American Institute of Mathematical Sciences

Advanced
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

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:
[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

show all references

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

[1]

Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097
[2]

Yuan-mei Xia, Xin-min Yang, Ke-quan Zhao. A combined scalarization method for multi-objective optimization problems. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020088
[3]

Wenbo Fu, Debnath Narayan. Optimization algorithm for embedded Linux remote video monitoring system oriented to the internet of things (IOT). Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1341-1354. doi: 10.3934/dcdss.2019092
[4]

Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095
[5]

Han Yang, Jia Yue, Nan-jing Huang. Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration. Journal of Industrial & Management Optimization, 2020, 16 (2) : 759-775. doi: 10.3934/jimo.2018177
[6]

Qiang Long, Xue Wu, Changzhi Wu. Non-dominated sorting methods for multi-objective optimization: Review and numerical comparison. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020009
[7]

Liwei Zhang, Jihong Zhang, Yule Zhang. Second-order optimality conditions for cone constrained multi-objective optimization. Journal of Industrial & Management Optimization, 2018, 14 (3) : 1041-1054. doi: 10.3934/jimo.2017089
[8]

Danthai Thongphiew, Vira Chankong, Fang-Fang Yin, Q. Jackie Wu. An on-line adaptive radiation therapy system for intensity modulated radiation therapy: An application of multi-objective optimization. Journal of Industrial & Management Optimization, 2008, 4 (3) : 453-475. doi: 10.3934/jimo.2008.4.453
[9]

Jian Xiong, Zhongbao Zhou, Ke Tian, Tianjun Liao, Jianmai Shi. A multi-objective approach for weapon selection and planning problems in dynamic environments. Journal of Industrial & Management Optimization, 2017, 13 (3) : 1189-1211. doi: 10.3934/jimo.2016068
[10]

Henri Bonnel, Ngoc Sang Pham. Nonsmooth optimization over the (weakly or properly) Pareto set of a linear-quadratic multi-objective control problem: Explicit optimality conditions. Journal of Industrial & Management Optimization, 2011, 7 (4) : 789-809. doi: 10.3934/jimo.2011.7.789
[11]

Lin Jiang, Song Wang. Robust multi-period and multi-objective portfolio selection. Journal of Industrial & Management Optimization, 2019  doi: 10.3934/jimo.2019130
[12]

Yingying Li, Stanley Osher. Coordinate descent optimization for l1 minimization with application to compressed sensing; a greedy algorithm. Inverse Problems & Imaging, 2009, 3 (3) : 487-503. doi: 10.3934/ipi.2009.3.487
[13]

Jae Deok Kim, Ganguk Hwang. Cross-layer modeling and optimization of multi-channel cognitive radio networks under imperfect channel sensing. Journal of Industrial & Management Optimization, 2015, 11 (3) : 807-828. doi: 10.3934/jimo.2015.11.807
[14]

Dušan M. Stipanović, Claire J. Tomlin, George Leitmann. A note on monotone approximations of minimum and maximum functions and multi-objective problems. Numerical Algebra, Control & Optimization, 2011, 1 (3) : 487-493. doi: 10.3934/naco.2011.1.487
[15]

Hamed Fazlollahtabar, Mohammad Saidi-Mehrabad. Optimizing multi-objective decision making having qualitative evaluation. Journal of Industrial & Management Optimization, 2015, 11 (3) : 747-762. doi: 10.3934/jimo.2015.11.747
[16]

Jiao-Yan Li, Xiao Hu, Zhong Wan. An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1203-1220. doi: 10.3934/jimo.2018200
[17]

Adriel Cheng, Cheng-Chew Lim. Optimizing system-on-chip verifications with multi-objective genetic evolutionary algorithms. Journal of Industrial & Management Optimization, 2014, 10 (2) : 383-396. doi: 10.3934/jimo.2014.10.383
[18]

Tien-Fu Liang, Hung-Wen Cheng. Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial & Management Optimization, 2011, 7 (2) : 365-383. doi: 10.3934/jimo.2011.7.365
[19]

Zongmin Li, Jiuping Xu, Wenjing Shen, Benjamin Lev, Xiao Lei. Bilevel multi-objective construction site security planning with twofold random phenomenon. Journal of Industrial & Management Optimization, 2015, 11 (2) : 595-617. doi: 10.3934/jimo.2015.11.595
[20]

Lorena Rodríguez-Gallego, Antonella Barletta Carolina Cabrera, Carla Kruk, Mariana Nin, Antonio Mauttone. Establishing limits to agriculture and afforestation: A GIS based multi-objective approach to prevent algal blooms in a coastal lagoon. Journal of Dynamics & Games, 2019, 6 (2) : 159-178. doi: 10.3934/jdg.2019012

2019 Impact Factor: 1.233

Tools

Metrics

  • PDF downloads (63)
  • HTML views (0)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences