期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：4
页码：1
DOI：10.1177/1550147717700642
出版社：Hindawi Publishing Corporation
摘要：Link prediction for opportunistic sensor network has been attracting more and more attention. However, the inherent dynamic nature of opportunistic sensor network makes it a challenging issue to ensure quality of service in opportunistic sensor network. In this article, a novel deep learning framework is proposed to predict links for opportunistic sensor network. The framework stacks the conditional restricted Boltzmann machine which models time series by appending connections from the past time steps. A similarity index based on time parameters is proposed to describe similarities between nodes. Through tuning learning rate layer-adaptively, reconstruction error of restricted Boltzmann machine goes stable rapidly so that the convergence time is shortened. The framework is verified by real data from INFOCOM set and MIT set. The results show that the framework can predict links of opportunistic sensor network effectively.
关键词：Opportunistic sensor network; link prediction; similarity index; deep belief network; quality of service
