Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks
| Autor: | Davide Zordan, Mohsen Hooshmand, Michele Rossi, Michele Zorzi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Spatio-temporal compression compressive sensing performance evaluation wireless sensor networks
Computer science Distributed source coding Sampling (statistics) 020206 networking & telecommunications 02 engineering and technology Covariance Signal symbols.namesake Fourier transform Compressed sensing Sampling (signal processing) Kronecker delta 0202 electrical engineering electronic engineering information engineering symbols Electronic engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Instrumentation Wireless sensor network Algorithm Energy (signal processing) Data compression |
| Zdroj: | IEEE Sensors Journal. 16:1716-1729 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal’s spatio-temporal correlation structure through the Kronecker CS framework. CB-CS’s performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver). |
| Databáze: | OpenAIRE |
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