Link quality prediction based on random forest

Linlan LIU; Shengrong GAO; Jian SHU

doi:10.11959/j.issn.1000-436x.2019025

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Link quality prediction based on random forest

Correspondences | 更新时间：2024-06-05

- Link quality prediction based on random forest
- Journal on Communications Vol. 40, Issue 4, Pages: 202-211(2019)
- 作者机构：
  
  1. 南昌航空大学信息工程学院，江西南昌 330063
  2. 南昌航空大学软件学院，江西南昌 330063
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61762065);The National Natural Science Foundation of China(61363015);The Natural Science Foundation of Jiangxi Province(20171BAB202009);The Natural Science Foundation of Jiangxi Province(20171BBH80022);The Key Research Foundation of Education Bureau of Jiangxi Province(GJJ150702);The Innovation Foundation for Postgraduate Student of Jiangxi Province(YC2017024)
- DOI：10.11959/j.issn.1000-436x.2019025
  CLC： TP393
- Online First：2019-04，
  
  Published：25 April 2019
- 稿件说明：
移动端阅览
Linlan LIU, Shengrong GAO, Jian SHU. Link quality prediction based on random forest[J]. Journal on Communications, 2019, 40(4): 202-211.
DOI：

Linlan LIU, Shengrong GAO, Jian SHU. Link quality prediction based on random forest[J]. Journal on Communications, 2019, 40(4): 202-211. DOI： 10.11959/j.issn.1000-436x.2019025.

摘要

链路质量预测对无线传感器网络的上层协议设计至关重要，通过链路质量预测方法选择高质量的链路通信，可以提高数据传输的可靠性和网络通信的效率。基于无监督聚类的高斯混合模型划分链路质量等级，采用零相位分量分析白化法去除样本间的相关性，计算信噪比、链路质量指示及接收信号强度指示的均值和方差，并将其结果作为链路质量参数；基于随机森林分类算法构建链路质量评估模型，采用随机森林回归算法构建链路质量预测模型，预测下一时刻的链路质量等级。在不同的实验场景下，与指数加权移动平均、三角度量、支持向量回归机和线性回归预测模型相比，所提预测模型具有更高的预测准确率。

Abstract

Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio

link quality indicator

and received signal strength indicator were taken as the estimation parameters of link quality

and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model

which predicted the link quality level at the next moment.In different scenarios

comparing with exponentially weighted moving average

triangle metric

support vector regression and linear regression prediction models

the proposed prediction model has higher prediction accuracy.

关键词

Keywords

references

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