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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.


Energy comparison between digital and low-rate CS varying the compressed vector size for digital CS and the number of samples gathered for the low-rate CS.
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f4-sensors-15-16654: Energy comparison between digital and low-rate CS varying the compressed vector size for digital CS and the number of samples gathered for the low-rate CS.

Mentions: In Figure 4, the comparison between the energy spent for low-rate and digital CS is reported, normalizing the energy with respect to the energy spent when no compression is applied. The low-rate CS is always more convenient with respect to the digital CS. In the plot is also visible the influence of the packet overhead on the power consumption that creates small abrupt increases in energy consumption when an additional packet has to be sent.


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

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

Energy comparison between digital and low-rate CS varying the compressed vector size for digital CS and the number of samples gathered for the low-rate CS.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-16654: Energy comparison between digital and low-rate CS varying the compressed vector size for digital CS and the number of samples gathered for the low-rate CS.
Mentions: In Figure 4, the comparison between the energy spent for low-rate and digital CS is reported, normalizing the energy with respect to the energy spent when no compression is applied. The low-rate CS is always more convenient with respect to the digital CS. In the plot is also visible the influence of the packet overhead on the power consumption that creates small abrupt increases in energy consumption when an additional packet has to be sent.

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.