基于CS的机载分簇型WSN数据采集方法

doi:10.11959/j.issn.1000-436x.2015197

Abstract

Abstract:

A data acquisition scheme which was suitable for airborne clustering WSN was proposed.On the one hand,this scheme adopts the random compressive sampling could reduce the amount of sampling data of the cluster nodes ef-fectively,and greatly reducing the hardware requirements of the cluster nodes; on the other hand,put forward a MP re-construction method based on composite chaotic-genetic algorithm expressly,which combined the excellent local searching characteristics of chaos theory with the powerful global search ability of genetic algorithm,could improve the signal reconstruction performance of the cluster head or Sink effectively.The experimental results show that,by dimin-ishing the sampling frequency to 1/8 of the original sampling frequency,this random compressive sensing scheme can dramatically reduce the sampling quantity,and the reconstruction precision can reach 10^-7magnitude.This random com-pressive sensing scheme provides a useful idea for practical WSN.

Key words: wireless sensor networks, compressive sensing, matching pursuit, reconstruction, genetic algorithm, chaos

HOUWei Z,INGBo J,UANGYi-feng H,IAOXiao-xuan J,UJia-xing H,IANGWei L. CS-based data collection method for airborne clustering WSN[J]. Journal on Communications, 2015, 36(5): 130-139.

Figures/Tables 11

References 23

[1]	STEVEN M , ALBERT B . The advanced subminiature telemetry system (ASMT):a wireless,no intrusive,network based,instrumentation system[EB/OL]. . 2007.
[2]	ARMSTRONG N L , ANTAR Y M M . Investigation of the electro-magnetic interference threat posed by a wireless network inside a passenger aircraft[J]. IEEE Transactions on Electromagnetic Compatibility, 2008,50(2): 277-284.
[3]	YEDAVALLI R K , BELAPURKAR R K . Application of wireless sensor networks to aircraft control and health management systems[J]. Journal of Control Theory and Applications, 2011,9(1): 28-33.
[4]	JASLEEN K N , GHAZANFAR A S . A wireless sensor network based structural health monitoring system for an airplane[A]. Proceedings of the 17th International Conference on Automation ＆ Computing,Huddersfield[C]. UK, 2011. 240-245.
[5]	JAMES A H J , JOSHUA K , MICHAEL P , et al. Monitoring of aircraft cabin particulate matter concentrations using a wireless sensor network[A]. Proceedings of the 43rd International Conference on Environmental Systems[C]. AIAA, Vail,USA, 2013. 1-17.
[6]	LI L , LI J . Research of compressed sensing theory in WSN data fusion[A]. Proceedings of the 2011 Fourth International Symposium on Computational Intelligence and Design[C]. Hangzhou,China, 2011. 125-128.
[7]	LIU X , LUO J , VASILAKOS A . Compressed data aggregation for energy efficient wireless sensor networks[A]. Proceedings of the 8th Annual IEEE Communications Society Conference on Sensor,Mesh and Ad Hoc Communications and Networks[C]. Salt Lake City,USA, 2011. 46-54.
[8]	ZOU Z Q , HU C C , ZHANG F , et al. WSN data acquisition by combining hierarchical routing method and compressive sensing[J]. Sensors, 2014(14): 16766-16784.
[9]	WANG J , TANG S , YIN B , et al. Data gathering in wireless sensor networks through intelligent compressive sensing[A]. Proceedings of the INFOCOM2012[C]. Orlando,USA, 2012. 603-611.
[10]	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.
[11]	WANG X , ZHAO Z F , XIA Y , et al. Compressed sensing for efficient random routing in multi-hop wireless sensor networks[A]. Proceedings of the 2010 IEEE GLOBECOM Workshops[C]. Miami,USA, 2010. 266-271.
[12]	WU X G , XIONG Y , HUANG W C , et al. An efficient compressive data gathering routing scheme for large-scale wireless sensor networks[J]. Computers and Electrical Engineering, 2013,(39): 1935-1946.
[13]	CANDèS E J , WAKIN M B . An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008,25(2): 21-30.
[14]	余恺，李元实，王智等．基于压缩感知的新型声信号采集方法[J]．仪器仪表学报， 2012,33(1):105-112. YU K , LI Y S , WANG Z , et al. New method for acoustic signal collection based on compressed sampling[J]. Chinese Journal of Scientific Instrument, 2012,33(1):105-112.
[15]	张金成，吕方旭，王钰等． WSN 中的分簇式压缩感知[J]．仪器仪表学报， 2014,35(1):169-177. ZHANG J C , LV F X , WANG Y , et al. Compressive sensing based on clustering network in WSN[J]. Chinese Journal of Scientific Instrument, 2014,35(1):169-177.
[16]	DONOHO D L . Compressed sensing[J]. IEEE Transactions on Information Theory, 2006,52(1): 1289-1306.
[17]	尹宏鹏，刘兆栋，柴毅等．压缩感知综述[J]．控制与决策， 2013,28(10):1441-1445. YIN H P , LIU Z D , CHAI Y , et al. Survey of compressed sensing[J]. Control and Decision, 2013,28(10):1441-1445.
[18]	石光明，刘丹华，高大化等．压缩感知理论及其研究进展[J]．电子学报， 2009,37(5):1070-1081. SHI G M , LIU D H , GAO D H , et al. Advances in theory and application of compressed sensing[J]. Acta Electronica Sinica, 2009,37(5):1070-1081.
[19]	CANDèS E J , ROMBERG J , TAO T . Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans on Information Theory, 2006,52(2): 489-509.
[20]	GAO Q , DUAN C D , FANG X B , et al. A study on matching pursuit based on genetic algorithm[A]. Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation[C]. Shanghai,China, 2011. 283-286.
[21]	SANTOS C . Reliability-redundancy optimization by means of a chaotic differential evolution approach[J]. Chaos Solitons and Fractals, 2009,41(2): 594-602.
[22]	WANG F , DAI Y S , WANG S S . Chaos-genetic algorithm based on the cat map and its application on seimic wavelet estimation[A]. Proceedings of the 2009 International Workshop on Chaos-Fractals Theories and applications[C]. Shenyang,China, 2009. 112-116.
[23]	YU L , BARBOT J P , ZHENG G , et al. Compressive sensing with chaotic sequence[J]. Signal Processing Letters, 2010,17(8): 1-3.

Metrics

Recommended 0

No Suggested Reading articles found!

CS-based data collection method for airborne clustering WSN

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 23

Related Articles 15

Metrics

Recommended 0

方法	指标
方法	ε=0.000 1时的迭代次数	相对速度(倍)
方法1	9	1
方法2	35	26
方法3	28	30
方法4	26	31
方法5	20	37

[1]	Shuangyan YI, Yongsheng LIANG, Jingjing LU, Wei LIU, Tao HU, Zhenyu HE. Robust feature selection method via joint low-rank reconstruction and projection reconstruction [J]. Journal on Communications, 2023, 44(3): 209-219.
[2]	Jin HOU, Xinqiang CHEN. DOA estimation based on geometric sequence decomposition and sparse reconstruction [J]. Journal on Communications, 2023, 44(1): 153-163.
[3]	Ding WANG, Weigang GAO, Zhidong WU. Array amplitude-phase and mutual coupling error joint correction method based on sparse Bayesian [J]. Journal on Communications, 2022, 43(9): 112-120.
[4]	Yanwen WANG, Weimin LEI, Wei ZHANG, Huan MENG, Xinyi CHEN, Wenhui YE, Qingyang JING. Survey on video image reconstruction method based on generative model [J]. Journal on Communications, 2022, 43(9): 194-208.
[5]	Ang LI, Jianxin CHEN, Xin WEI, Liang ZHOU. 6G-oriented cross-modal signal reconstruction technology [J]. Journal on Communications, 2022, 43(6): 28-40.
[6]	Hua REN, Shaozhang NIU, Ruyong REN, Zhen YUE. Research on meaningful image encryption algorithm based on 2-dimensional compressive sensing [J]. Journal on Communications, 2022, 43(5): 45-57.
[7]	Xiaofeng FENG, Jianfeng XU, Chuan HE. Dynamic generalized principal component analysis with applications to fault subspace modeling [J]. Journal on Communications, 2022, 43(5): 92-101.
[8]	Hua YANG, Yu WANG, Siyuan XU, Guoping JIANG. Time hopping short reference carrier index differential chaos shift keying communication scheme [J]. Journal on Communications, 2022, 43(3): 113-123.
[9]	Haixia LIU, Hao YI, Xiangjin MA, Shuyao YUE, Xudong KONG, Pei MA, Yuxin ZENG, Long LI. Indoor wireless signal coverage and enhancement based on passive reconfigurable intelligent metasurface [J]. Journal on Communications, 2022, 43(12): 32-44.
[10]	Hang QIU, Hongbo TANG, Wei YOU, Yu ZHAO, Yi BAI. QGA-based network service extension algorithm in NFV [J]. Journal on Communications, 2022, 43(11): 44-52.
[11]	Yuliang CONG, Wenxi SUN, Ke XUE, Zhihong QIAN, Mianshu CHEN. Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm [J]. Journal on Communications, 2022, 43(10): 77-85.
[12]	Xin SU, Haoyang XUE, Yiqing ZHOU, Jinxiu ZHU. Research on computing offloading method for maritime observation monitoring sensor network [J]. Journal on Communications, 2021, 42(5): 149-163.
[13]	Yong LIAO, Zhirong CAI. Basis expansion model-based improved regularized orthogonal matching pursuit channel estimation for V2X fast time-varying SC-FDMA [J]. Journal on Communications, 2021, 42(4): 177-184.
[14]	Zhaojun WU, Limin ZHANG, Zhaogen ZHONG, Renxin LIU. Reconstruction of sparse check matrix for LDPC at high bit error rate [J]. Journal on Communications, 2021, 42(3): 1-10.
[15]	Zhengquan LI, Yuan LIN, Mengya LI, Yang LIU, Qiong WU, Song XING. Digital modulation recognition based on discriminative restricted Boltzmann machine [J]. Journal on Communications, 2021, 42(2): 81-91.