基于高斯过程回归的链路质量预测模型  

Link quality prediction model based on Gaussian process regression

作　　者：舒坚[1] 刘满兰 尚亚青 陈宇斌[1] 刘琳岚[2] SHU Jian;LIU Manlan;SHANG Yaqing;CHEN Yubin;LIU Linlan(School of Software,Nanchang Hangkong University,Nanchang 330063,China;School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学软件学院，江西 南昌 330063 [2]南昌航空大学信息工程学院，江西 南昌 330063

出　　处：《通信学报》2018年第7期148-156,共9页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61762065;No.61363015;No.61501218;No.61501217);江西省自然科学基金资助项目(No.20171BAB202009;No.20171ACB20018)

摘　　要：基于链路质量的路由选择机制可有效感知当前链路的变化,且对无线传感器网络的可靠通信起着重要作用,基于此,提出基于高斯过程回归的链路质量预测模型。通过灰关联方法计算链路质量参数与分组接收率的关联度,选取链路质量指示均值和信噪比均值作为模型的输入参数,以降低计算复杂度。采用链路质量指示均值、信噪比均值和分组接收率构建基于组合协方差函数的高斯过程回归模型预测链路质量。稳定场景与不稳定场景下的实验结果表明,与动态贝叶斯网络预测模型相比,所提模型具有更好的预测精确度。Link quality is an important factor of reliable communication and the foundation of upper protocol design for wireless sensor network.Based on this,a link quality prediction model based on Gaussian process regression was proposed.It employed grey correlation algorithm to analyze correlation between link quality parameters and packet receive rate.The mean of the link quality indication and the mean of the signal-to-noise were selected as input parameters so as to reduce the computational complexity.The above parameters and packet receive rate were taken to build Gaussian process regression model with combination of covariance function,so that link quality could be predicted.In the stable and unstable scenarios,the experimental results show that the proposed model has better prediction accuracy than the one of dynamic Bayesian network prediction model.

关 键 词：无线传感器网络 高斯过程回归 链路质量预测 组合协方差函数 灰关联算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]