## 5 Simulation and Results  
### Simulation Parameter Settings  
The simulation aims to evaluate the performance of the proposed improved anti-interference weighted clustering algorithm for UAV self-organizing networks. The specific settings can be found in Table 1. In the simulation, the area is set to 1000 \(\times\) 1000 \(\times\) 1000 m, with 16-300 nodes participating. The nodes move at speeds ranging between 20 and 30 m/s, and the acceleration is 5-10 m/s\({}^{2}\). Each node starts with an initial energy of 100 J. The maximum communication distance between nodes within the cluster is limited to 300 m, and the maximum communication distance between cluster head nodes is limited to600 m. In the subsequent anti-interference comparison simulation, the interference power increases in a gradient of 50 w.  
In the simulation, the weighting coefficients for five key factors in CH and BCH selection are determined as follows and may be adjusted in future studies to suit specific operational requirements. The selection analysis of weighting coefficients is as follows:  
* External interference factor (\(\varepsilon_{4}=0.3\)): The interference is a critical determinant of communication stability, especially in complex emergency rescue environments, where it can cause signal attenuation, data loss, and connection interruptions. To ensure that the system can effectively cope with interference and maintain communication stability, the weight of the external interference factor is set to 0.30, prioritizing nodes with strong anti-jamming capabilities during CH and BCH selection. * Connectivity Factor (\(\varepsilon_{1}=0.25\)): The number of neighboring nodes directly affects network stability and scalability, since intra-cluster communication relies on the cluster head. So the cluster head should prioritize selecting UAVs that are connected to more neighbor nodes to ensure efficient communication. Based on this, the weight of the connectivity factor is set to 0.25 to ensure the selection of nodes with a larger number of neighboring nodes as the cluster head. * Average distance factor (\(\varepsilon_{5}=0.2\)): Distance is an important factor influencing communication efficiency and energy consumption. Cluster heads tend to select nodes that are closer to others to reduce communication delay, improve signal quality, and lower energy consumption. Therefore, the weight of distance is set to 0.20 to ensure that cluster head selection optimizes communication quality and energy efficiency to the greatest extent. * Remaining energy factor (\(\varepsilon_{2}=0.15\)): Remaining energy determines node longevity and overall network lifespan. To extend the network's operational time and ensure system stability, the weight of the energy factor is set to 0.15, allowing the node's energy state to be fully considered during cluster head selection. * Mobility similarity factor (\(\varepsilon_{3}=0.1\)): Mobility affects cluster-head stability, since rapid speed differences can lead to frequent role changes. Considering that the speed difference between nodes is relatively small in this task scenario, the influence of mobility on cluster head selection is relatively small. So the weight of the mobility similarity factor is set to 0.10 to appropriately reflect the dynamic characteristics of the nodes during selection. This section validates and analyzes the proposed algorithm, and compares it with IWCA [34], K-means++, WCA, and Random selection (RS) to ultimately verify the effectiveness and correctness of the algorithm proposed in this paper.