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Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding

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Publicado en:Mathematics vol. 13, no. 3 (2025), p. 366
Autor principal: Liu, Quan
Otros Autores: Huang, Ziling, Chen, Kun, Xiao, Jianmin
Publicado:
MDPI AG
Materias:
Wireless communications
Dictionaries
Accuracy
Humidity
Datasets
Time compression
Multidimensional methods
Monitoring
Research & development > R&D
Energy consumption
Data compression
Internet of Things
Coding
Sensors
Longitudinal waves
Buoys
Lossless equipment
Multidimensional data
Algorithms
Energy efficiency
Flags
Methods
Real time
Data transmission
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2227-7390
DOI:10.3390/math13030366
Fuente:Engineering Database