Adaptive Enhanced Weighted Clustering Algorithm for UAV Swarm

摘要

In recent years, it is a hot topic to put into large-scale UAV swarm in military operations , but the large number of high-speed UAV makes network management complicated. Clustering is one of the effective means to optimize network management. In this paper, we propose a adaptive enhanced weighted clustering algorithm. This algorithm not only based on the consideration of the optimal node degree and the distance between the cluster head and neighbor nodes, but also introduces the average link retention rate between UAVs and energy consumption. The UAV with the minimum weight will be selected as the cluster head by considering these four parameters synthetically. The simulation results show that the clustering algorithm not only reasonably allocates the number of cluster heads, reduces the switching rate between clusters, improves the stability of cluster structure, but also balances the energy consumption of the network, extends the minimum survival time of the network, and improves the overall endurance of the UAVs.