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

Department of Electrical and Computer Engineering - Communication, Control and Automation

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

9 Citations (Scopus)

260 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.

Original language	English
Title of host publication	2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings : Proceedings
Number of pages	6
Publisher	IEEE
Publication date	2020
Article number	9322393
ISBN (Electronic)	978-1-7281-8298-8
DOIs	https://doi.org/10.1109/GLOBECOM42002.2020.9322393
Publication status	Published - 2020
Event	2020 IEEE Global Communications Conference - Taipei, Taiwan + Online (Hybrid), Taipei, Taiwan Duration: 7 Dec 2020 → 11 Dec 2020 https://globecom2020.ieee-globecom.org/

Conference

Conference	2020 IEEE Global Communications Conference
Location	Taipei, Taiwan + Online (Hybrid)
Country/Territory	Taiwan
City	Taipei
Period	07/12/2020 → 11/12/2020
Internet address	https://globecom2020.ieee-globecom.org/

Keywords

Compression
IoT
Smart meters
generalized deduplication

Access to Document

10.1109/GLOBECOM42002.2020.9322393

1570643131Accepted manuscript, 570 KB

Cite this

@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

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Smart Meter Data Compression using Generalized Deduplication

Abstract

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Keywords

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SCALE-loT - Scalable Systems for Massive loT

Starting Grant

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