%0 Journal Article
%A JIA Chen-hao
%A SHU Jian
%A TAO Juan
%T Link Quality Prediction for Sensor Network Based on Improved LS-SVR
%D 2018
%R 10.13190/j.jbupt.2017-024
%J Journal of Beijing University of Posts and Telecommunications
%P 44-49
%V 41
%N 2
%X In order to predict the link quality accurately, a link quality prediction model was proposed to predict link quality for sensor networks based on improved least square support vector regression machine (LS-SVR). The rough set (RS) was introduced to reduce the link quality metrics so as to extract the effective characteristic metrics of the link quality. And the genetic algorithm (GA) was employed in LS-SVR to optimize the penalty factor and kernel width. Experiments show that compared with the experts advice-based prediction model, the proposed prediction model achieves better accuracy.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-024