Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW is compared to MWCRSF, DCM, and GWO, and the evaluation results are presented in two scenarios, namely network evaluation in the clustering process and network evaluation in the routing process. Accordingly, in the first scenario, ICW has low clustering time and a high cluster lifetime. In the second scenario, ICW optimizes energy consumption, network longevity, packet delivery rate, routing overhead, and delay compared to other approaches. However, throughput in ICW is about 3.9% lower than that in MWCRSF.

中文翻译：

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飞行自组织网络中基于鲸鱼优化算法的智能聚类方案


由于无人机（UAV）的进步，这项新技术在军事和民用领域得到广泛应用。多无人机网络通常称为飞行自组织网络 (FANET)。由于这些应用，FANET必须保证通信稳定性并具有高可扩展性。这些目标是通过在 FANET 中提出集群技术来实现的。然而，这些网络的节点移动性高、能量有限、拓扑动态等特点，给分簇协议的两个重要过程，即簇的构建和簇头的选择带来了巨大的挑战。在本文中，在飞行自组织网络中提出了一种基于鲸鱼优化算法（称为ICW）的智能聚类方案。首先，每个无人机根据相邻链路的寿命指定其Hello间隔，以保证ICW对FANET的适应性。然后，使用鲸鱼优化算法（WOA）完成集中式聚类过程，以找到网络上的最佳聚类中心。为了确定每个无人机在集群中的成员资格，ICW 采用了一种新的标准，即接近比，以便每个无人机加入具有最佳接近比的集群。此外，对每条鲸鱼的评估是基于适应度函数进行的，该适应度函数由三个部分组成，即孤立簇的数量、簇间距离与簇内距离的比率以及簇大小。然后，根据分数值为每个簇选择簇头。该分数取决于四个指标的加权和，即剩余能量、每个无人机与其邻居之间的平均链路寿命、邻居度以及每个无人机与其邻居之间的平均距离。 最后一步，在FANET中引入了两个路由过程，即簇内路由和簇间路由。然后，通过NS2模拟器对ICW进行评估和实现。完成仿真过程后​​，将ICW与MWCRSF、DCM和GWO进行比较，并给出两种场景的评估结果，即聚类过程中的网络评估和路由过程中的网络评估。因此，在第一种情况下，ICW具有较短的聚类时间和较高的聚类寿命。在第二种情况下，与其他方法相比，ICW 优化了能耗、网络寿命、数据包传输率、路由开销和延迟。然而，ICW 的吞吐量比 MWCRSF 低约 3.9%。