A Holistic Approach to Reconstruct Data in Ocean Sensor Network Using Compression Sensing

In the complex marine environment, a large-scale wireless sensor network (WSN) is often deployed to resolve the sparsity issue of the signal and to enforce an accurate reconstruction of the signal by upgrading the transmission efficiency. To best implement, such a WSN, we develop a holistic method b...

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Bibliographic Details
Published inIEEE access Vol. 6; pp. 280 - 286
Main Authors Wu, Huafeng, Suo, Meng, Wang, Jun, Mohapatra, Prasant, Cao, Junkuo
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive algorithms
Algorithm design and analysis
Algorithms
Compression sensing
data reconstruction
Discrete cosine transform
Fourier transforms
Marine environment
Matching
Matching pursuit algorithms
Mathematical analysis
ocean sensor network
Oceans
Sensors
Signal processing
Signal reconstruction
Sparsity
Tracking
Transforms
Transmission efficiency
Wireless sensor networks
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Summary:In the complex marine environment, a large-scale wireless sensor network (WSN) is often deployed to resolve the sparsity issue of the signal and to enforce an accurate reconstruction of the signal by upgrading the transmission efficiency. To best implement, such a WSN, we develop a holistic method by considering both raw signal processing and signal reconstruction factors: a node re-ordering scheme based on compression sensing and an improved sparse adaptive tracking algorithm. First, the sensor nodes are reordered at the sink node to improve the sparsity of the compression sensing algorithm in the discrete cosine transformation or Fourier transform domain. After that, we adopt the matching test to estimate sparse degree Kis. At last, we develop a sparse degree adaptive matching tracking framework step-by-step to calculate the approximation of sparsity, and ultimately converge to a precise reconstruction of the signal. In this paper, we employ MATLAB to simulate the algorithm and conduct comprehensive tests. The experimental results show that the proposed method can effectively reduce the sparsity of the signal and deliver an accurate reconstruction of the signal especially in the case of unknown sparsity.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2753240