A link prediction approach based on deep learning for opportunistic sensor network

Abstract

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.