Name
Abstract | ||
|---|---|---|
This paper introduces Stochastic Compressive Data Aggregation (S-CDA) for wireless sensor networks (WSN) under random deployments. The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISWCS.2015.7454307 | 2015 International Symposium on Wireless Communication Systems (ISWCS) |
Keywords | Field | DocType |
stochastic compressive data aggregation,Poisson point process observation,S-CDA,wireless sensor network,WSN random deployment,sparsifying matrix,random discrete Fourier transform,RDFT,smooth signal recovery method,accelerated iterative hard thresholding,AIHT,PPP,stochastic geometry,compressed sensing | Stochastic geometry,Mathematical optimization,Computer science,Matrix (mathematics),Algorithm,Real-time computing,Poisson point process,Thresholding,Discrete Fourier transform,Data aggregator,Wireless sensor network,Compressed sensing | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giancarlo Pastor | 1 | 38 | 4.83 |
| Ilkka Norros | 2 | 613 | 86.52 |
| Riku Jäntti | 3 | 773 | 92.13 |
| Antonio J. Caamaño | 4 | 82 | 12.07 |