Graph Attentional Based Agglomerative Cluster for UAV Swarm Networks

Abstract

In recent years, UAVs have been widely employed in the construction of post-disaster emergency communication networks due to their flexible mobility and self-organizing capabilities, enabling them to quickly and spontaneously establish communication links. Post-disaster rescue efforts, restoring connectivity and communication, as well as providing disaster information, all require strong support from network applications. However, an essential element in the development of effective UAV swarm-based applications is the network system. Compared to the fixed networks, UAV swarm networks present unique challenges in design and implementation due to its characteristics of high mobility nodes, unstable links, and dynamic topology. Recently, clustering technology has gained recognition as an effective approach to constructing stable UAV swarm networks. In this paper, for UAV swarm networks, we propose a graph attention-based agglomerative clustering algorithm. This algorithm allows UAV nodes to learn similarity relationships through a graph attention network. By considering the mobility similarity between UAV nodes, each UAV node can merge with its adjacent nodes. Furthermore, we also design a cluster head selection algorithm based on mixed strategy games. The algorithm's effectiveness and accuracy were demonstrated by simulation results, which showed a 80.04% increase in network lifetime compared to baseline algorithms.