Smart Meter Data Compression using Generalized Deduplication

Marcell Fehér; Niloofar Yazdani; Morten Tranberg   Hansen; Flemming Enevold Vester; Daniel Enrique Lucani Rötter

doi:10.1109/GLOBECOM42002.2020.9322393

Smart Meter Data Compression using Generalized Deduplication

Marcell Fehér, Niloofar Yazdani, Morten Tranberg Hansen, Flemming Enevold Vester, Daniel Enrique Lucani Rötter

Institut for Elektro- og Computerteknologi - Communication, Control and Automation

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

8 Citationer (Scopus)

259 Downloads (Pure)

Abstract

Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.

Originalsprog	Engelsk
Titel	2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings : Proceedings
Antal sider	6
Forlag	IEEE
Publikationsdato	2020
Artikelnummer	9322393
ISBN (Elektronisk)	978-1-7281-8298-8
DOI	https://doi.org/10.1109/GLOBECOM42002.2020.9322393
Status	Udgivet - 2020
Begivenhed	2020 IEEE Global Communications Conference - Taipei, Taiwan + Online (Hybrid), Taipei, Taiwan Varighed: 7 dec. 2020 → 11 dec. 2020 https://globecom2020.ieee-globecom.org/

Konference

Konference	2020 IEEE Global Communications Conference
Lokation	Taipei, Taiwan + Online (Hybrid)
Land/Område	Taiwan
By	Taipei
Periode	07/12/2020 → 11/12/2020
Internetadresse	https://globecom2020.ieee-globecom.org/

Adgang til dokumentet

10.1109/GLOBECOM42002.2020.9322393

1570643131Accepteret manuskript, 570 KB

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Link to publication in Scopus

SCALE-loT - Scalable Systems for Massive loT
Lucani Rötter, D. E. (Deltager)
SCALE-loT - Scalable Systems for Massive loT
01/01/2019 → 31/12/2022
Projekter: Projekt › Forskning
Starting Grant
Lucani Rötter, D. E. (Deltager)
Starting Grant
01/02/2017 → 01/01/2020
Projekter: Projekt › Forskning

Citationsformater

@inproceedings{d7f8f2e8d07a4229af7cb17660b1784a,

title = "Smart Meter Data Compression using Generalized Deduplication",

abstract = "Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.",

keywords = "Compression, IoT, Smart meters, generalized deduplication",

author = "Marcell Feh{\'e}r and Niloofar Yazdani and Hansen, {Morten Tranberg} and Vester, {Flemming Enevold} and {Lucani R{\"o}tter}, {Daniel Enrique}",

year = "2020",

doi = "10.1109/GLOBECOM42002.2020.9322393",

language = "English",

booktitle = "2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings",

publisher = "IEEE",

note = "2020 IEEE Global Communications Conference, Globecom ; Conference date: 07-12-2020 Through 11-12-2020",

url = "https://globecom2020.ieee-globecom.org/",

}

TY - GEN

T1 - Smart Meter Data Compression using Generalized Deduplication

AU - Fehér, Marcell

AU - Yazdani, Niloofar

AU - Hansen, Morten Tranberg

AU - Vester, Flemming Enevold

AU - Lucani Rötter, Daniel Enrique

PY - 2020

Y1 - 2020

N2 - Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.

AB - Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.

KW - Compression

KW - IoT

KW - Smart meters

KW - generalized deduplication

UR - http://www.scopus.com/inward/record.url?scp=85100373108&partnerID=8YFLogxK

U2 - 10.1109/GLOBECOM42002.2020.9322393

DO - 10.1109/GLOBECOM42002.2020.9322393

M3 - Article in proceedings

BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

PB - IEEE

T2 - 2020 IEEE Global Communications Conference

Y2 - 7 December 2020 through 11 December 2020

ER -

Smart Meter Data Compression using Generalized Deduplication

Abstract

Konference

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Fingeraftryk

Projekter

SCALE-loT - Scalable Systems for Massive loT

Starting Grant

Citationsformater