Name
Abstract | ||
|---|---|---|
Compressive Sensing (CS) is a recently developed mechanism that allows signal acquisition and compression to be performed in one inexpensive step so that the sampling process itself produces a compressed version of the signal. This significantly improves systemic energy efficiency because the average sampling rate can be considerably reduced and explicit compression eliminated. In this paper, we introduce a modification to the canonical CS recovery technique that enables even higher gains for event detection applications. We show a practical implementation of this compressive detection with energy constrained wireless sensor nodes and quantify the gains accrued through simulation and experimentation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1594233.1594339 | ISLPED |
Keywords | Field | DocType |
canonical cs recovery technique,systemic energy efficiency,sampling process,explicit compression,wireless sensor network,higher gain,signal acquisition,event detection application,compressive sensing,energy efficient sampling,compressive detection,average sampling rate,design,energy efficient,wireless sensor networks,compressed sensing | Key distribution in wireless sensor networks,Compression (physics),Wireless,Computer science,Efficient energy use,Sampling (signal processing),Electronic engineering,Real-time computing,Sampling (statistics),Wireless sensor network,Compressed sensing | Conference |
Citations | PageRank | References |
15 | 0.88 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zainul Charbiwala | 1 | 150 | 12.93 |
| Younghun Kim | 2 | 446 | 38.54 |
| Sadaf Zahedi | 3 | 1002 | 56.82 |
| Jonathan Friedman | 4 | 584 | 54.98 |
| Mani Srivastava | 5 | 13052 | 1317.38 |