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Bi-dimensional Signal Compression Based on Linear Prediction Coding: Application to WSN

Published: 29 May 2019 Publication History

Abstract

The big data phenomenon has gained much attention in the wireless communications field. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. In such a context, data compression helps to reduce the amount of data required to represent redundant information while reliably preserving the original content as much as possible. We here consider Compressed Sensing (CS) theory for extracting critical information and representing it with substantially reduced measurements of the original data. For CS application, it is, however, required to design a convenient sparsifying basis or transform. In this work, a large amount of bi-dimensional (2D) correlated signals are considered for compression. The envisaged application is that of data collection in large scale Wireless Sensor Networks. We show that, using CS, it is possible to recover a large amount of data from the collection of a reduced number of sensors readings. In this way, CS use makes it possible to recover large data sets with acceptable accuracy as well as reduced global scale cost. For sparsifying basis search, in addition to conventional sparsity-inducing methods, we propose a new transformation based on Linear Prediction Coding (LPC) that effectively exploits correlation between neighboring data. The steps of data aggregation using CS include sparse compression basis design and then decomposition matrix construction and recovery algorithm application. Comparisons to the case of one-dimensional (1D) reading and to conventional 2D compression methods show the benefit from the better exploitation of the correlation by herein envisaged 2D processing. Simulation results on both synthetic and real WSN data demonstrate that the proposed LPC approach with 2D scenario realizes significant reconstruction performance enhancement compared to former conventional transformations.

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  • (2021)Least-Squares Fuzzy Transforms and Autoencoders: Some Remarks and ApplicationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2020.300744229:1(129-136)Online publication date: Jan-2021

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 15, Issue 3
August 2019
324 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3335317
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 29 May 2019
Accepted: 01 February 2019
Revised: 01 December 2018
Received: 01 December 2017
Published in TOSN Volume 15, Issue 3

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Author Tags

  1. 2D correlated measurements
  2. Data compression and collection
  3. causal
  4. compressed sensing
  5. noncausal processing
  6. prediction error
  7. sparsity-inducing transforms design
  8. uniform spatial correlation

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  • (2021)Least-Squares Fuzzy Transforms and Autoencoders: Some Remarks and ApplicationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2020.300744229:1(129-136)Online publication date: Jan-2021

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