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Energy-efficient sensing in wireless sensor networks using compressed sensing.

Razzaque MA, Dobson S - Sensors (Basel) (2014)

Bottom Line: This assumption does not hold in a number of practical applications.Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks.It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudai, JB 81310, Malaysia. marazzaque@utm.my.

ABSTRACT
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

No MeSH data available.


Related in: MedlinePlus

CS in a temporally correlated seismic wave.
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f6-sensors-14-02822: CS in a temporally correlated seismic wave.

Mentions: Figures 1, 2, 3, 4, 5, 6, 7 and 8 present the first part of the results. We present two results for each dataset and their corresponding data correlation (temporal or spatial). One for the sparsification or compressibility test and the other for the signal reconstruction, which visualize the potential of CS/DCS energy-efficient sampling in WSNs. The results of compressibility include the number of significant coefficients in wavelet analysis and their fit with the power law. For the reconstruction, we performed experiments for N = 1,024 and 2,048 for the temperature (temporal) and volcanic (temporal) datasets and N = 512 and 1,024 for CO2 (temporal) with variable M. Due to space limitations, we only present plots for N = 1,024 for temperature and volcanic datasets and N = 512 for CO2, but summarizing all of the results in a table.


Energy-efficient sensing in wireless sensor networks using compressed sensing.

Razzaque MA, Dobson S - Sensors (Basel) (2014)

CS in a temporally correlated seismic wave.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958246&req=5

f6-sensors-14-02822: CS in a temporally correlated seismic wave.
Mentions: Figures 1, 2, 3, 4, 5, 6, 7 and 8 present the first part of the results. We present two results for each dataset and their corresponding data correlation (temporal or spatial). One for the sparsification or compressibility test and the other for the signal reconstruction, which visualize the potential of CS/DCS energy-efficient sampling in WSNs. The results of compressibility include the number of significant coefficients in wavelet analysis and their fit with the power law. For the reconstruction, we performed experiments for N = 1,024 and 2,048 for the temperature (temporal) and volcanic (temporal) datasets and N = 512 and 1,024 for CO2 (temporal) with variable M. Due to space limitations, we only present plots for N = 1,024 for temperature and volcanic datasets and N = 512 for CO2, but summarizing all of the results in a table.

Bottom Line: This assumption does not hold in a number of practical applications.Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks.It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudai, JB 81310, Malaysia. marazzaque@utm.my.

ABSTRACT
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.

No MeSH data available.


Related in: MedlinePlus