Name
Abstract | ||
|---|---|---|
Compressive sensing(CS) is an emerging technology that can recover a signal from under sampled measurements based on sparsity of the signal in some basis domain. Even though CS associated with wireless sensor networks (WSNs) has contributed in developing efficient compression and detection algorithms, most research has focused on detection problem with a simple model without considering properties of physical channels. This paper considers a compressive sensing based WSN, that exploits channel gain to transmit and detect signals efficiently. Assuming that measured signals at each sensor are correlated and sparse at some basis domain, we propose a novel sensor selection scheme and associated signaling channel design to improve detection performance. The simulation results show that the proposed method support reduction in the number of measurmenets by 60~80% for a wide range of sparsity level at high and low SNRs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WMNC.2013.6548981 | WMNC |
Keywords | Field | DocType |
sensor selection scheme,detection performance,detection algorithms,sparsity level,sensor scheduling,compressive sensing,snr efficient transmission,telecommunication channels,compressive sensing based wsn,compressed sensing,signaling channel design,sampled measurements,wireless sensor networks,telecommunication signalling,signal detection,encoding,sparse matrices,signal to noise ratio,vectors,measurement uncertainty | Compression (physics),Key distribution in wireless sensor networks,Detection theory,Computer science,Computer network,Communication channel,Electronic engineering,Mobile wireless sensor network,Channel gain,Wireless sensor network,Compressed sensing | Conference |
ISBN | Citations | PageRank |
978-1-4673-5614-5 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seunggye Hwang | 1 | 4 | 2.13 |
| Junghun Park | 2 | 27 | 3.31 |
| Dong Ku Kim | 3 | 245 | 60.39 |
| Janghoon Yang | 4 | 136 | 38.21 |