Limits...
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.


Quality of the reconstruction varying the training data used in the gradient projection-based sparse reconstruction (GPSR) algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541899&req=5

f7-sensors-15-16654: Quality of the reconstruction varying the training data used in the gradient projection-based sparse reconstruction (GPSR) algorithm.

Mentions: As seen in Figure 7, our training data are obtained: (1) exploiting temporal correlation by using data of the same sensor on the same node reconstructed in the previous acquisition cycle; (2) by averaging a maximum of 10 signals reconstructed in the previous acquisition cycles; (3) by using a pseudo-signal obtained combining the raw data gathered by neighbor nodes; and (4) by using a line-powered node taken as the reference providing uncompressed data placed near the compressing node. This last point is a fictitious case taken as the reference, since it is not always possible to have a line-powered node providing a continuous stream of data, but it is useful to evaluate the recovery when spatially-correlated data are used for reconstruction.


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

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

Quality of the reconstruction varying the training data used in the gradient projection-based sparse reconstruction (GPSR) algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-15-16654: Quality of the reconstruction varying the training data used in the gradient projection-based sparse reconstruction (GPSR) algorithm.
Mentions: As seen in Figure 7, our training data are obtained: (1) exploiting temporal correlation by using data of the same sensor on the same node reconstructed in the previous acquisition cycle; (2) by averaging a maximum of 10 signals reconstructed in the previous acquisition cycles; (3) by using a pseudo-signal obtained combining the raw data gathered by neighbor nodes; and (4) by using a line-powered node taken as the reference providing uncompressed data placed near the compressing node. This last point is a fictitious case taken as the reference, since it is not always possible to have a line-powered node providing a continuous stream of data, but it is useful to evaluate the recovery when spatially-correlated data are used for reconstruction.

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.