Matrix Factorization and Deep Autoencoder based Clustering Scheme for Large-scale UAV Networks

In recent years, unmanned aerial vehicle (UAV) ad-hoc networks have achieved rapid development due to their autonomy and high reliability. Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks. However, due to the limited channel resources and complex communication environment, it becomes challenging to obtain complete information needed for clustering from remote nodes. In addition, the high dimension and non-linear relationships of data also make large-scale clustering diffcult. Therefore, in this paper, we propose a noval distributed clustering scheme. First, a matrix factorization (MF) based time series algorithm is proposed to predict and complement the incomplete information. Second, we adopt deep autoencoder to wisely incorporate the non-linear relationship of information needed for clustering. Finally we extract features as input to k-means to obtain clustering results. Simulation results demonstrate the effectiveness of the proposed clustering scheme.