计算机应用研究2016，Vol.33Issue(6)：1826-1829,4.DOI:10.3969/j.issn.1001-3695.2016.06.050
基于静态权值组合集成模型的 WSN 时钟偏差估计
Clock bias estimation for WSN based on static weights integration model
摘要
Abstract
Aiming at the low accuracy and high complexity in the clock synchronization procedure for WSN wireless sensor network,this paper proposed a clock error prediction method based on static weight composite integration model.Sample back into the observed time stamp value of sensor nodes,and then improved the error function and threshold adjustment method of the algorithm for the regression problems based AdaBoost.RT integrated learning algorithm.Lastly,the improved algorithm would be used as an integration framework,and DPNN would be used as weak machine learning to build integrated local model to effectively predict the time deviation.Experiments show that for long-term prediction,prediction effect of the AdaBoost.RT model and the improved AdaBoost.RT model increased by 20% compared with DPNN global model.In addition,in both the long-term and short-term observations,the predicted effect of the improved AdaBoost.RT model is superior to AdaBoost.RT model,and it can more effectively reduce clock estimation bias of the WSNs.关键词
无线传感器网络/时钟偏差/AdaBoost.RT 模型/集成局域模型Key words
WSN/clock deviation/AdaBoost.RT model/integrated local model分类
信息技术与安全科学引用本文复制引用
高子林,鄢傲,熊江,潘勇..基于静态权值组合集成模型的 WSN 时钟偏差估计[J].计算机应用研究,2016,33(6):1826-1829,4.基金项目
国家自然科学基金资助项目（61273219）；重庆市教委科学技术研究资助项目（KJ131108，KJ1401029）；重庆三峡学院科学研究计划资助项目 （）
