Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding
The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we pr...
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MDPI AG
2025-01-01
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| author | Quan Liu Ziling Huang Kun Chen Jianmin Xiao |
| author_facet | Quan Liu Ziling Huang Kun Chen Jianmin Xiao |
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| description | The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism (ERCS_Lossy_Flag), which incorporates a flag bit for each dimension, indicating if the prediction error exceeds a threshold, followed by further compression using Golomb-Rice coding. We conducted experiments on 24-dimensional weather and wave element data from a single buoy, and the results show that ERCS_Lossless achieves an average compression rate of 47.40%. In real communication scenarios, splicing and byte alignment operations are performed on multidimensional data, and the results show that the variance of the payload increases but the mean decreases after compression, realizing a 38.60% transmission energy saving, which is better than existing real-time lossless compression methods. In addition, ERCS_Lossy_Flag further reduces the amount of data and improves energy efficiency when lower data accuracy is acceptable. |
| format | Article |
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| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Mathematics |
| spelling | doaj-art-fa6d5edb5b0645f390e9a630e0471eef2025-08-20T02:12:25ZengMDPI AGMathematics2227-73902025-01-0113336610.3390/math13030366Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice CodingQuan Liu0Ziling Huang1Kun Chen2Jianmin Xiao3School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThe energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism (ERCS_Lossy_Flag), which incorporates a flag bit for each dimension, indicating if the prediction error exceeds a threshold, followed by further compression using Golomb-Rice coding. We conducted experiments on 24-dimensional weather and wave element data from a single buoy, and the results show that ERCS_Lossless achieves an average compression rate of 47.40%. In real communication scenarios, splicing and byte alignment operations are performed on multidimensional data, and the results show that the variance of the payload increases but the mean decreases after compression, realizing a 38.60% transmission energy saving, which is better than existing real-time lossless compression methods. In addition, ERCS_Lossy_Flag further reduces the amount of data and improves energy efficiency when lower data accuracy is acceptable.https://www.mdpi.com/2227-7390/13/3/366multidimensional datareal-time compressionGolomb-Rice codingenergy saving |
| spellingShingle | Quan Liu Ziling Huang Kun Chen Jianmin Xiao Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding Mathematics multidimensional data real-time compression Golomb-Rice coding energy saving |
| title | Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding |
| title_full | Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding |
| title_fullStr | Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding |
| title_full_unstemmed | Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding |
| title_short | Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding |
| title_sort | efficient and real time compression schemes of multi dimensional data from ocean buoys using golomb rice coding |
| topic | multidimensional data real-time compression Golomb-Rice coding energy saving |
| url | https://www.mdpi.com/2227-7390/13/3/366 |
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