Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications

Mauro Mangia; Fabio Pareschi; Rohan Varma; Riccardo Rovatti; Jelena Kovačević; Gianluca Setti

doi:10.1109/TCSII.2018.2821241

Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications

Mauro Mangia, Fabio Pareschi, Rohan Varma, Riccardo Rovatti, Jelena Kovačević, Gianluca Setti

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.

Original language	English (US)
Pages (from-to)	682-686
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	65
Issue number	5
DOIs	https://doi.org/10.1109/TCSII.2018.2821241
State	Published - May 1 2018
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCSII.2018.2821241

Cite this

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abstract = "Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.",

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AU - Pareschi, Fabio

AU - Varma, Rohan

AU - Rovatti, Riccardo

AU - Kovačević, Jelena

AU - Setti, Gianluca

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PY - 2018/5/1

Y1 - 2018/5/1

N2 - Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.

AB - Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness.

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JO - IEEE Transactions on Circuits and Systems II: Express Briefs

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ER -

Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications

Abstract

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ASJC Scopus subject areas

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