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

Jian-Wu  Xue; Xiao-Kun  Xu; Feng  Zhang

doi:10.3934/dcdss.2015.8.1401

Article Contents

2015, Volume 8, Issue 6: 1401-1414. Doi: 10.3934/dcdss.2015.8.1401

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

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

Received: May 31, 2015

Revised: July 31, 2015

Published: November 2015

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Abstract

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:

Mathematics Subject Classification: Primary: 18G35, 60J20; Secondary: 68M11, 47D62.

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