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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

Comparison of Ecomm, Esm and Ecomp.
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f9-sensors-14-02822: Comparison of Ecomm, Esm and Ecomp.

Mentions: Figures 9 and 10 present comparison snapshots of Ecomm, Esm and Ecomp, normalized by Ecomm for temperature and CO2 sensors when attached to a TelosB mote [65] for N = 1,024 and M = 256 and N = 512 and M = 128, respectively. In summary, these figures and the Table 4, along with Figures 4, 5, 7 and 8, show the potential of CS and DCS in saving sensing and overall energy costs in WSNs. These benefits are coming at the cost of increased complexity at the sink and increased delay. This delay can be problematic in real-time WSN applications.


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

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

Comparison of Ecomm, Esm and Ecomp.
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-14-02822: Comparison of Ecomm, Esm and Ecomp.
Mentions: Figures 9 and 10 present comparison snapshots of Ecomm, Esm and Ecomp, normalized by Ecomm for temperature and CO2 sensors when attached to a TelosB mote [65] for N = 1,024 and M = 256 and N = 512 and M = 128, respectively. In summary, these figures and the Table 4, along with Figures 4, 5, 7 and 8, show the potential of CS and DCS in saving sensing and overall energy costs in WSNs. These benefits are coming at the cost of increased complexity at the sink and increased delay. This delay can be problematic in real-time WSN applications.

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