基于AdaBoost的链路质量预测机制研究

舒坚; 刘满兰; 郑巍

doi:10.11959/j.issn.1000-436x.2017233

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基于AdaBoost的链路质量预测机制研究

学术论文 | 更新时间：2024-06-05

- 基于AdaBoost的链路质量预测机制研究
- Study on AdaBoost-based link quality prediction mechanism
- 通信学报 2017年38卷第Z1期页码：39-45
- 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。" ]
  [ "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网络、链路质量。" ]
  [ "郑巍（1982-），男，江西萍乡人，南昌航空大学副教授，主要研究方向为物联网、社交网络、智能优化。" ]
- 基金信息：
  
  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(61762065);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.2017233
  中图分类号： TP393
- 网络出版日期：2017-10，
  
  纸质出版日期：2017-10-25
- 稿件说明：
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舒坚, 刘满兰, 郑巍. 基于AdaBoost的链路质量预测机制研究[J]. 通信学报, 2017,38(Z1):39-45.

Jian SHU, Man-lan LIU, Wei ZHENG. Study on AdaBoost-based link quality prediction mechanism[J]. Journal on communications, 2017, 38(Z1): 39-45.
舒坚, 刘满兰, 郑巍. 基于AdaBoost的链路质量预测机制研究[J]. 通信学报, 2017,38(Z1):39-45. DOI： 10.11959/j.issn.1000-436x.2017233.

Jian SHU, Man-lan LIU, Wei ZHENG. Study on AdaBoost-based link quality prediction mechanism[J]. Journal on communications, 2017, 38(Z1): 39-45. DOI： 10.11959/j.issn.1000-436x.2017233.

摘要

在无线传感器网络中，节点所在环境复杂多变导致其通信链路质量的不可靠，若能提前感知链路质量信息，则能很大程度上降低网络中节点的额外能量消耗。在分析现有链路质量预测方法的基础上，提出基于AdaBoost的链路质量预测机制。通过收集多个实验场景下的链路质量样本，采用基于密度的无监督聚类算法对训练样本划分链路质量等级；采用以支持向量机为弱分类器的 AdaBoost 算法，构建链路质量预测机制。实验结果表明，所提预测机制具有较高的预测精度。

Abstract

The link quality was vulnerable to the complexity environment in wireless sensor network.Obtaining link quality information in advance could reduce energy consumption of nodes.After analyzing the existing link quality prediction methods

AdaBoost-based link quality prediction mechanism was put forward.Link quality samples in deferent scenarios were collected.Density-based unsupervised clustering algorithm was employed to classify training samples into deferent link quality levels.The AdaBoost with SVM-based component classifiers was adopted to build link quality prediction mechanism.Experimental results show that the proposed mechanism has better prediction precision.

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references

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