有效提升了网络吞吐量.
To address the difficulties of UAV(unmanned aerial vehicle)nodes in networking on the same channel due to differences in available channels,especially under conditions of intense spectrum competition or dynamic electromagnetic environments,a multi-channel UAV ad hoc networks clustering algorithm based on adaptive node degree difference was proposed.Implemented a hierarchical clustering approach optimized for modularity,the algorithm calculated node similarity by including adaptive node degree difference under multi-channel conditions.The network modularity function was maximized to cluster large-scale UAV nodes,and network throughput was analyzed using the Bianchi model.Simulation results demonstrate that the proposed algorithm not only achieves a more balanced topology but efficiently increases network throughput compared to the Fast Unfolding,JS_CNC,and HVC_MCNC algorithms.
张力丹;王海军;张姣;马东堂;周力;魏急波
国防科技大学 电子科学学院,湖南长沙 410073国防科技大学 电子科学学院,湖南长沙 410073国防科技大学 电子科学学院,湖南长沙 410073国防科技大学 电子科学学院,湖南长沙 410073国防科技大学 电子科学学院,湖南长沙 410073国防科技大学 电子科学学院,湖南长沙 410073
信息技术与安全科学
大规模网络多信道无人机自组网分簇算法自适应节点度差
large-scale networksmulti-channelunmanned aerial vehicle ad hoc networksclustering algorithmadaptive node degree difference
《国防科技大学学报》 2026 (4)
97-106,10
国家自然科学基金资助项目(62371462,61372099)博士后创新人才支持计划资助项目(BX20240493)
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