The flying ad hoc networks (FANETs) have attracted a large amount of attention from both academia and industry. Benefiting from the flexibility, the FANETs have been widely deployed in various scenarios, ranging from agricultural production to emergency rescue. However, in FANETs, the mobility of unmanned aerial vehicles (UAVs) has led to critical challenges for the stability of communications. Especially, the routing flooding mechanism extremely limits the scalability of FANET. To overcome these technical challenges, constructing a hierarchy and clustering structure in FANETs is considered a promising solution. In this paper, we propose the fission spectral clustering (FSC) strategy for UAV swarm networks. We model the UAV clustering problem as a graph cut problem. The time-sequential attributes weight of nodes and edges will be input to the FSC algorithm. Then, it will construct the Laplace matrix and calculate the first $k$ -th eigenvectors of it. We apply the K-Means algorithm into this feature space to cut the graph by clustering the eigenvectors. Each cluster will constantly fission with this strategy until it satisfies the size and structure constraints in the UAV clusters. Some simulations are implemented to evaluate our proposed algorithm in comparison to the other state-of-the-art solutions.

中文翻译：

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无人机群体网络的裂变谱聚类策略

飞行自组织网络（FANET）引起了学术界和工业界的广泛关注。得益于灵活性，FANET已广泛部署在从农业生产到应急救援的各种场景中。然而，在 FANET 中，无人机 (UAV) 的移动性给通信稳定性带来了严峻挑战。特别是，路由洪泛机制极大地限制了FANET的可扩展性。为了克服这些技术挑战，在 FANET 中构建层次结构和集群结构被认为是一种有前途的解决方案。在本文中，我们提出了无人机群体网络的裂变谱聚类（FSC）策略。我们将无人机聚类问题建模为图割问题。节点和边的时序属性权重将输入到FSC算法中。然后，它将构造拉普拉斯矩阵并计算第一个$k$ - 它的特征向量。我们将 K-Means 算法应用到这个特征空间中，通过对特征向量进行聚类来切割图。每个集群都会按照这种策略不断裂变，直到满足无人机集群中的大小和结构约束。实施一些模拟来评估我们提出的算法与其他最先进的解决方案的比较。