Dynamic sampling method for wireless sensor network based on compressive sensing

doi:10.11772/j.issn.1001-9081.2017.01.0183

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (1): 183-187.DOI: 10.11772/j.issn.1001-9081.2017.01.0183

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Dynamic sampling method for wireless sensor network based on compressive sensing

SONG Yang^1,2, HUANG Zhiqing^1,2, ZHANG Yanxin³, LI Mengjia^1,2

1. School of Software Engineering, Beijing University of Technology, Beijing 100020, China;
2. Beijing Engineering Research Center for IoT Software and System, Beijing 100020, China;
3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2016-05-26 Revised:2016-07-18 Online:2017-01-09 Published:2017-01-10
Supported by:
This work is partially supported by the Fundamental Research Funds for the Beijing University of Technology (025000514314004), the National Development and Reform Commission's Project Item (Q5025001201502).

基于压缩感知的无线传感器网络动态采样方法

宋洋^1,2, 黄志清^1,2, 张严心³, 李梦佳^1,2

1. 北京工业大学软件学院, 北京 100020;
2. 北京市物联网软件与系统工程技术研究中心, 北京 100020;
3. 北京交通大学电子信息工程学院, 北京 100044

通讯作者: 黄志清
作者简介:宋洋(1990-),男,北京人,硕士研究生,主要研究方向:无线传感器网络、压缩感知;黄志清(1970-),男,四川自贡人,副教授,博士,主要研究方向:无线传感器网络、物联网、软件定义网络;张严心(1976-),女,辽宁盘锦人,副教授,博士,主要研究方向:无线传感器网络、复杂网络控制、复杂系统可靠控制;李梦佳(1993-),女,北京人,硕士研究生,主要研究方向:无线传感器网络、压缩感知。
基金资助:
北京工业大学基础研究基金资助项目（025000514314004）；国家发改委项目子项（Q5025001201502）。

Abstract

Abstract: It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.

Key words: Wireless Sensor Network (WSN), Compressive Sensing (CS), data prediction, feedback control, dynamic sampling

摘要： 基于固定采样率的无线传感网（WSN）压缩感知（CS）在收集随时间变化的数据时难以获得满意的数据恢复精度。针对该问题，提出了一种基于数据预测和采样率反馈控制的动态采样方法。首先，汇聚节点通过分析当前采样时段与上一采样时段获取数据的线性度量指标，预测数据的变化趋势；然后，根据预测结果计算感知节点未来的采样率，并通过反馈控制机制对感知节点的采样过程进行动态调节。实验结果表明，相比基于目前广泛采用的基于固定采样率的无线传感网压缩感知数据收集方法，该方法能够有效提高压缩数据的恢复精度。

关键词: 无线传感器网络, 压缩感知, 数据预测, 反馈控制, 动态采样

CLC Number:

SONG Yang, HUANG Zhiqing, ZHANG Yanxin, LI Mengjia. Dynamic sampling method for wireless sensor network based on compressive sensing[J]. Journal of Computer Applications, 2017, 37(1): 183-187.

宋洋, 黄志清, 张严心, 李梦佳. 基于压缩感知的无线传感器网络动态采样方法[J]. 计算机应用, 2017, 37(1): 183-187.

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Dynamic sampling method for wireless sensor network based on compressive sensing

基于压缩感知的无线传感器网络动态采样方法

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