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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 compressive sensing (CS) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).
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f3-sensors-15-16654: Energy comparison between digital and low-rate compressive sensing (CS) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).

Mentions: Practically, using this kind of measurement matrix means that the node is required only to randomly gather M samples with an under-sampling ratio of order ρ = M/N. As done before, the energy consumption on average after the Nacc sampling period is:(9)Esub=(M⋅(Esetup+Esampl+Estore)+Nacc⋅Esleep+Env+Esend)/NaccIn Figure 3, the comparison between digital and low-rate CS is reported. As inferred from Equations (7) and (9), the energy savings is mainly due to three factors: (1) there is no energy spent in compression for the analog version of CS; (2) the contribution of Esetup, Esampl and Estore is reduced by a factor ρ; and (3) Env is decreased since the number of bytes to store in flash is reduced.


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 compressive sensing (CS) (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

f3-sensors-15-16654: Energy comparison between digital and low-rate compressive sensing (CS) (simulation parameters: Nacc = 512, M = 100, Tsleep = 10 s).
Mentions: Practically, using this kind of measurement matrix means that the node is required only to randomly gather M samples with an under-sampling ratio of order ρ = M/N. As done before, the energy consumption on average after the Nacc sampling period is:(9)Esub=(M⋅(Esetup+Esampl+Estore)+Nacc⋅Esleep+Env+Esend)/NaccIn Figure 3, the comparison between digital and low-rate CS is reported. As inferred from Equations (7) and (9), the energy savings is mainly due to three factors: (1) there is no energy spent in compression for the analog version of CS; (2) the contribution of Esetup, Esampl and Estore is reduced by a factor ρ; and (3) Env is decreased since the number of bytes to store in flash is reduced.

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.