Compressive data aggregation from Poisson point process observations

Giancarlo Pastor; Ilkka Norros; Riku Jäntti; Antonio Caamaño

doi:10.1109/ISWCS.2015.7454307

Compressive data aggregation from Poisson point process observations

Giancarlo Pastor
, Ilkka Norros
, Riku Jäntti
, Antonio Caamaño

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

Abstract

This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively.

Original language	English
Title of host publication	Wireless Communication Systems (ISWCS), 2015 International Symposium on
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	106-110
ISBN (Electronic)	978-1-4673-6540-6, 978-1-4673-6539-0
DOIs	https://doi.org/10.1109/ISWCS.2015.7454307
Publication status	Published - 2015
MoE publication type	A4 Article in a conference publication
Event	12th International Symposium on Wireless Communication Systems, ISWCS 2015 - Brussels, Belgium Duration: 25 Aug 2015 → 28 Aug 2015 Conference number: 12

Conference

Conference	12th International Symposium on Wireless Communication Systems, ISWCS 2015
Abbreviated title	ISWCS
Country/Territory	Belgium
City	Brussels
Period	25/08/15 → 28/08/15

Keywords

stochastic geometry
point process
compressed sensing
compressive sampling
data aggregation
sensor networks

Access to Document

10.1109/ISWCS.2015.7454307

Cite this

@inproceedings{17dfa6f8918a4a649f79d43d2ec2d062,

title = "Compressive data aggregation from Poisson point process observations",

abstract = "This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively.",

keywords = "stochastic geometry, point process, compressed sensing, compressive sampling, data aggregation, sensor networks",

author = "Giancarlo Pastor and Ilkka Norros and Riku J{\"a}ntti and Antonio Caama{\~n}o",

year = "2015",

doi = "10.1109/ISWCS.2015.7454307",

language = "English",

pages = "106--110",

booktitle = "Wireless Communication Systems (ISWCS), 2015 International Symposium on",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "12th International Symposium on Wireless Communication Systems, ISWCS 2015, ISWCS ; Conference date: 25-08-2015 Through 28-08-2015",

}

Pastor, G, Norros, I, Jäntti, R & Caamaño, A 2015, Compressive data aggregation from Poisson point process observations. in Wireless Communication Systems (ISWCS), 2015 International Symposium on. IEEE Institute of Electrical and Electronic Engineers, pp. 106-110, 12th International Symposium on Wireless Communication Systems, ISWCS 2015, Brussels, Belgium, 25/08/15. https://doi.org/10.1109/ISWCS.2015.7454307

Compressive data aggregation from Poisson point process observations. / Pastor, Giancarlo; Norros, Ilkka; Jäntti, Riku et al.
Wireless Communication Systems (ISWCS), 2015 International Symposium on. IEEE Institute of Electrical and Electronic Engineers, 2015. p. 106-110.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Compressive data aggregation from Poisson point process observations

AU - Pastor, Giancarlo

AU - Norros, Ilkka

AU - Jäntti, Riku

AU - Caamaño, Antonio

N1 - Conference code: 12

PY - 2015

Y1 - 2015

N2 - This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively.

AB - This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals, such as temperature or humidity maps, which consist of few frequency components. The recovery methods are based on the accelerated iterative hard thresholding (AIHT) which sets all but the largest (in magnitude) frequency components to zero. The adoption of the PPP allows to analyze the communication and compression aspects of S-CDA using previous results from stochastic geometry and compressed sensing, respectively.

KW - stochastic geometry

KW - point process

KW - compressed sensing

KW - compressive sampling

KW - data aggregation

KW - sensor networks

U2 - 10.1109/ISWCS.2015.7454307

DO - 10.1109/ISWCS.2015.7454307

M3 - Conference article in proceedings

SP - 106

EP - 110

BT - Wireless Communication Systems (ISWCS), 2015 International Symposium on

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 12th International Symposium on Wireless Communication Systems, ISWCS 2015

Y2 - 25 August 2015 through 28 August 2015

ER -

Compressive data aggregation from Poisson point process observations

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this