Run-length encoding (RLE) data compression algorithm performance analysis on climate datasets for Internet of Things (IoT) application
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Universiti Malaysia Perlis (UniMAP)
2022
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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) |
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Data Compression Run length encoding Internet of Things |
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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 |
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Link to publisher's homepage at http://ijneam.unimap.edu.my |
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asral@unimap.edu.my |
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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 |