## 1 Introduction  
Unmanned aerial vehicle (UAV) can be described as aerial platforms that, by integrating various applications, fulfill the requirement of replacing humans in different fields of work [1, 2]. UAVs, with their high performance and mobility, play an important role across various industries [3, 4]. In the field of UAV emergency rescue, the application of UAVs is becoming increasingly widespread and diversified. It is not limited to disaster area reconnaissance and information collection tasks but also plays an increasingly important role in areas such as casualty location, supply delivery, and environmental monitoring [5, 6, 7]. Especially with continuous advancements in technology and the accumulation of practical experience, UAVs have demonstrated increasingly exceptional performance in emergency rescue operations, establishing themselves as vital tools for enhancing the efficiency and effectiveness of rescue efforts [8]. However, despite the ability of individual UAV to respond quickly and execute tasks, their operational capabilities in complex battlefield environments remain limited. Autonomous UAVs, constrained by their limited sensing range, computational capacity, and mission load, often struggle to perform complex rescue tasks in the face of multiple task requirements and changing environments [9; 10]. For example, under complex terrain, extreme weather conditions, or constantly changing task requirements, the effectiveness of a single UAV is severely limited, making it difficult to achieve coordinated actions, comprehensive situational awareness, or dynamic task adaptation. Thus, to overcome the limitations of a standalone UAV, collaborative networked rescue has become a key development focus. The UAV group realizes flexible dynamic task allocation through networking cooperation [11] and combines with the ground control station to improve the real-time and accuracy of data transmission with edge computing services [12; 13]. Such a coordinated rescue mode not only improves task execution efficiency but also enhances adaptability and anti-interference capability in complex emergency environments, making it an important force in future emergency rescue operations. Therefore, with the growing demand for collaborative task execution of the UAV, the development of a reliable communication network has become a key focus of research. The self-organizing networks formed by UAVs exhibit characteristics such as high node mobility and highly dynamic network topologies [14; 15]. To fully exploit the advantages of the collaborative task execution, the network typically consists of a large number of nodes, wider spatial distribution, and greater network scale. Additionally, the emergency rescue scenarios faced by UAVs are often complex, involving multiple targets that require different groups to coordinate and complete specific tasks. In actual emergency rescue environments, the threat of interference against UAV self-organizing networks must also be considered. Such interference can affect a broad range of nodes or communication links, leading to significant changes in network topology and compromising the overall stability of the network [16]. The optimal solution to the aforementioned challenges is to partition the UAV network into multiple cluster structures, with each cluster responsible for executing different tasks. By dividing nodes into several clusters, each cluster can be regarded as a relatively stable sub-network [17; 18]. Compared to flat network architectures, a clustered structure mitigates the impact of partial changes on the overall network. During the routing computation and generation process, only a subset of nodes needs to participate, which effectively reduces routing and control overhead. In addition, the clustered architecture enables more efficient network control and management, thereby enhancing overall network stability [19]. UAVs can achieve efficient deployment and stable communication in a clustered network structure.