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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 spent in one sampling cycle when CS is used to compress the sample compared to the energy consumed when the sample is sent without compression. The first bar refers to CS when the measurement matrix is obtained from a Bernoulli distribution (T6), while for the second bar, the compression is performed using a Gaussian matrix (T2) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).
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f2-sensors-15-16654: Energy spent in one sampling cycle when CS is used to compress the sample compared to the energy consumed when the sample is sent without compression. The first bar refers to CS when the measurement matrix is obtained from a Bernoulli distribution (T6), while for the second bar, the compression is performed using a Gaussian matrix (T2) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).

Mentions: In Figure 2, the result of simulations is reported when Nacc = 512, M = 100, Tsleep = 10 s with an overhead of 10 bytes for each packet sent. The other parameters in Equations (6) and (8) are derived from these values and the hardware specification data in the datasheets. The two compression matrices used in the simulation when CS is performed are: (T2) Gaussian matrix generated using a Box-Muller transformation with mean zero and variance 1/M and (T6) the matrix generated from the symmetric Bernoulli distribution P(Φjk = ±1) = 1/2. According to Figure 1 these two matrices define the energy consumption boundary for CS.


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

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

Energy spent in one sampling cycle when CS is used to compress the sample compared to the energy consumed when the sample is sent without compression. The first bar refers to CS when the measurement matrix is obtained from a Bernoulli distribution (T6), while for the second bar, the compression is performed using a Gaussian matrix (T2) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-16654: Energy spent in one sampling cycle when CS is used to compress the sample compared to the energy consumed when the sample is sent without compression. The first bar refers to CS when the measurement matrix is obtained from a Bernoulli distribution (T6), while for the second bar, the compression is performed using a Gaussian matrix (T2) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).
Mentions: In Figure 2, the result of simulations is reported when Nacc = 512, M = 100, Tsleep = 10 s with an overhead of 10 bytes for each packet sent. The other parameters in Equations (6) and (8) are derived from these values and the hardware specification data in the datasheets. The two compression matrices used in the simulation when CS is performed are: (T2) Gaussian matrix generated using a Box-Muller transformation with mean zero and variance 1/M and (T6) the matrix generated from the symmetric Bernoulli distribution P(Φjk = ±1) = 1/2. According to Figure 1 these two matrices define the energy consumption boundary for CS.

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.