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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 between DCS, the adaptive sampling approach (ASAP) and TC in a spatially correlated signal.
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f17-sensors-14-02822: Comparison between DCS, the adaptive sampling approach (ASAP) and TC in a spatially correlated signal.

Mentions: Figures 13, 14, 15 and 16 present the results for the comparative study between CS,TC, PC-US and PC-AS, and Figures 17 and 18 present the results for the comparative study between DCS, TC and ASAP. We have used fixed N and two values of M in the temperature and CO2 datasets (mentioned in the figures), but one M in the volcanic dataset. The performance is summarized in terms of sensing energy minimized Esmmin, overall energy savings Esaving, Rmean, RMSE and event detection capability in Tables 5 and 6. CS using M1 (CS1) and PC-AS perform less well than TC and PC-US in terms of Rmean and RMSE, but they provide better SR and, hence, better sensing and overall energy savings.


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

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

Comparison between DCS, the adaptive sampling approach (ASAP) and TC in a spatially correlated signal.
© Copyright Policy
Related In: Results  -  Collection

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

f17-sensors-14-02822: Comparison between DCS, the adaptive sampling approach (ASAP) and TC in a spatially correlated signal.
Mentions: Figures 13, 14, 15 and 16 present the results for the comparative study between CS,TC, PC-US and PC-AS, and Figures 17 and 18 present the results for the comparative study between DCS, TC and ASAP. We have used fixed N and two values of M in the temperature and CO2 datasets (mentioned in the figures), but one M in the volcanic dataset. The performance is summarized in terms of sensing energy minimized Esmmin, overall energy savings Esaving, Rmean, RMSE and event detection capability in Tables 5 and 6. CS using M1 (CS1) and PC-AS perform less well than TC and PC-US in terms of Rmean and RMSE, but they provide better SR and, hence, better sensing and overall energy savings.

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