传感器网络基于DTW的多小波压缩算法

周四望; 李兰

doi:10.3969/j.issn.1000-436x.2014.08.012

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传感器网络基于DTW的多小波压缩算法

学术论文 | 更新时间：2024-10-11

- 传感器网络基于DTW的多小波压缩算法
- DTW-based multi-wavelet data compression algorithm for wireless sensor networks
- 通信学报 2014年35卷第8期页码：86-94
- 作者机构：
- 作者简介：
  
  [ "周四望（1971-），男，湖南岳阳人，博士，湖南大学副教授，主要研究方向为传感器网络信息处理、压缩感知与小波分析。" ]
  [ "李兰（1984-），女，福建福州人，湖南大学硕士生，主要研究方向为传感器网络信息处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(60973127);新世纪优秀人才支持计划基金资助项目;Program for New Century Excellent Talents in University(NCET-11-0136);湖南省自然科学基金资助项目;The Natural Science Foundation of Hunan Province(14JJ2051)
- DOI：10.3969/j.issn.1000-436x.2014.08.012
  中图分类号： TP393
- 网络出版日期：2014-08，
  
  纸质出版日期：2014-25-25
- 稿件说明：
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周四望, 李兰. 传感器网络基于DTW的多小波压缩算法[J]. 通信学报, 2014,35(8):86-94.

Si-wang ZHOU, Lan LI. DTW-based multi-wavelet data compression algorithm for wireless sensor networks[J]. Journal on communications, 2014, 35(8): 86-94.
周四望, 李兰. 传感器网络基于DTW的多小波压缩算法[J]. 通信学报, 2014,35(8):86-94. DOI： 10.3969/j.issn.1000-436x.2014.08.012.

Si-wang ZHOU, Lan LI. DTW-based multi-wavelet data compression algorithm for wireless sensor networks[J]. Journal on communications, 2014, 35(8): 86-94. DOI： 10.3969/j.issn.1000-436x.2014.08.012.

摘要

提出传感器网络环境下基于DTW的多小波数据压缩算法。首先研究汇聚节点中异步数据点—点对的对应关系，设计迭代算法求出具有最大相关性的DTW弯曲路径。接着提出最佳匹配点选择算法，通过DTW弯曲路径中一对一数据点—点对来预测异步数据向量间的函数关系，获取最佳匹配点，得到具有最大相关性的传感数据矩阵。然后设计多小波变换，利用传感数据矩阵的相关性来压缩数据，同时解决数据矩阵的行列不对称问题。实验结果表明，所提出的算法在能量聚集比、重构精度和运行时间等压缩性能指标上优于经典的分布式小波压缩算法。

Abstract

A data compression algorithm for wireless sensor networks based on DTW and multi-wavelet transform is pro-posed. Firstly

the correlation and warping path of asynchronous data point pairs is introduced

and then an iterative algo-rithm for solving DTW warping path with maximal similarity is designed. Secondly

a best match point algorithm is pre-sented

which employs prediction to estimate the relationship of synchronous data vectors and then find out the best match points. A sensory data matrix with maximal correlation is thus obtained. Afterwards

a multi-wavelet transform is introduced

which is capable of utilizing the spatial correlation of sensory data matrix and solving the row-column asym-metry problem. Experiment results show that proposed method has higher energy concentration ratio

better reconstruc-tion accuracy and lower running time compared to the traditional distributed wavelet compression algorithm.

关键词

Keywords

references

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