基于压缩感知与矩阵补全技术的WSN数据收集算法
WSN data gathering algorithm based on compressive sensing and matrix completion technique
- 通信学报 2018年39卷第2期 页码：164-173
- 作者机构：
1. 信息工程大学信息系统工程学院，河南 郑州 450001
2. 61377部队，广东 深圳 518000
3. 清华大学电子系，北京 100084
- 作者简介：
[ "张策（1991-），男，四川南充人，信息工程大学博士生，主要研究方向为无线自组织网络、无线传感网与路由协议。" ]
[ "李鸥（1961-），男，陕西宝鸡人，博士，信息工程大学教授、博士生导师，主要研究方向为无线传感网、认知无线电网络与无线自组织网络。" ]
[ "童昕（1990-），女，湖北黄冈人，61377部队助理工程师，主要研究方向为多天线信号联合处理。" ]
[ "杨延平（1986-），男，河南平顶山人，清华大学博士后，主要研究方向为无线认知通信、网络编码、自适应调制。" ]
- 基金信息：国家科技重大专项基金资助项目(2016zx03001010)
DOI：10.11959/j.issn.1000-436x.2018034
中图分类号： TP393-
网络首发：2018-02，
纸质出版：2018-02-25
- 稿件说明：
移动端阅览
张策, 李鸥, 童昕, 等. 基于压缩感知与矩阵补全技术的WSN数据收集算法[J]. 通信学报, 2018,39(2):164-173.
Ce ZHANG, Ou LI, Xin TONG, et al. WSN data gathering algorithm based on compressive sensing and matrix completion technique[J]. Journal on Communications, 2018, 39(2): 164-173.
张策, 李鸥, 童昕, 等. 基于压缩感知与矩阵补全技术的WSN数据收集算法[J]. 通信学报, 2018,39(2):164-173. DOI： 10.11959/j.issn.1000-436x.2018034.
Ce ZHANG, Ou LI, Xin TONG, et al. WSN data gathering algorithm based on compressive sensing and matrix completion technique[J]. Journal on Communications, 2018, 39(2): 164-173. DOI： 10.11959/j.issn.1000-436x.2018034.
摘要
WSN无线链路不可靠，分组丢失现象普遍存在，且基于压缩感知（CS）数据收集算法对分组丢失十分敏感。首先，通过实验对分组丢失率和基于CS数据重构精度关系进行定量研究，提出极稀疏块观测矩阵，在降低每轮数据采集能耗的同时，也保持观测矩阵的近似低秩性质。其次，结合矩阵补全（MC）技术与CS 技术，提出基于极稀疏块观测矩阵的压缩感知数据收集算法，在一个采集周期内进行数据收集，利用 MC 技术恢复丢失数据，减少分组丢失对数据收集的影响；利用CS技术重构全网数据，减少数据收集量，降低节点在数据收集时所需能耗，延长网络寿命。仿真分析表明，所提算法在分组丢失率小于50%的情况下能够保证高精度重构全网数据，抵抗不可靠链路。
Abstract
The unreliable links and packet losing are ubiquitous in WSN.The performance of data collection algorithm based on compressive sensing is sensitive to packet losing.Firstly
the relationship between packet loss rate and CS-based reconstruction precision was analyzed
and the sparsest block measurement (SBM) matrix was formulated to keep the data gathering consumption smallest and make sure the low-rank property of measurements.Then
combined with the matrix completion (MC) and compressive sensing (CS)
the CS data gathering algorithm based on sparsest block measurement matrix (CS-SBM) algorithm was proposed.CS-SBM gathered data in a period and recovered the loss data based on MC to weaken the impact of packet loss on data gathering.CS-SBM reconstructed data based on CS to reduce measurement number and energy consumption and prolong the network lifetime.Simulation analysis indicates that the proposed algorithm reconstruct the whole data with high-accuracy under 50% packet loss rate
resisting unreliable links effectively.
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Keywords
references
COVER T , HART P . Nearest neighbor pattern classification [J ] . IEEE Transactions on Information Theory , 1967 , 13 ( 1 ): 21 - 27 .
ZHU H , ZHU Y , LI M , et al . SEER:metropolitan-scale traffic perception based on lossy sensory data [C ] // IEEE INFOCOM 2009 . 2009 : 217 - 225 .
DONOHO D L . Compressed sensing [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .
BARANIUK R . Compressive sensing [J ] . IEEE Signal Processing Magazine , 2007 , 56 ( 4 ): 4 - 5 .
LUO C , WU F , SUN J , et al . Compressive data gathering for large-scale wireless sensor networks [C ] // The 15th Annual Int Conf on Mobile Computing and Networking . 2009 : 145 - 156 .
LUO C , WU F , SUN J , et al . Efficient measurement generation and pervasive sparsity for compressive data gathering [J ] . IEEE Transactions on Wireless Communications , 2010 , 9 ( 12 ): 3728 - 3738 .
LUO J , XIANG L , ROSENBERG C . Does compressed sensing improve the throughput of wireless sensor networks? [C ] // IEEE International Conference on Communications (ICC 2010) . 2010 : 1 - 6 .
WU X , XIONG Y , HUANG W , et al . An efficient compressive data gathering routing scheme for large-scale wireless sensor networks [J ] . Computers and Electrical Engineering , 2013 , 39 ( 6 ): 1935 - 1946 .
WU X , YANG P , JUNG T , et al . Compressive sensing meets unreliable link:sparsest random scheduling for compressive data gathering in lossy WSN [C ] // The 15th ACM Int Symposium on Mobile Ad Hoc Networking and Computing . 2014 : 13 - 22 .
张策 , 张霞 , 李鸥 , 等 . 不可靠链路下基于压缩感知的WSN数据收集算法 [J ] . 通信学报 , 2016 , 37 ( 9 ): 131 - 141 .
ZHANG C , ZHANG X , LI O , et al . Compressive sensing based data gathering algorithm over unreliable links in WSN [J ] . Journal on Communications , 2016 , 37 ( 9 ): 131 - 141 .
CHENG J , JIANG H , MA X , et al . Efficient data collection with sampling in WSNs:making use of matrix completion techniques [C ] // Global Telecommunications Conference . 2010 : 1 - 5 .
FRAGKIADAKIS A , ASKOXYLAKIS I , TRAGOS E . Joint compressed-sensing and matrix-completion for efficient data collection in WSNs [C ] // International Workshop on Computer Aided Modeling and Design of Communication Links and Networks . 2014 : 84 - 88 .
CANDES E , RWCHT B . Exact matrix completion via convex optimization [J ] . Foundations of Computational Mathematics , 2009 , 9 ( 6 ): 717 - 772 .
CHENG J , YE Q , JIANG H , et al . STCDG:an efficient data gathering algorithm based on matrix completion for wireless sensor networks [J ] . IEEE Transactions on Wireless Communications , 2013 , 12 ( 2 ): 850 - 861 .
LAKHINA A , PAPAGIANNAKI K , CROVELLA M , et al . Structural analysis of network traffic flows [J ] . ACM SIGMETRICS Performance Evaluation Review , 2004 , 32 ( 1 ): 61 - 72 .
ZHANG Y , ROUGHAN M , WILLINGER W , et al . Spatio-temporal compressive sensing and internet traffic matrices [C ] // ACM SIG COMM 2009 Conference on Data Communication . 2009 : 267 - 278 .
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