Name
Title | ||
|---|---|---|
On the benefit of using tight frames for robust data transmission and compressive data gathering in wireless sensor networks |
Abstract | ||
|---|---|---|
Compressive sensing (CS), a new sampling paradigm, has recently found several applications in wireless sensor networks (WSNs). In this paper, we investigate the design of novel sensing matrices which lead to good expected-case performance - a typical performance indicator in practice - rather than the conventional worst-case performance that is usually employed when assessing CS applications. In particular, we show that tight frames perform much better than the common CS Gaussian matrices in terms of the reconstruction average mean squared error (MSE). We also showcase the benefits of tight frames in two WSN applications, which involve: i) robustness to data sample losses; and ii) reduction of the communication cost. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICC.2012.6363655 | ICC |
Keywords | Field | DocType |
wsn,sampling paradigm,signal sampling,compressive sensing,communication cost reduction,wireless sensor network,matrix algebra,gaussian sensing matrix,mse,compressive data gathering,compressed sensing,cs,signal reconstruction,wireless sensor networks,data sample loss,average mean squared error reconstruction,data transmission,losses,mean square error methods,cost reduction,coherence,robustness,sensors,vectors,sparse matrices | Performance indicator,Computer science,Mean squared error,Real-time computing,Robust statistics,Robustness (computer science),Gaussian,Wireless sensor network,Signal reconstruction,Compressed sensing | Conference |
ISSN | ISBN | Citations |
1550-3607 E-ISBN : 978-1-4577-2051-2 | 978-1-4577-2051-2 | 1 |
PageRank | References | Authors |
0.37 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Chen | 1 | 56 | 4.40 |
| Miguel R. D. Rodrigues | 2 | 1500 | 111.23 |
| Ian J. Wassell | 3 | 288 | 35.10 |