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

您当前的位置：

首页 >

文章列表页 >

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
- 网络首发：2017-11，
  
  纸质出版：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

付帅 , 马建峰 , 李洪涛 , 等 . 无线传感器网络中匿名的聚合节点选举协议 [J ] . 通信学报 , 2015 , 36 ( 2 ): 88 - 97 .

FU S , MA J F , LI H T , et al . Anonymous aggregator election protocol for wireless sensor networks [J ] . Journal on Communications , 2015 , 36 ( 2 ): 88 - 97 .

BACCOUR N , KOUB , ANIS A , et al . Radio link quality estimation in wireless sensor networks:a survey [J ] . ACM Transactions on Sensor Networks , 2012 , 8 ( 4 ): 688 - 722 .

翁丽娜 , 杨杰 , 柯海舟 , 等 . 基于时间序列分析的链路质量预测和稳定路由算法研究 [J ] . 电子与信息学报 , 2011 , 33 ( 4 ): 885 - 890 .

WENG L N , YANG J , KE H Z , et al . A time series analysis-based link quality prediction algorithm and its application to reliable routing [J ] . Journal of Electronics ＆ Information Technology , 2011 , 33 ( 4 ): 885 - 890 .

尚凤军 , 龚文娟 , 耿哲 . 基于链路预测和网络编码的MAC机制 [J ] . 通信学报 , 2016 , 37 ( 1 ): 17 - 27 .

SHAN F J , GONG W J , GENG Z . MAC mechanism based on link prediction and network coding [J ] . Journal on Communications , 2016 , 37 ( 1 ): 17 - 27 .

WENG L N , ZHANG P , FENG Z Y , et al . Short-term link quality prediction using non-parametric time series analysis [J ] . Science China Information Sciences , 2015 , 58 ( 8 ): 1 - 15 .

BOANO C A , ZUNIGA M A , VOIGT T , et al . The triangle metric:fast link quality estimation for mobile wireless sensor networks [C ] // The 19th International Conference on Computer Communications and Networks . 2010 : 1 - 7 .

WANG J , LIU Y , HE Y , et al . QoF:towards comprehensive path quality measurement in wireless sensor networks [J ] . IEEE Transactions on Parallel and Distributed Systems , 2014 , 25 ( 4 ): 1003 - 1013 .

BILDEA A , ALPHAND O , ROUSSEAU F , et al . Link quality estimation with the gilbert-elliot model for wireless sensor networks [J ] . Kips Transactions Partc , 2015 , 88 ( 4 ): 495 - 504 .

WEN J , AASAR Z , DARGIE W . A link quality estimation model for energy-efficient wireless sensor networks [C ] // The 2015 IEEE International Conference on Communications (ICC) . 2015 : 6694 - 6700 .

WANG Y , MARTONOSI M , PEH L S . Predicting link quality using supervised learning in wireless sensor networks [J ] . ACM Sigmobile Mobile Computing ＆ Communications Review , 2007 , 11 ( 3 ): 71 - 83 .

LIU T , CERPA A E . Foresee(4C):wireless link prediction using link features [C ] // International Conference on Information Processing in Sensor Networks . 2011 : 294 - 305 .

LIU T , CERPA A E . Talent:temporal adaptive link estimator with no training [C ] // The 10th ACM Conference on Embedded Network Sensor Systems . 2012 : 253 - 266 .

郭志强 , 王沁 , 万亚东 , 等 . 基于综合性评估的无线链路质量分类预测机制 [J ] . 计算机研究与发展 , 2013 , 50 ( 6 ): 1227 - 1238 .

GUO Z Q , WANG Q , WAN Y D , et al . A classification prediction mechanism based on comprehensive assessment for wireless link quality [J ] . Journal of Computer Research ＆ Development , 2013 , 5 ( 6 ): 1227 - 1238 .

舒坚 , 汤津 , 刘琳岚 , 等 . 基于模糊支持向量回归机的WSNs链路质量预测 [J ] . 计算机研究与发展 , 2015 , 52 ( 8 ): 1842 - 1851 .

SHU J , TANG J , LIU L L , et al . Fuzzy support vector regression-based link quality prediction model for wireless sensor networks [J ] . Journal of Computer Research ＆ Development , 2015 , 52 ( 8 ): 1842 - 1851 .

卢记仓 , 刘粉林 , 罗向阳 , 等 . 基于辨识性统计特征的PQ隐密图像识别算法 [J ] . 通信学报 , 2015 , 36 ( 3 ): 197 - 206 .

LU J C , LIU F L , LUO X Y , et al . Recognition of PQ stego images based on identifiable statistical feature [J ] . Journal on Communications , 2015 , 36 ( 3 ): 197 - 206 .

MOHAMMAD A K , MOHAMMAD M H . Deep belief network training improvement using elite samples minimizing free energy [J ] . International Journal of Pattern Recognition ＆ Artificial Intelligence , 2015 , 29 ( 5 ): 1 - 18 .

梁淑芬 , 刘银华 , 李立琛 . 基于LBP和深度学习的非限制条件下人脸识别算法 [J ] . 通信学报 , 2014 , 35 ( 6 ): 154 - 160 .

LIANG S F , LIU Y H , LI L C.Face recognition under unconstrained based on LBP and deep learning . Chinese relation extraction based on deep belief nets [J ] . Journal on Communications , 2014 , 35 ( 6 ): 154 - 160 .

张亚军 , 刘宗田 , 周文 . 基于深度信念网络的事件识别 [J ] . 电子学报 , 2017 , 45 ( 6 ): 1415 - 1423 .

ZHANG Y J , LIU Z T , ZHOU W . Event recognition based on deep belief network [J ] . ACTA Electronica Sinica , 2017 , 45 ( 6 ): 1415 - 1423 .

顾珊波 , 邵枫 , 蒋刚毅 , 等 . 基于支持向量回归的立体图像客观质量评价模型 [J ] . 电子与信息学报 , 2012 , 34 ( 2 ): 368 - 374 .

GU S B , SHAO F , JIANG G Y , et al . Objective stereoscopic image quality assessment model based on support vector regression [J ] . Journal of Electronics ＆ Information Technology , 2012 , 34 ( 2 ): 368 - 374 .

RAHHAL M M A , BAZI Y , ALHICHRI H , et al . Deep learning approach for active classification of electrocardiogram signals [J ] . Information Sciences , 2016 , 345 ( C ): 340 - 354 .

刘琳岚 , 樊佑磊 , 舒坚 , 等 . 一种基于BP神经网络的WSNs链路质量预测方法 [J ] . 计算机研究与发展 , 2011 , 48 ( Suppl ): 212 - 215 .

LIU L L , FAN Y L , SHU J , et al . A link quality prediction method for WSNs based on BP artificial neural network [J ] . Journal of Computer Research ＆ Development , 2011 , 48 ( Suppl ): 212 - 215 .

浏览量

1395

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于随机森林的链路质量预测

基于高斯过程回归的链路质量预测模型

传感器网络中GPS无关的轻量化反射式协同虚假数据过滤方案

基于图提示微调的WSN节点时空相关性异常检测方法

面向WSN异常节点检测的融合重构机制与对比学习方法