## 1 Introduction  
Unmanned aerial vehicles (UAVs) have recently gained significant attention from researchers due to their various characteristics, including flexible deployment, line-of-sight communication, and strong scalability [1]. UAV communication can support high-speed data transmission, enhance better communication quality, and expand network coverage [2, 3], and is an effective carrier for wide-area data acquisition [4]. Additionally, UAVs serve as effective carriers for wide-area data acquisition. UAV communication is extensively utilized in remote areas or after-disaster scenarios, e.g., emergency rescue, remote areas monitoring, industrial inspections, and other scenarios. In these scenarios, multi-UAV communication networks, commonly referred to as flying ad hoc networks (FANETs) [5], are utilized to enhance multi-tasking abilities and extend lifetimes through collaboration among UAVs. Various network topologies such as star, mesh, and cluster are applied to connect the multiple UAV nodes in FANETs [6]. UAVs have become increasingly important to support the resource starving applications of FANETs in 5G and beyond 5G mobile networks. Examples of use cases are after-disaster rescue, advanced mapping, and aerial photography, in which UAVs must satisfy the ever-increasing demands for mobile data communication and ubiquitous connectivity to different kinds of wireless devices [7]. FANETs have progressively advanced commencing from a solitary enormous measured UAV doing the allotted mission to a set of small-sized UAVs [8], owing to their nimbless, maneuverability, and cost-effectiveness compared to larger UAVs. In FANETs,smaller UAVs are also more convenient to deploy and operate, especially when maintaining network connectivity requires a significant number of nodes. Table 1 displays the features of various types of UAVs. Owing to the expansion of network scale, clustering network topology has gained wide acceptance. The concept of clustering entails selecting nodes with requisite capabilities for data processing and transmission as cluster head nodes, while other nodes can register with the cluster head node and become members of the cluster. The cluster head nodes collaborate to form a connected virtual backbone network that ensures network topology and stability [10]. In [11], the authors proposed a weighted clustering algorithm (WCA), which takes into account the ideal degree, transmission power, mobility, and battery power of mobile network nodes. However, the proposed WCA algorithm is geared towards ad hoc networks with low node mobility and is not appropriate for high-mobility UAV networks. The current research on UAV-based clustering algorithms [6; 12; 13; 14; 15; 16; 17; 18] still has limitations in the following two aspects. Firstly, due to the high mobility of UAVs, frequent re-clustering is necessary, which leads to a decline in network performance. Secondly, limited energy supply in UAVs can cause energy loss during task execution and also the possibility of machine failure or other accidents. The interruption of cluster head nodes' service results in wastage of time and resources during the reselection of cluster heads and transmission of information required by cluster heads. Therefore, ensuring cluster stability in UAV networks is an urgent problem that needs to be addressed. To solve the above problems, in this paper a dynamic weighted clustering algorithm with dual cluster heads (DWCA-DCH) is propopposed to deploy the UAV network and a survivability analysis method is provided to evaluate the network performance. The main contribution of this paper consists of the following aspects:  
* We propose a DWCA-DCH to improve the network's stability and availability by selecting the prime cluster head (pCH) with higher communication quality and the backup cluster head (bCH) with higher stability. By introducing the dual cluster head mechanism, the bCH can take over the role of the pCH when the pCH fails to continue data transmission, thereby improving the system availability.