DEEP NEURAL NETWORK BASED MATRIX COMPLETION FOR INTERNET OF THINGS NETWORK LOCALIZATION

Abstract

In this paper, we propose a deep neural network based matrix completion approach for Internet of Things (IoT) localization. In the proposed method, we recast Euclidean distance matrix completion problem into the alternating minimization problem. By using a cascade of multiple deep neural networks to recover the location map of sensors (and the original distance matrix) from the noisy observed matrix, the proposed method can achieve an accurate reconstruction performance of the distance matrix. The numerical simulations demonstrate that the proposed method outperforms state-of-the-art matrix completion algorithms both in noisy and noiseless scenarios.