基于深度信念网络的WSN链路质量预测

刘琳岚; 许江波; 李越; 杨志勇

doi:10.11959/j.issn.1000-436x.2017257

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基于深度信念网络的WSN链路质量预测

学术论文 | 更新时间：2024-06-05

- 基于深度信念网络的WSN链路质量预测
- Deep belief network-based link quality prediction for wireless sensor network
- 通信学报 2017年38卷第Z2期页码：17-25
- 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：
  
  [ "刘琳岚（1968-），女，湖南东安人，南昌航空大学教授，主要研究方向为物联网、软件工程。" ]
  [ "许江波（1991-），男，安徽桐城人，南昌航空大学硕士生，主要研究方向为无线传感器网络。" ]
  [ "李越（1991-），男，江西抚州人，南昌航空大学硕士生，主要研究方向为无线传感器网络。" ]
  [ "杨志勇（1982-），男，河南开封人，南昌航空大学讲师，主要研究方向为物联网。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61363015);国家自然科学基金资助项目;The National Natural Science Foundation of China(61762065);国家自然科学基金资助项目;The National Natural Science Foundation of China(61501218);江西省自然科学重点基金资助项目;The Natural Science Foundation of Jiangxi Province(20171BAB202009);江西省自然科学重点基金资助项目;The Natural Science Foundation of Jiangxi Province(20171ACB20018);江西省研究生创新专项基金资助项目;The Innovation Foundation for Postgraduate Student of Jiangxi Province(YC2016-S348)
- DOI：10.11959/j.issn.1000-436x.2017257
  中图分类号： TP393
- 网络出版日期：2017-11，
  
  纸质出版日期：2017-11-25
- 稿件说明：
移动端阅览
刘琳岚, 许江波, 李越, 等. 基于深度信念网络的WSN链路质量预测[J]. 通信学报, 2017,38(Z2):17-25.

Lin-lan LIU, Jiang-bo XU, Yue LI, et al. Deep belief network-based link quality prediction for wireless sensor network[J]. Journal on communications, 2017, 38(Z2): 17-25.
刘琳岚, 许江波, 李越, 等. 基于深度信念网络的WSN链路质量预测[J]. 通信学报, 2017,38(Z2):17-25. DOI： 10.11959/j.issn.1000-436x.2017257.

Lin-lan LIU, Jiang-bo XU, Yue LI, et al. Deep belief network-based link quality prediction for wireless sensor network[J]. Journal on communications, 2017, 38(Z2): 17-25. DOI： 10.11959/j.issn.1000-436x.2017257.

摘要

在分析现有链路质量预测模型的基础上，提出基于深度信念网络的无线传感器网络链路质量预测模型。采用支持向量分类机对链路质量进行评估，获得链路质量等级；采用深度信念网络提取链路质量特征，并采用softmax预测下一时刻的链路质量。在不同实验场景下，与逻辑回归、BP神经网络以及贝叶斯网络预测模型相比，所提出模型具有更好的预测准确率。

Abstract

After analyzing the existing link quality prediction models

a link quality prediction model for wireless sensor network was proposed

which was based on deep belief network.Support vector classification was employed to estimate link quality

so as to get link quality levels.Deep belief network was applied in extracting the features of link quality

and softmax was taken to predict the next time link quality.In different scenarios

compared with the model of link quality prediction based on logistic regression

BP neural network and Bayesian network methods

the experimental results show that the proposed prediction model achieves better precision.

关键词

Keywords

references

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