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Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application

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Main Authors: Nor Asilah, Khairi, Asral Bahari, Jambek
Other Authors: asral@unimap.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Data Compression
Run length encoding
Internet of Things
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75222
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id my.unimap-75222
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spelling my.unimap-752222022-05-13T08:08:19Z Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application Nor Asilah, Khairi Asral Bahari, Jambek asral@unimap.edu.my Data Compression Run length encoding Internet of Things Link to publisher's homepage at http://ijneam.unimap.edu.my Wireless sensor nodes play an important role for Internet of Things (IoT) applications. However, these devices often come with limited memory sizes and battery life. Thus, to overcome these problems, this work focuses on studying the data compression algorithm suitable for wireless sensor nodes. In this work, run-length encoding (RLE) compression algorithm performance is studied, especially when compressing various climate datasets. This dataset includes temperature, sea-level pressure, air pollution index, and water level. In our experiment, the RLE algorithm gives the best compression ratio for temperature and sea-level pressure, with 0.62 and 0.63 compression ratios, respectively. These are equivalent to around 40% data saving. For air pollution index and water level dataset, our experiment gives 0.96 and 0.93 compression ratios, respectively. Since this data has a low number of repetitive values, the RLE achieves around 10% saving for this kind of data. 2022-05-13T08:08:19Z 2022-05-13T08:08:19Z 2021-12 Article International Journal of Nanoelectronics and Materials, vol.14 (Special Issue), 2021, pages 191-197 1985-5761 (Printed) 1997-4434 (Online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75222 http://ijneam.unimap.edu.my en Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Data Compression
Run length encoding
Internet of Things
spellingShingle Data Compression
Run length encoding
Internet of Things
Nor Asilah, Khairi
Asral Bahari, Jambek
Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
description Link to publisher's homepage at http://ijneam.unimap.edu.my
author2 asral@unimap.edu.my
author_facet asral@unimap.edu.my
Nor Asilah, Khairi
Asral Bahari, Jambek
format Article
author Nor Asilah, Khairi
Asral Bahari, Jambek
author_sort Nor Asilah, Khairi
title Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
title_short Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
title_full Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
title_fullStr Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
title_full_unstemmed Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
title_sort run-length encoding (rle) data compression algorithm performance analysis on climate datasets for internet of things (iot) application
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75222
_version_ 1738511721684795392
score 13.222552

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