Hardware, software and distributed supercomputer systems

Memory-efficient sensor data compression

Yu. V. Shevchuk

Ailamazyan Program Systems Institute of RAS, Ves'kovo, Russia

Abstract: 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.

Key words and phrases: 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.

UDC: 004.627+004.272.45

MSC: Primary 68P30; Secondary 94A45

Received: 18.01.2022
Accepted: 04.04.2022

DOI: 10.25209/2079-3316-2022-13-2-3-33

 English version: , 2022, 13:2, 35–63