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dc.contributor.authorNor Asilah, Khairi
dc.contributor.authorAsral Bahari, Jambek
dc.date.accessioned2022-05-13T08:08:19Z
dc.date.available2022-05-13T08:08:19Z
dc.date.issued2021-12
dc.identifier.citationInternational Journal of Nanoelectronics and Materials, vol.14 (Special Issue), 2021, pages 191-197en_US
dc.identifier.issn1985-5761 (Printed)
dc.identifier.issn1997-4434 (Online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/75222
dc.descriptionLink to publisher's homepage at http://ijneam.unimap.edu.myen_US
dc.description.abstractWireless 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.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subject.otherData Compressionen_US
dc.subject.otherRun length encodingen_US
dc.subject.otherInternet of Thingsen_US
dc.titleRun-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) applicationen_US
dc.typeArticleen_US
dc.identifier.urlhttp://ijneam.unimap.edu.my
dc.contributor.urlasral@unimap.edu.myen_US


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