Compressive sensing based data gathering algorithm over unreliable links in WSN

Ce ZHANG; Xia ZHANG; Ou LI; Guan-lin MEI; Zhe HAN; Da-long ZHANG; Guang-yi LIU

doi:10.11959/j.issn.1000-436x.2016185

您当前的位置：

首页 >

文章列表页 >

Compressive sensing based data gathering algorithm over unreliable links in WSN

Papers | 更新时间：2024-06-05

- Compressive sensing based data gathering algorithm over unreliable links in WSN
- Journal on Communications Vol. 37, Issue 9, Pages: 131-141(2016)
- 作者机构：
  
  1. 解放军信息工程大学信息系统工程学院，河南郑州 450001
  2. 郑州大学信息工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Science ＆ Technology Major Project(2014ZX03006003)
- DOI：10.11959/j.issn.1000-436x.2016185
  CLC： TP393
- Online First：2016-09，
  
  Published：25 September 2016
- 稿件说明：
移动端阅览
Ce ZHANG, Xia ZHANG, Ou LI, et al. Compressive sensing based data gathering algorithm over unreliable links in WSN[J]. Journal on Communications, 2016, 37(9): 131-141.
DOI：

Ce ZHANG, Xia ZHANG, Ou LI, et al. Compressive sensing based data gathering algorithm over unreliable links in WSN[J]. Journal on Communications, 2016, 37(9): 131-141. DOI： 10.11959/j.issn.1000-436x.2016185.

摘要

为了解决WSN中基于压缩感知（CS

compressive sensing）的数据收集方法会受不可靠链路影响的问题，首先通过实验对基于 CS 的数据收集算法中数据重构信噪比与链路误码率的关系进行了定量研究，根据 WSN 链路分组丢失特性将分组丢失问题分为轻负载和重负载2种情况。针对轻负载下的链路不可靠，建立随机分组丢失模型，并提出了基于邻居拓扑空间相关预测的CS数据收集算法，利用数据空间相关性减小错传的影响。针对重负载下的链路不可靠，建立节点伪失效模型，并提出了基于稀疏调度的CS数据收集算法，通过改变观测矩阵稀疏度，避免观测出错数据，弱化不可靠链路的影响。仿真分析表明，在不增加能耗的前提下有效提高了数据重构质量，降低了不可靠链路对CS数据收集的影响。

Abstract

To solve the problem that the ubiquitous unreliable links in the WSN influence the performance of the compressive sensing (CS) based data gathering

first the relationship between the reconstruction SNR of CS-based data gathering algorithm and the bit-error-ratio (BER) were simulated quantitatively.Then classify two cases were classified

namely light-payload and heavy-payload

relying on the analysis of wireless link packet loss characteristics.The random packet loss model was conceived to describe the packet loss under light-payload scenario.Further the neighbor topology spatial correlation prediction-based CS data gathering (CS-NTSC) algorithm was proposed

which utilized the nodes spatial correlation to reduce the impact of error.Additionally

the node pseudo-failure model was conceived to describe the packet loss occurred in network congestion

and then the sparse schedule-aided CS data gathering (CS-SSDG) algorithm were conceived

for the purpose of changing the sparsity of measurement matrix and avoiding measurements amongst the nodes affected by unreliable links

thus weakening the impact of error/loss on data reconstruction.Simulation analysis indicates that the proposed algorithms are not only capable of improving the accuracy of the data reconstruction without extra energy

but also effectively reducing the impact affected by the unreliable links imposed on CS-based data gathering.

关键词

Keywords

references

RABBAT M , HAUPT J , SINGH A , et al . Decentralized compression and predistribution via randomized gossiping [C ] // The 5th Int Conf on Information Processing in Sensor Networks . New York:ACM , 2006 : 51 - 59 .

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 . New York:ACM , 2009 : 145 - 156 .

WANG J , TANG S , YIN B , et al . Data gathering in wireless sensor networks through intelligent compressive sensing [C ] // IEEE INFOCOM 2012 . Piscataway,NJ:IEEE , 2012 : 603 - 611 .

DONOHO D L . Compressed sensing [J ] . IEEE Trans on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

BARANIUK R . Compressive sensing [J ] . IEEE Signal Processing Magazine , 2007 , 56 ( 4 ): 4 - 5 .

OSAMY W , SALIM A , AZIZ A . Efficient compressive sensing based technique for routing in wireless sensor networks [J ] . Infocomp Journal of Computer Science , 2013 , 12 ( 1 ): 1 - 9 .

LUO C , WU F , SUN J , et al . Efficient measurement generation and pervasive sparsity for compressive data gathering [J ] . IEEE Trans 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 Int Conf on Communications (ICC 2010) . New York:IEEE Communications Society , 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 & Electrical Engineering , 2013 , 39 ( 6 ): 1935 - 1946 .

AKYILDIZ I F , SU W , SANKARASUBRAMANIAM Y , et al . Wireless sensor networks:a survey [J ] . Computer Networks , 2002 , 38 ( 4 ): 393 - 422 .

NDZI D L , ARIF M A M , SHAKAFF A Y M , et al . Signal propagation analysis for low data rate wireless sensor network applications in sport grounds and on roads [J ] . Progress in Electromagnetics Research , 2012 , 125 ( 17 ): 1 - 19 .

AHMED N , KANHERE S S , JHA S . Utilizing link characterization for improving the performance of aerial wireless sensor networks [J ] . IEEE Journal on Selected Areas in Communications , 2013 , 31 ( 8 ): 1639 - 1649 .

BACCOUR N , KOUBAA A , MOTTOLA L , et al . Radio link quality estimation in wireless sensor networks:a survey [J ] . ACM Transactions on Sensor Networks , 2012 , 8 ( 4 ):688.

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 . New York:ACM , 2014 : 13 - 22 .

唐亮 , 周正 , 石磊 , 等 . 基于 LEACH 和压缩感知的无线传感网目标探测 [J ] . 北京邮电大学学报 , 2011 , 34 ( 3 ): 8 - 11 .

TANG L , ZHOU Z , SHI L , et al . Source detection in wireless sensor network by leach and compressive sensing [J ] . Journal of Beijing University of Posts & Telecommunications , 2011 , 34 ( 3 ): 8 - 11 .

张策 , 张霞 , 李鸥 , 等 . 基于CS的无线传感网动态分簇数据收集算法 [J/OL ] . http://crad.ict.ac/cn/CN/abstract/abstract3059.shtml http://crad.ict.ac/cn/CN/abstract/abstract3059.shtml .

ZHANG C , ZHANG X , LI O , et al . Data gathering using dynamic clustering based on WSN compressive sensing algorithm [J/OL ] . http://crad.ict.ac/cn/CN/abstract/abstract3059.shtml http://crad.ict.ac/cn/CN/abstract/abstract3059.shtml .

WANG W , GAROFALAKIS M , RAMCHANDRAN K . Distributed sparse random projections for refinable approximation [C ] // The 6th Int Conf on Information Processing in Sensor Networks . New York:ACM , 2007 : 331 - 339

KONG L , XIA M , LIU X Y , et al . Data loss and reconstruction in sensor networks [C ] // IEEE INFOCOM 2012 . Piscataway,NJ:IEEE , 2013 : 1654 - 1662 .

WU L , YU K , DU T , et al . Efficient information transmission under lossy WSNs link using compressive sensing [C ] // 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA) . NJ:IEEE , 2014 : 493 - 498 .

Views

1121

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于模糊聚类和时域跟踪的视频分割

Related Author

黄波

杨勇

王桥

吴乐南

Related Institution

东南大学无线电工程系

东南大学无线电工程系江苏南京210096

AI问答

⁰