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Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate.

Brunelli D, Caione C - Sensors (Basel) (2015)

Bottom Line: Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs).In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device.The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.

View Article: PubMed Central - PubMed

Affiliation: University of Trento, Via Sommarive 9, Trento I-38122, Italy. davide.brunelli@unitn.it.

ABSTRACT
Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs). In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device. Using data from a real dataset, we compare an implementation of CS using dense encoding matrices, where samples are gathered at a Nyquist rate, with the reconstruction of signals sampled at a sub-Nyquist rate. The quality of recovery is addressed, and several algorithms are used for reconstruction exploiting the intra- and inter-signal correlation structures. We finally define an optimal under-sampling ratio and reconstruction algorithm capable of achieving the best reconstruction at the minimum energy spent for the compression. The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.

No MeSH data available.


With the same nomenclature previously introduced, this plot highlights the different choices for the measurement and reconstruction phase that permit one to achieve better reconstruction with the minimum energy expenditure.
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f8-sensors-15-16654: With the same nomenclature previously introduced, this plot highlights the different choices for the measurement and reconstruction phase that permit one to achieve better reconstruction with the minimum energy expenditure.

Mentions: In Sections 4 and 5, we have investigated the compression phase, coming to the conclusion that a sparse measurement matrix is the best compression matrix to save energy in compression. Afterwards, in Section 6, we have obtained that, among several reconstruction algorithms and using this sparse measurement matrix, GPSR is the best algorithm capable of guaranteeing the higher reconstruction quality. In Figure 8, a graphical review of the best choices in the measurement and reconstruction phase is reported.


Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate.

Brunelli D, Caione C - Sensors (Basel) (2015)

With the same nomenclature previously introduced, this plot highlights the different choices for the measurement and reconstruction phase that permit one to achieve better reconstruction with the minimum energy expenditure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541899&req=5

f8-sensors-15-16654: With the same nomenclature previously introduced, this plot highlights the different choices for the measurement and reconstruction phase that permit one to achieve better reconstruction with the minimum energy expenditure.
Mentions: In Sections 4 and 5, we have investigated the compression phase, coming to the conclusion that a sparse measurement matrix is the best compression matrix to save energy in compression. Afterwards, in Section 6, we have obtained that, among several reconstruction algorithms and using this sparse measurement matrix, GPSR is the best algorithm capable of guaranteeing the higher reconstruction quality. In Figure 8, a graphical review of the best choices in the measurement and reconstruction phase is reported.

Bottom Line: Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs).In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device.The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.

View Article: PubMed Central - PubMed

Affiliation: University of Trento, Via Sommarive 9, Trento I-38122, Italy. davide.brunelli@unitn.it.

ABSTRACT
Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs). In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device. Using data from a real dataset, we compare an implementation of CS using dense encoding matrices, where samples are gathered at a Nyquist rate, with the reconstruction of signals sampled at a sub-Nyquist rate. The quality of recovery is addressed, and several algorithms are used for reconstruction exploiting the intra- and inter-signal correlation structures. We finally define an optimal under-sampling ratio and reconstruction algorithm capable of achieving the best reconstruction at the minimum energy spent for the compression. The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.

No MeSH data available.