Recovering Lost Sensor Data through Compressed Sensing [Slides]

NESL Technical Report #: 2010-1-3

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Abstract: Data loss in wireless sensing applications is inevitable, both due to communication impairments as well as faulty sensors. We introduce an idea using Compressed Sensing (CS) that exploits knowledge of signal model for recovering lost sensor data. In particular, we show that if the signal to be acquired is compressible, it is possible to use CS not only to reduce the acquisition rate but to also improve robustness to losses.This becomes possible because CS employs randomness within the sampling process and to the receiver, lost data is virtually indistinguishable from randomly sampled data.To ensure performance, all that is required is that the sensor over-sample the phenomena, by a rate proportional to the expected loss. In this talk, we will cover a brief introduction to Compressed Sensing and then illustrate the recovery mechanism we call CS Erasure Coding (CSEC). We show that CSEC is efficient for handling missing data in erasure (lossy) channels, that it parallels the performance of competitive coding schemes and that it is also computationally cheaper. We support our proposal through extensive performance studies on real world wireless channels.
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Recovering Lost Sensor Data through Compressed Sensing
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Publication Forum: CENS Technical Seminar

Date: 2010-01-01

Place: UCLA

Public Document?: Yes

NESL Document?: Yes

Document category: Slides

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