Anti-Jamming Weighted Clustering Algorithm for UAV-Mounted Weapon Self-Organizing Network

Compared to traditional self-organizing networks, UAV-mounted weapon self-organizing networks are characterized by high node mobility, vulnerability to interference and attacks, extensive coverage, and large-scale network sizes. These factors complicate network management and the operation of routing protocols. Cluster-based topologies can be employed to optimize network management, reducing the impact of local topology changes on the entire network. To address the issue of cluster instability caused by the high mobility and interference susceptibility of UAV-mounted weapons, a anti-jamming weighted clustering algorithm(AJWCA) is proposed. The algorithm builds on existing weighted clustering approaches by incorporating an interference factor into the cluster head selection process. Additionally, a backup cluster head mechanism is introduced to mitigate cluster oscillations resulting from the failure of cluster head nodes in battlefield and emergency scenarios. The effectiveness of the proposed algorithm was validated through simulations. The results show that the algorithm effectively enables efficient clustering of network nodes and improves cluster head duration under interference conditions. Moreover, the method demonstrates rapid recovery capability in the event of node failures, making it well-suited for deployment in complex operational environments.