Grey Kernel Partial Least Squares-based Prediction for Temporal Data Aggregation in Sensor Networks
Data aggregation is a current hot research area in sensor networks. Aiming at the time series data in sensor networks, we present GRBFKPLS (Grey RBF Kernel Partial Least Squares), a novel prediction model data aggregation of sensor networks. In this model, grey model prediction theory is introduced into partial least squares. By the approach, the input data are firstly mapped to a nonlinear higher dimensional feature space, a linear partial least squares model is then constructed by RBF kernel transformation. Moreover, moving widow method is utilized to update samples continuously in this dynamical prediction model. The model is validated with fuel pressure data of injector. The results show that the model can execute dynamic multi-step prediction, and it has high precision prediction and flexibility. Thus, it can observably reduce the number of transmissions in sensor networks and save energy. Besides, it also has better performance in latency and computation. Comparing with RBFKPLS (RBF Kernel Partial Least Squares), GRBFKPLS is more effective for senor networks, so it has a good foreground to improve the prediction performance of data aggregation.
grey model partial least squares RBF sensor network data aggregation
Jian Kang Liwei Tang Xianzhang Zuo Hao Li
Department of Guns Engineering Mechanical Engineering College Shijiazhuang,P.R.China Unit 63880 Luoyang,P.R.China
国际会议
上海
英文
1853-1857
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）