Label big data compression in Internet of things based on piecewise linear regression
In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2022.2.0136 |
| _version_ | 1797688734159732736 |
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| author | Su Ming Zhang Kun Zhao Jianwei Babaker Siddiq |
| author_facet | Su Ming Zhang Kun Zhao Jianwei Babaker Siddiq |
| author_sort | Su Ming |
| collection | DOAJ |
| description | In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k < 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified. |
| first_indexed | 2024-03-12T01:34:58Z |
| format | Article |
| id | doaj.art-4624025cfc074fdfa4775c438a800953 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-12T01:34:58Z |
| publishDate | 2023-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-4624025cfc074fdfa4775c438a8009532023-09-11T07:01:09ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562023-01-01811477148610.2478/amns.2022.2.0136Label big data compression in Internet of things based on piecewise linear regressionSu Ming0Zhang Kun1Zhao Jianwei2Babaker Siddiq31Baoding Vocational and Technical College, Baoding, Hebei, 071000, China1Baoding Vocational and Technical College, Baoding, Hebei, 071000, China1Baoding Vocational and Technical College, Baoding, Hebei, 071000, China2College of Administrative Sciences, Applied Science University, BahrainIn order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k < 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.https://doi.org/10.2478/amns.2022.2.0136wireless sensor networksdata compressionenergy efficiency evaluationsource program energy consumption optimization55s36 |
| spellingShingle | Su Ming Zhang Kun Zhao Jianwei Babaker Siddiq Label big data compression in Internet of things based on piecewise linear regression Applied Mathematics and Nonlinear Sciences wireless sensor networks data compression energy efficiency evaluation source program energy consumption optimization 55s36 |
| title | Label big data compression in Internet of things based on piecewise linear regression |
| title_full | Label big data compression in Internet of things based on piecewise linear regression |
| title_fullStr | Label big data compression in Internet of things based on piecewise linear regression |
| title_full_unstemmed | Label big data compression in Internet of things based on piecewise linear regression |
| title_short | Label big data compression in Internet of things based on piecewise linear regression |
| title_sort | label big data compression in internet of things based on piecewise linear regression |
| topic | wireless sensor networks data compression energy efficiency evaluation source program energy consumption optimization 55s36 |
| url | https://doi.org/10.2478/amns.2022.2.0136 |
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