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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.


Related in: MedlinePlus

Signals ensembles for (a) relative humidity (RH), (b) solar radiation (SR) and (c) wind speed (WS) for seven different weather stations near Monterey (CA). Each different line in each sensor plot refers to a different node: each kind of sensor presents a different level of correlation among different nodes.
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f5-sensors-15-16654: Signals ensembles for (a) relative humidity (RH), (b) solar radiation (SR) and (c) wind speed (WS) for seven different weather stations near Monterey (CA). Each different line in each sensor plot refers to a different node: each kind of sensor presents a different level of correlation among different nodes.

Mentions: In our experiments, we consider data coming from the CIMIS [37] dataset that manages a network of over 120 automated weather stations in the state of California. We take as the reference the data collected during the 23rd week of 2012 by seven different weather stations near Monterey (CA). For our simulations, we refer to three different kinds of sensors: temperature, relative humidity and wind speed, as reported in Figure 5. The ensemble of signals is chosen, such that it includes periodic and highly correlated signals (temperature and relative humidity) with less correlated signals (wind speed).


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

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

Signals ensembles for (a) relative humidity (RH), (b) solar radiation (SR) and (c) wind speed (WS) for seven different weather stations near Monterey (CA). Each different line in each sensor plot refers to a different node: each kind of sensor presents a different level of correlation among different nodes.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-15-16654: Signals ensembles for (a) relative humidity (RH), (b) solar radiation (SR) and (c) wind speed (WS) for seven different weather stations near Monterey (CA). Each different line in each sensor plot refers to a different node: each kind of sensor presents a different level of correlation among different nodes.
Mentions: In our experiments, we consider data coming from the CIMIS [37] dataset that manages a network of over 120 automated weather stations in the state of California. We take as the reference the data collected during the 23rd week of 2012 by seven different weather stations near Monterey (CA). For our simulations, we refer to three different kinds of sensors: temperature, relative humidity and wind speed, as reported in Figure 5. The ensemble of signals is chosen, such that it includes periodic and highly correlated signals (temperature and relative humidity) with less correlated signals (wind speed).

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.


Related in: MedlinePlus