基于高斯过程回归的链路质量预测模型

2018年7月 Journal on Communications July 2018

2018113-1

第39卷第7期

通信学报 V ol.39 No.7基于高斯过程回归的链路质量预测模型

舒坚1，刘满兰2，尚亚青2，陈宇斌1，刘琳岚2

（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）

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。

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

中图分类号：TP393

文献标识码：A

doi: 10.11959/j.issn.1000?436x.2018113

Link quality prediction model based on

Gaussian process regression

SHU Jian 1, LIU Manlan 2, SHANG Yaqing 2, CHEN Yubin 1, LIU Linlan 2

1. School of Software, Nanchang Hangkong University, Nanchang 330063, China

2. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Abstract: 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 pro-

posed. 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 un-

stable scenarios, the experimental results show that the proposed model has better prediction accuracy than the one of

dynamic Bayesian network prediction model.

Key words: wireless sensor network, Gaussian process regression, link quality prediction, combination of covariance

function, grey correlation algorithm

1 引言

近年来，随着微机电、无线通信和嵌入式等技

术的不断发展和相互融合，低成本、低功耗、多功

能的微型传感器得到了广泛应用。无线传感器网络（WSN, wireless sensor network ）是一种由大量廉价微型传感器构成、具有动态拓扑结构、以数据传输为中心的多跳自组织网络[1]。节点部署环境的复杂多变导致数据传输过程易受周围环境的干扰和影响，链路特性表现为不规则性、非收稿日期：2017?09?18；修回日期：2018?05?25

通信作者：刘琳岚，liulinlan@https://www.360docs.net/doc/c49339469.html,

基金项目：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）

Foundation Items: The National Natural Science Foundation of China (No.61762065, No.61363015, No.61501218, No.61501217),The Natural Science Foundation of Jiangxi Province (No.20171BAB202009, No.20171ACB20018)

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