UAV-Clustering: Cluster head selection and update for UAV swarms searching with unknown target location

UAV swarms based on cooperative communication networks are widely used in many fields, which have the advantages of high mobility, high flexibility and low cost. However, UAVs face limited spectrum resources in a specific area and may interfere with primary users. Effective communication management between UAVs is a challenging problem. There-fore, this paper proposes a UAV clustering method based on the improved cluster head selection weight, which provides an effective management for the communication between UAVs and improves the efficiency of data collection. The proposed algorithm employs a new cluster head selection strategy based on the searched targets and available channel resources. Moreover, we analyze the weight factors of UAVs in flight and communication energy consumption. Considering the decreasing the member of the UAV clusters, we also design a maintenance strategy to improve the degree of data sharing in the cluster. The experimental results show that, compared with the traditional UAV clustering methods, the proposed method can effectively improve the network management for communication resources, reduce the collision and interference rate with the primary user by 25%, shorten the time required to fully acquire multi-target point data for the first time by 9%, and increase the amount of target point data collected by 26%.