Big data dynamic compressive sensing system architecture and optimization algorithm for internet of things

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December 2015, 8(6): 1401-1414. doi: 10.3934/dcdss.2015.8.1401

Big data dynamic compressive sensing system architecture and optimization algorithm for internet of things

Jian-Wu Xue ^1, , Xiao-Kun Xu ^1, and Feng Zhang ^2,

1.	School of Management, Northwestern Polytechnical University, Xi'an 710072, China, China
2.	School of Information Engineering, Yulin University, Yulin 719000, China

Received June 2015 Revised August 2015 Published December 2015

Abstract
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In order to reduce the amount of data collected in the Internet of things, to improve the processing speed of big data. To reduce the collected data from Internet of Things by compressed sensing sampling method is proposed. To overcome high computational complexity of compressed sensing algorithms, the search terms of the gradient projection sparse reconstruction algorithm (GPSR-BB) are improved by using multi-objective optimization particle swarm optimization algorithm; it can effectively improve the reconstruction accuracy of the algorithm. Application results show that the proposed multi-objective particle swarm optimization-Genetic algorithm (MOPSOGA) is than traditional GPSR-BB algorithm iterations decreased 51.6%. The success rate of reconstruction is higher than that of the traditional algorithm of 0.15; it's with a better reconstruction performance.

Keywords: Big data, internet of things, optimization algorithm, multi-objective optimization., dynamic compressive sensing.

Mathematics Subject Classification: Primary: 18G35, 60J20; Secondary: 68M11, 47D6.

Citation: Jian-Wu Xue, Xiao-Kun Xu, Feng Zhang. Big data dynamic compressive sensing system architecture and optimization algorithm for internet of things. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1401-1414. doi: 10.3934/dcdss.2015.8.1401

References:

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References:

[1]	H. Ammari, J. Garnier and V. Jugnon, Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 389. Google Scholar
[2]	S. J. Birkinshaw and G. Parkin, A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts,, Journal of Hydroinformatics, 10 (2013), 127. Google Scholar
[3]	B. Bonnard, T. Combot and L. Jassionnesse, Integrability methods in the time minimal coherence transfer for Ising chains of three spins,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4095. doi: 10.3934/dcds.2015.35.4095. Google Scholar
[4]	M. Costantiti, A. Farina and F. Zirilli, The fusion of different resolution SAR images,, in Proceedings of the IEEE. Vol. 85, (1997), 139. doi: 10.1109/5.554214. Google Scholar
[5]	Z. Du, X. Chen and Z. Feng, Multiple positive periodic solutions to a predator-prey model with Leslie-Gower Holling-type II functional response and harvesting terms,, Discrete and Continuous Dynamical Systems - Series S, 7 (2014), 1203. doi: 10.3934/dcdss.2014.7.1203. Google Scholar
[6]	F. Zhang, H.-F. Xue and D.-S. Xu, Big data cleaning algorithms in cloud computing,, International Journal of Online Engineering, 9 (2013), 77. Google Scholar
[7]	K. Zhang, H. Huang and H. Yang, A transformer fault diagnosis method integrating improved genetic algorithm with least square support vector machine,, Power System Technology, 34 (2010), 164. Google Scholar
[8]	J. Li, H. Liu and Q. Wang, Fast imaging of electromagnetic scatterers by a two-stage multilevel sampling method,, Discrete and Continuous Dynamical Systems - Series S, 8 (2015), 547. Google Scholar
[9]	M. Padilla, A. Perera, I. Montoliu, A. Chaudry, K. Persaud and S. Marco, Drift compensation of gas sensor array data by orthogonal signal correction,, Chemometrics and Intelligent Laboratory Systems, 100 (2010), 28. doi: 10.1016/j.chemolab.2009.10.002. Google Scholar
[10]	C. Pohl and J. L. Van Genderen, Image fusionin remote sensing: Concepts, methods and applications,, International Journal of Remote Sensing, 19 (1999), 823. Google Scholar
[11]	V. M. Quiroga and I. Popescu, Cloud and cluster computing in uncertainty analysis of integrated flood models,, Journal of Hydroinformatics, 15 (2013), 55. Google Scholar
[12]	R. Hwang Ryol and M. fred Huber, A particle filter approach fro multi-target tracking intelligent sobots and systems,, Intelligent Robots and Systems, 11 (2007), 2753. Google Scholar
[13]	H. Schättler and U. Ledzewicz, Fields of extremals and sensitivity analysis for multi-input bilinear optimal control problems,, Discrete and Continuous Dynamical Systems - Series A, 35 (2015), 4611. Google Scholar
[14]	D. Tsujinishi and S. Abe, Fuzzy least squares support vector machines for multiclass problems,, Neural Networks, 16 (2003), 785. doi: 10.1016/S0893-6080(03)00110-2. Google Scholar
[15]	B. Üstün and W. J. Melssen, Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization,, Analytical Chimica Acta (S0003-2670), 544 (2005), 0003. Google Scholar

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