Deep Learning-Based Dependability Assessment Method for Industrial Wireless Network

Abstract

Techniques on 5G and Internet of things bring a strong potential paradigm shift to wireless communication applications in industrial domain. Hence, there is a strong need for quantitative dependability assessment in these applications. However, with the evergrowing complexity and amount of wireless communication systems, their dependability relevant parameters also increase rapidly. In addition, the deep neural network has advantages on high dimensional data process. Hence, a deep learning-based dependability assessment method is proposed to address the issue, wherein a deep auto-encoder based approach is proposed to reduce data dimension and to obtain the data codes, and DBSCAN is used to cluster these codes. An experimental environment is built for collecting data set on the Multifaces, and a rough classification method is proposed to obtain a superior deep encoder model. Based on the superior model and DBSCAN, the data set are mainly divided into four dependability clusters.