Name
Abstract | ||
|---|---|---|
Collecting data continuously in Wireless Sensor Networks (WSNs) with limited power and bandwidth is still a challenging issue. Recently, the sparse nature of these data motivated the use of Compressive Sensing (CS) as an efficient data gathering technique. In this paper, several algorithms are proposed to effectively exploit the temporal correlation and the sparsity inherent in sensor network data over time. These algorithms combine recent advances in compressive sensing (CS) theory, data compression, and data gathering algorithms. Experimental analysis through simulation evinces that the proposed algorithms significantly reduce the power consumption by reducing the number of sent measurements for the same Normalized Mean Square Error (NMSE). |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VTCSpring.2014.7022923 | Vehicular Technology Conference |
Keywords | Field | DocType |
compressed sensing,correlation methods,data compression,mean square error methods,telecommunication power management,wireless sensor networks,NMSE,WSN,compressive sensing theory,data compression,data gathering algorithms,data gathering technique,normalized mean square error,power consumption,sparse signals,temporal correlation,wireless sensor networks | Data collection,Algorithm design,Computer science,Signal-to-noise ratio,Exploit,Electronic engineering,Bandwidth (signal processing),Data compression,Wireless sensor network,Compressed sensing | Conference |
ISSN | Citations | PageRank |
1550-2252 | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed S. Alwakeel | 1 | 0 | 0.34 |
| Mohamed F. Abdelkader | 2 | 0 | 1.69 |
| Karim G. Seddik | 3 | 59 | 10.63 |
| Atef M. Ghuniem | 4 | 7 | 2.80 |