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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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cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department d529e9b5-30cc-47a5-a0c9-8eeee55f7c96
dc.contributor.author Nor Asilah Khairi
dc.contributor.author Asral Bahari Jambek
dc.date.accessioned 2024-07-30T04:01:27Z
dc.date.available 2024-07-30T04:01:27Z
dc.date.issued 2021-12
dc.description.abstract 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.
dc.identifier.uri https://ijneam.unimap.edu.my/index.php/volume-14-december-2021-special-issue-incape-2021
dc.identifier.uri https://ijneam.unimap.edu.my/
dc.identifier.uri https://hdl.handle.net/20.500.14170/3354
dc.language.iso en
dc.relation.ispartof International Journal of Nanoelectronics and Materials (IJNeaM)
dc.relation.issn 1985-5761
dc.subject Data Compression
dc.subject Run Length Encoding
dc.subject Internet of Things
dc.title Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for internet of things (IoT) application
dc.type Resource Types::text::journal::journal article
dspace.entity.type Publication
oaire.citation.endPage 197
oaire.citation.issue Special Issue INCAPE 2021
oaire.citation.startPage 191
oaire.citation.volume 14
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
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