|
 |
|
Papers are presented in PDF format
|
|
 |
• Содержание выпуска •
• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Modelling •
Software and Hardware for Distributed Systems and Supercomputers
Responsible for the Section: Sergei Abramov, Dr. Phys.-Math.Sci.,
corresponding member of RAS
On the left: assigned number of the paper, submission date, the number
of A5 pages contained in the paper,
and the reference to the full-text PDF
/r1/pdf.jpg) .
|
Article #
6_2022
31
p.
/r1/pdf.jpg)
PDF |
submitted on 18th
Feb 2022 displayed on
website on 04th Apr
2022 Yury V.
Shevchuk
Memory-efficient sensor data compression
We treat scalar data compression in sensor network
nodes in streaming mode (compressing data points as they arrive, no
pre-compression buffering). Several experimental algorithms based on
linear predictive coding (LPC) combined with run length encoding (RLE)
are considered. In entropy coding stage we evaluated (a)
variable-length coding with dynamic prefixes generated with MTF-transform,
(b) adaptive width binary coding, and (c) adaptive Golomb-Rice
coding. We provide a comparison of known and experimental
compression algorithms on 75 sensor data sources. Compression ratios
achieved in the tests are about 1.5/4/1000000 (min/med/max), with
compression context size about 10 bytes. (In Russian)
Key words: LPC, linear predictive coding, DTN, delay tolerant
network, Laplace distribution, adaptive compression, bookstack, MTF
transform, RLE, RLGR, prefix code, Elias Gamma coding, Golomb-Rice
coding, vbinary coding. |
|
article citation |
http://psta.psiras.ru/read/psta2022_2_3-33.pdf |
|
DOI |
https://doi.org/10.25209/2079-3316-2022-13-2-3-33 |
|
Article #
7_2022
29
p.
PDF |
submitted on 18th
Feb 2022 displayed on
website on 04th Apr
2022 Yury V.
Shevchuk
Memory-efficient sensor data compression
We treat scalar data compression in sensor network
nodes in streaming mode (compressing data points as they arrive, no
pre-compression buffering). Several experimental algorithms based on
linear predictive coding (LPC) combined with run length encoding (RLE)
are considered. In entropy coding stage we evaluated (a)
variable-length coding with dynamic prefixes generated with MTF-transform,
(b) adaptive width binary coding, and (c) adaptive Golomb-Rice
coding. We provide a comparison of known and experimental
compression algorithms on 75 sensor data sources. Compression ratios
achieved in the tests are about 1.5/4/1000000 (min/med/max), with
compression context size about 10 bytes. (In Russian)
Key words: LPC, linear predictive coding, DTN, delay tolerant
network, Laplace distribution, adaptive compression, bookstack, MTF
transform, RLE, RLGR, prefix code, Elias Gamma coding, Golomb-Rice
coding, vbinary coding. |
|
article citation |
http://psta.psiras.ru/read/psta2022_2_35-63.pdf |
|
DOI |
https://doi.org/10.25209/2079-3316-2022-13-2-35-63 |
• Содержание выпуска •
• Software and Hardware for Distributed Systems and Supercomputers •
• Mathematical Modelling •
|
