基于高斯过程回归的链路质量预测模型

舒坚; 刘满兰; 尚亚青; 陈宇斌; 刘琳岚

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

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基于高斯过程回归的链路质量预测模型

学术论文 | 更新时间：2024-06-05

- 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
- 通信学报 2018年39卷第7期页码：148-156
- 作者机构：
  
  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
- 作者简介：
  
  [ "舒坚（1964-），男，江西南昌人，南昌航空大学教授、硕士生导师，主要研究方向为无线传感器网络、软件工程等。" ]
  [ "刘满兰（1992-），女，湖南耒阳人，南昌航空大学硕士生，主要研究方向为无线传感器网络。" ]
  [ "尚亚青（1991-），女，河南开封人，南昌航空大学硕士生，主要研究方向为无线传感器网络。" ]
  [ "陈宇斌（1977-），男，江西南昌人，南昌航空大学讲师，主要研究方向为无线传感器网络、高性能计算。" ]
  [ "刘琳岚（1968-），女，湖南东安人，南昌航空大学教授，主要研究方向为无线传感器网络、分布系统等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61762065);国家自然科学基金资助项目(61363015);国家自然科学基金资助项目(61501218);国家自然科学基金资助项目(61501217);江西省自然科学重点基金资助项目(20171BAB202009);江西省自然科学重点基金资助项目(20171ACB20018)
- DOI：10.11959/j.issn.1000-436x.2018113
  中图分类号： TP393
- 网络出版日期：2018-07，
  
  纸质出版日期：2018-07-25
- 稿件说明：
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舒坚, 刘满兰, 尚亚青, 等. 基于高斯过程回归的链路质量预测模型[J]. 通信学报, 2018,39(7):148-156.

Jian SHU, Manlan LIU, Yaqing SHANG, et al. Link quality prediction model based on Gaussian process regression[J]. Journal on communications, 2018, 39(7): 148-156.
舒坚, 刘满兰, 尚亚青, 等. 基于高斯过程回归的链路质量预测模型[J]. 通信学报, 2018,39(7):148-156. DOI： 10.11959/j.issn.1000-436x.2018113.

Jian SHU, Manlan LIU, Yaqing SHANG, et al. Link quality prediction model based on Gaussian process regression[J]. Journal on communications, 2018, 39(7): 148-156. DOI： 10.11959/j.issn.1000-436x.2018113.

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。

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 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

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references

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