带有同步预测的WBAN时序数据融合算法

王汝言; 翟美玲; 吴大鹏

doi:10.11959/j.issn.1000-436x.2015147

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带有同步预测的WBAN时序数据融合算法

学术论文 | 更新时间：2024-06-05

- 带有同步预测的WBAN时序数据融合算法
- Time series data aggregation algorithm with synchronous prediction for WBAN
- 通信学报 2015年36卷第6期页码：13-21
- 作者机构：
  
  重庆邮电大学宽带泛在接入技术研究所，重庆 400065
- 作者简介：
  
  [ "王汝言（1969-），男，湖北浠水人，博士，重庆邮电大学教授、博士生导师，主要研究方向为泛在网络、全光网络理论与技术、多媒体信息处理等。" ]
  [ "翟美玲（1988-），女，河北保定人，重庆邮电大学硕士生，主要研究方向为无线体域网络、数据融合算法。" ]
  [ "吴大鹏（1979-），男，黑龙江大庆人，博士，重庆邮电大学教授、硕士生导师，主要研究方向为泛在无线网络、无线网络服务质量管理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61371097);重庆市自然科学重点基金资助项目(cstc2013jjB40001);重庆市自然科学重点基金资助项目(cstc2013jjB40006);重庆邮电大学青年自然科学基金资助项目(A2012-93);重庆市青年科技人才培养计划基金资助项目(CSTC2014KJRC-QNRC40001)
- DOI：10.11959/j.issn.1000-436x.2015147
  中图分类号： TP393
- 网络出版日期：2015-06，
  
  纸质出版日期：2015-06-25
- 稿件说明：
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王汝言, 翟美玲, 吴大鹏. 带有同步预测的WBAN时序数据融合算法[J]. 通信学报, 2015,36(6):13-21.

Ru-yan WANG, Mei-ling ZHAI, Da-peng WU. Time series data aggregation algorithm with synchronous prediction for WBAN[J]. Journal on communications, 2015, 36(6): 13-21.
王汝言, 翟美玲, 吴大鹏. 带有同步预测的WBAN时序数据融合算法[J]. 通信学报, 2015,36(6):13-21. DOI： 10.11959/j.issn.1000-436x.2015147.

Ru-yan WANG, Mei-ling ZHAI, Da-peng WU. Time series data aggregation algorithm with synchronous prediction for WBAN[J]. Journal on communications, 2015, 36(6): 13-21. DOI： 10.11959/j.issn.1000-436x.2015147.

摘要

提出一种带有同步预测的时序数据融合算法，利用多分辨率分析特性对采集的原始数据进行预处理，挖掘反映人体生理状态的本质特征，进而采用同步预测机制在感知节点和汇聚节点处分别建立轻量级预测模型，消除网内冗余数据的传输以降低能耗。结果表明所提出的融合算法具有较高的预测精度，能够实现低开销的无线体域网时序数据融合。

Abstract

Due to the nonlinearity and nonstationarity of the physiological data sensed by WBAN

data aggregation cannot be effectively achieved according to the time-domain trend of data.Therefore

a novel time series data aggregation algorithm with synchronous prediction was proposed.By preprocessing the original sensing data with the multi-resolution analysis

the inherent characteristics of the physiological data can be obtained to establish a light-weight synchronous prediction model at both the sensor and sink.Numerical results show that the proposed aggregation algorithm can achieve a favorable prediction precision and a low energy consumption rate by eliminating the in-network data redundancy.

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references

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