基于联合稀疏模型的无线传感网数据重构算法

doi:10.11959/j.issn.1000-436x.2016269

Abstract

Abstract:

The data of wireless sensor network has strong joint sparse characteristics,by utilizing compressed sensing theory,compressed data by joint encoding,and then reconstructed the data by joint decoding,the sensed data can be gathered with low computational cost.A synchronous subspace pursuit algorithm based on joint sparse model and com-pressed sensing theory was proposed.By utilizing the sparsity of the sensed data,it selected the correct joint subspace and reconstruct the original signal group accurately with fewer observations in a backtracking iterative manner.Com-pared with SCoSaMP algorithm and SP algorithm,the proposed algorithm presents better data reconstruction perform-ance under the conditions of different sparsity and sampling rate.

Key words: wireless sensor network, compressive sensing, joint sparse model, data reconstruction

Yi-yin LIU,Guo-rui LI,Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on Communications, 2016, 37(Z1): 211-218.

Figures/Tables 6

References 22

[1]	许志强 . 压缩感知[J]. 中国科学， 2012,42(9):865-877. XU Z Q . Compressed sensing[J]. Science China, 2012,42(9):865-877.
[2]	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. DAI Q H , FU C J , JI X Y . Research on compressed sensing[J]. Chinese Journal of Computers, 2011,34(3):425-434.
[3]	DONOHO D L . Compressed sensing[J]. IEEE Transactions on Infor-mation Theory, 2006,52(4):1289-1306.
[4]	UDDIN B , CELEBI M E , KINGRAVI H . Accurate genomic signal recovery using compressed sensing[C]//International Conference on Pattern Recognition. 2012:3144-3147.
[5]	KYRIAKIDES I . Adaptive compressive sensing and processing of delay-doppler radar waveforms[J]. IEEE Transactions on Signal Proc-essing, 2012,60(2):730-739.
[6]	LIU L , CHONG J S , WANG X Q . Adaptive source location estimation based on compressed sensing in wireless sensor networks[J]. Interna-tional Journal of Distributed Sensor Networks, 2012,8(1):1-15.
[7]	LIANG M , LI Y , MENG H . Reconfigurable array design to realize principal component analysis (PCA)-based microwave compressive sensing imaging system[J]. IEEE Antennas and Wireless Propagation Letters, 2015,14:1039-1042.
[8]	AHN J H . Compressive sensing and recovery for binary images[J]. IEEE Transactions on Image Processing, 2016,25(10):4796-4802.
[9]	WANG J , TANG S J , YIN B C . Data gathering in wireless sensor networks through intelligent compressive sensing[C]//International Conference on Computer Communications. 2012:603-611.
[10]	XU L W , QI X , WANG Y X . Efficient data gathering using com-pressed sparse functions[C]//International Conference on Computer Communications. 2013:310-314.
[11]	MALLOY M L , NOWAK R D . Near-optimal adaptive compressed sensing[J]. IEEE Transactions on Information Theory, 2014,60(7):4001-4012.
[12]	LI X L , TAO X F , LIU Y J . Autoregressive model based data gathering algorithm for wireless sensor networks with compressive sens-ing[C]//International Symposium on Personal,Indoor,and Mobile Ra-dio Communications. 2015:2044-2048.
[13]	SHEN Y R , HU W , RANA R . Nonuniform compressive sensing for heterogeneous wireless sensor networks[J]. IEEE Sensors Journal, 2013,13(6):2120-2128.
[14]	方红，杨海蓉 . 贪婪算法与压缩感知理论[J]. 自动化学报， 2011,37(12):1414-1421. FANG H , YANG H R. . Greedy algorithms and compressed sensing[J]. Acta Automatica Sinica, 2011,37(12):1414-1421.
[15]	YU Z L , SU J C , YANG F . Fast compressive sensing reconstruction algorithm on FPGA using orthogonal matching pursuit[C]// Interna-tional Symposium on Circuits and Systems. 2016:249-252.
[16]	LIN Y M , KUO H C , WU A Y . Robust LMS-based compressive sens-ing reconstruction algorithm for noisy wireless sensor net-works[C]//International Conference on Intelligent Green Building and Smart Grid. 2016:1-5.
[17]	李树涛，魏丹 . 压缩传感综述[J]. 自动化学报， 2009,35(11):1370-1377. LI S T , WEI D . A survey on compressive sensing[J]. Acta Automatica Sinica, 2009,35(11):1370-1377.
[18]	CANDèS E J . The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematiqus, 2008,346(9-10):589-592.
[19]	DAI W , MILENKOVIC O . Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009,55(5):2230-2249.
[20]	BLANCHARD J D , CERMAK M , HANLE D . Greedy algorithms for joint sparse recovery[J]. IEEE Transactions on Signal Processing, 2014,62(7):1964-1704.
[21]	康莉，谢维信，黄建军 . 无线传感器网络中的分布式压缩感知技术[J]. 信号处理， 2013,29(11):1560-1567. KANG L , XIE W X , HUANG J J . Distributed compressive sensing for wireless sensor networks[J]. Journal of Signal Processing, 2013,29(11):1560-1567.
[22]	GOGNA A , SHUKLA A , AGARWAL H K . Split bregman algorithms for sparse/joint-sparse and low-rank signal recovery:application in compressive hyperspectral imaging[C]//IEEE International Conference on Image Processing. 2014:1302-1306.

Metrics

Recommended 0

No Suggested Reading articles found!

			SNR/dB
传感数据	观测值
传感数据	观测值
		SCoSaMP	SP	SSP
4	20	32.8	41.6	41.6
4	35	148.4	153.0	153.0
4	60	155.8	157.0	157.0
4	75	155.9	157.2	157.2
20	60	11.7	48.6	48.6
20	80	138.3	153.4	153.4
20	100	150.0	153.6	153.6
20	120	152.1	153.8	153.8
36	100	2.0	106.1	106.1
36	120	136.7	151.9	151.9
36	140	147.6	152.5	152.5
36	160	149.8	152.6	152.6

			还原时间/s
传感数据秩	观测值
传感数据秩	观测值
		SCoSaMP	SP	SSP
4	20	0.016	0.312	0.009
4	35	0.015	0.328	0.015
4	60	0.016	0.343	0.016
4	75	0.016	0.359	0.015
20	60	0.016	0.687	0.016
20	80	0.016	1.138	0.016
20	100	0.015	0.968	0.015
20	120	0.016	0.827	0.016
36	100	0.016	1.513	0.015
36	120	0.031	1.357	0.032
36	140	0.047	1.419	0.047
36	160	0.031	1.404	0.047

Joint sparse model based data reconstruction algorithm for wireless sensor network

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 22

Related Articles 15

Metrics

Recommended 0

[1]	Yue ZHAO, Zan LI, Bing LI, Benjian HAO. TDoA localization-refused area analysis and node placement strategy research [J]. Journal on Communications, 2023, 44(1): 1-13.
[2]	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.
[3]	Cuiran LI, Xuejie WANG, Jianli XIE, Anqi LYU. Routing algorithm for railway monitoring linear WSN based on improved PSO [J]. Journal on Communications, 2022, 43(5): 155-165.
[4]	Xiujuan DU, Lijuan WANG, Jingping LIU, Long JIN. Ranging localization method for nodes in underwater wireless sensor network based on zeroing neural dynamics [J]. Journal on Communications, 2022, 43(10): 177-185.
[5]	Aijing SUN, Shichang LI, Yicai ZHANG. WSN clustering routing algorithm based on PSO optimized fuzzy C-means [J]. Journal on Communications, 2021, 42(3): 91-99.
[6]	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.
[7]	Tianjing WANG,Xiuqin LI,Guangwei BAI,Hang SHEN. Multi-target localization algorithm based on adaptive grid in wireless sensor network [J]. Journal on Communications, 2019, 40(7): 197-207.
[8]	Xiaochen HAO,Ning YAO,Lixia XIE,Jiaojiao WANG,Liyuan WANG. Research on WSN lifetime optimization game algorithm combined power and channel [J]. Journal on Communications, 2019, 40(4): 62-70.
[9]	Linlan LIU,Shengrong GAO,Jian SHU. Link quality prediction based on random forest [J]. Journal on Communications, 2019, 40(4): 202-211.
[10]	Weina YUAN,Qiu YAN. Channel estimation method based on compressive sensing for FBMC/OQAM system [J]. Journal on Communications, 2019, 40(12): 98-104.
[11]	Xiaonian WU,Chuyun ZHANG,Runlian ZHANG,Yaping SUN. Clustering routing protocol based on improved PSO algorithm in WSN [J]. Journal on Communications, 2019, 40(12): 114-123.
[12]	Haoran LIU,Heyao ZHAO,Yujing DENG,Xingqi WANG,Rongrong YIN. Coverage control algorithm for wireless sensor networks based on non-cooperative game [J]. Journal on Communications, 2019, 40(1): 71-78.
[13]	Jian SHU,Manlan LIU,Yaqing SHANG,Yubin CHEN,Linlan LIU. Link quality prediction model based on Gaussian process regression [J]. Journal on Communications, 2018, 39(7): 148-156.
[14]	Feiyun WU,Kunde YANG,Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry [J]. Journal on Communications, 2018, 39(6): 127-132.
[15]	Junbin LIANG,Xiang ZHOU,Taoshen LI. Energy saving proactive neighbor discovery algorithm in mobile low-duty-cycle wireless sensor network [J]. Journal on Communications, 2018, 39(4): 45-55.