基于StOMP算法的WSN压缩感知数据重构

doi:10.3969/j.issn.1000-3428.2017.09.027

计算机工程

基于StOMP算法的WSN压缩感知数据重构

黄志清^1,2,张严心³,李梦佳 ^1,2,成志鹏³

(1.北京工业大学软件学院,北京 100124; 2.北京市物联网软件与系统工程技术研究中心,北京 100124; 3.北京交通大学电子信息工程学院先进控制系统研究所,北京 100044)

收稿日期:2016-07-15 出版日期:2017-09-15 发布日期:2017-09-15
作者简介:黄志清(1970—),男,副教授、博士,主研方向为无线传感器网络、物联网、软件定义网络;张严心(通信作者),博士;李梦佳、成志鹏,硕士。
基金资助:
国家发改委项目(Q5025001201502);中央高校基本科研业务费专项资金(W16JB00340)。

WSN Compressed Sensing Data Reconstruction Based on StOMP Algorithm

HUANG Zhiqing^1,2,ZHANG Yanxin³,LI Mengjia^1,2,CHENG Zhipeng ³

(1.School of Software,Beijing University of Technology,Beijing 100124,China; 2.Beijing Engineering Research Center for IoT Software and Systems,Beijing 100124,China; 3.Advanced Control Systems Laboratory,School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)

Received:2016-07-15 Online:2017-09-15 Published:2017-09-15

摘要/Abstract

摘要： 分段正交匹配追踪算法(StOMP)运算速度快、计算量小,适用于无线传感器网络(WSN)压缩感知数据重构。为此,分析并研究StOMP算法的门限阈值选取对WSN压缩感知数据重构精度的影响,提出一种StOMP算法门限阈值的自适应调整方法。基于比例-积分-微分方法的思想,根据StOMP算法的当次重构误差计算门限阈值的调整值,并使用调整后的门限阈值重新进行数据重构,重复该过程以提高重构精度。实验结果表明,该方法能快速找到满足误差要求的门限阈值,与采用固定门限阈值的调整方法相比,重构精度更高。

关键词: 无线传感器网络, 压缩感知, 数据重构, 分段正交匹配追踪算法, 比例-积分-微分方法

Abstract: Stagewise Orthogonal Matching Pursuit(StOMP) algorithm has rapid computing speed and small amount of calculation quantity,and is thus highly suited to the reconstruction of Wireless Sensor Network(WSN) Compressed Sensing(CS) data.The influence of threshold selection of StOMP algorithm on the reconstruction accuracy of CS data in WSN is analyzed and studied.And this paper proposes an adaptive adjustment method of threshold value in the StOMP algorithm.Based on the idea of Proportional-Integral-Derivative(PID) method,the adjustment value of the threshold is calculated according to the reconstruction error.Reconstruct the data with the new threshold,and this process is repeated to improve the accuracy of reconstruction.Experimental result shows that this method can quickly find the threshold value which meets the error requirement and has distinctly improved reconstruction precision compared with the adjustment method of fixed threshold value.

Key words: Wireless Sensor Network(WSN), Compressed Sensing(CS), data reconstruction, Stagewise Orthogonal Matching Pursuit(StOMP) algorithm, Proportional-Integral-Derivative(PID) method

中图分类号:

TP301.6

黄志清,张严心,李梦佳,成志鹏. 基于StOMP算法的WSN压缩感知数据重构[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2017.09.027.

HUANG Zhiqing,ZHANG Yanxin,LI Mengjia,CHENG Zhipeng. WSN Compressed Sensing Data Reconstruction Based on StOMP Algorithm[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2017.09.027.

http://www.ecice06.com/CN/Y2017/V43/I9/149

参考文献

参考文献［1］DONOHO D L.Compressed Sensing［J］.IEEE Transac-tions on Information Theory,2006,52(4):1289-1306. ［2］CANDES E,ROMBERG J,TAO T.Robust Uncertainty Principles:Exact Signal Reconstruction from Highly Incomplete Frequency Information［J］.IEEE Transactions on Information Theory,2006,52(2):489-509. ［3］戴琼海,付长军,季向阳.压缩感知研究［J］.计算机学报,2011,34(3):425-434. ［4］LUO Chong,WU Feng,SUN Jun,et al.Compressive Data Gathering for Large-scale Wireless Sensor Networks［C］//Proceedings of International Conference on Mobile Computing and Networking.New York,USA:ACM Press,2009:145-156. ［5］LIU Xiaoyang,ZHU Yanmin,KONG Linghe,et al.CDC:Compressive Data Collection for Wireless Sensor Networks［J］.IEEE Transactions on Parallel & Distributed Systems,2015,26(8):2188-2197. ［6］QUER G,ZORDAN D,MASIERO R,et al.WSN-Control:Signal Reconstruction Through Compressive Sensing in Wireless Sensor Networks［C］//Proceedings of the 35th Conference on Local Computer Networks.Washington D.C.,USA:IEEE Press,2010:921-928. ［7］WANG Jin,TANG Shaojie,YIN Baocai,et al.Data Gathering in Wireless Sensor Networks Through Intelligent Compressive Sensing［C］//Proceedings IEEE INFOCOM’12.Washington D.C.,USA:IEEE Press,2012:603-611. ［8］王国英,江雨佳,莫路锋,等.基于压缩感知的土壤呼吸监测传感网动态采样调度策略［J］.中国科学:信息科学,2013,43(10):1326-1341. ［9］张明,朱俊平,蔡骋.WSN中基于压缩感知的数据收集方案［J］.计算机工程,2012,38(20):68-71. ［10］CHEN S S,DONOHO D L,SAUNDERS M A.Atomic Decomposition by Basis Pursuit［J］.SIAM Review,2001,43(1):33-61. ［11］DAUBECHIES I,DEFRISE M,MOL C D.An Iterative Thresholding Algorithm for Linear Inverse Problems with a SparsityConstraint［J］.Communications on Pure & Applied Mathematics,2003,57(11):1413-1457. ［12］GILBERT A C,STRAUSS M J,TROPP J A,et al.Algorithmic Linear Dimension Reduction in the l_1 Norm For Sparse Vectors［EB/OL］.［2016-08-15］.https://arxiv.org/abs/cs/0608079. ［13］DONOHO D L,TSAIG Y,DRORI I,et al.Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit［J］.IEEE Transactions on Information Theory,2012,58(2):1094-1121. ［14］TROPP J A,GILBERT A C.Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit［J］.IEEE Transactions on Information Theory,2008,53(12):4655-4666. ［15］NEEDELL D,VERSHYNIN R.Signal Recovery from Incomplete and Inaccurate Measurements via Regularized Orthogonal MatchingPursuit［J］.IEEE Journal of Selected Topics in Signal Processing,2007,4(2):310-316. ［16］NEEDELL D,TROPP J A.CoSaMP:Iterative Signal Recovery from Incomplete and Inaccurate Samples［J］.Applied and Computational Harmonic Analysis,2009,26(3):301-321. ［17］ANG K H,CHONG G,LI Y,et al.PID Control System Analysis,Design,and Technology［J］.IEEE Transactions on Control Systems and Technology,2005,13(4):559-576. 编辑陆燕菲

[1]	杨敏, 张玲华. WSN中基于区域感知的改进AODV路由协议[J]. 计算机工程, 2023, 49(8): 130-136.
[2]	区展华, 李翠然, 杨茜. 基于ANN的能量采集无线传感器网络中继选择策略[J]. 计算机工程, 2023, 49(5): 215-222,230.
[3]	潘金凤, 尹丽菊, 高明亮, 邹国峰. 压缩感知观测信号的低秩稀疏分解[J]. 计算机工程, 2022, 48(8): 234-239.
[4]	周文康, 王行甫. 一种基于AHP和FIS的WSN路由算法[J]. 计算机工程, 2022, 48(3): 131-138,161.
[5]	王海浪, 张玲华. 基于PEGASIS的无线传感器网络路由协议改进[J]. 计算机工程, 2022, 48(12): 165-171,179.
[6]	张清国, 张勇, 张伟, 席瑞洁. 基于蜂窝结构的改进混合无线传感器网络覆盖优化算法[J]. 计算机工程, 2022, 48(12): 172-179.
[7]	刘玉红, 陈满银, 刘晓燕. 基于通道注意力的多尺度全卷积压缩感知重构[J]. 计算机工程, 2022, 48(12): 189-195.
[8]	缪祎晟, 赵春江, 吴华瑞. 基于改进粒子群的农田WSN路由优化方法[J]. 计算机工程, 2022, 48(10): 218-223.
[9]	陈辉, 高岩. 基于双簇头的WSNs非均匀分簇路由算法[J]. 计算机工程, 2022, 48(10): 184-192.
[10]	方海涛, 李明齐, 卞鑫. 基于DFT寻径的压缩感知信道估计改进算法[J]. 计算机工程, 2022, 48(1): 182-187.
[11]	韩优佳, 胡黄水, 姚美琴. 基于信任感知的无线传感器网络安全路由协议[J]. 计算机工程, 2021, 47(9): 145-152.
[12]	于吉喆, 白乐强, 曹科研. 基于垂线的WSN基站位置隐私保护算法[J]. 计算机工程, 2021, 47(8): 170-176,182.
[13]	蒋宝庆, 陈宏滨. 基于Q学习的无人机辅助WSN数据采集轨迹规划[J]. 计算机工程, 2021, 47(4): 127-134,165.
[14]	胡乔木, 邓昀. 基于压缩HMAC算法的传感器网络范围查询方法[J]. 计算机工程, 2021, 47(12): 200-208.
[15]	李翠然, 李昂. 太阳能补给的线型WSN能量高效路由算法[J]. 计算机工程, 2020, 46(9): 178-185.

选择文件类型/文献管理软件名称

选择包含的内容

基于StOMP算法的WSN压缩感知数据重构

WSN Compressed Sensing Data Reconstruction Based on StOMP Algorithm

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于StOMP算法的WSN压缩感知数据重构

WSN Compressed Sensing Data Reconstruction Based on StOMP Algorithm

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价