Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey

Alex Marchioni; Cesar H. Pimentel-Romero; Fabio Pareschi; Mauro Mangia; Riccardo Rovatti; Gianluca Setti

doi:10.1109/ISCAS.2018.8351891

Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey

Alex Marchioni, Cesar H. Pimentel-Romero, Fabio Pareschi, Mauro Mangia, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.

Original language	English (US)
Title of host publication	Proceedings - IEEE International Symposium on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8351891
State	Published - Apr 26 2018
Externally published	Yes

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10.1109/ISCAS.2018.8351891

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title = "Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey",

abstract = "The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.",

author = "Alex Marchioni and Pimentel-Romero, {Cesar H.} and Fabio Pareschi and Mauro Mangia and Riccardo Rovatti and Gianluca Setti",

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Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey. / Marchioni, Alex; Pimentel-Romero, Cesar H.; Pareschi, Fabio et al.
Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers Inc., 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey

AU - Marchioni, Alex

AU - Pimentel-Romero, Cesar H.

AU - Pareschi, Fabio

AU - Mangia, Mauro

AU - Rovatti, Riccardo

AU - Setti, Gianluca

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2018/4/26

Y1 - 2018/4/26

N2 - The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.

AB - The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.

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U2 - 10.1109/ISCAS.2018.8351891

DO - 10.1109/ISCAS.2018.8351891

M3 - Conference contribution

SN - 9781538648810

BT - Proceedings - IEEE International Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Resource Redistribution in Internet of Things applications by Compressed Sensing: A Survey

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