Memory-efficient sensor data compression

Memory-efficient sensor data compression

Автор: Shevchuk Yury Vladimirovich

Журнал: Программные системы: теория и приложения @programmnye-sistemy

Рубрика: Программное и аппаратное обеспечение распределенных и суперкомпьютерных систем

Статья в выпуске: 2 (53) т.13, 2022 года.

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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.

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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

Короткий адрес: https://sciup.org/143178813

IDR: 143178813   |   DOI: 10.25209/2079-3316-2022-13-2-35-63

Список литературы Memory-efficient sensor data compression

  • K. Fall. "A delay tolerant network architecture for challenged internets", SIGCOMM '03: Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (August 25-29, 2003, Karlsruhe, Germany), ACM, New York, 2003, ISBN 978-1-58113-735-4, pp. 27-34.
  • C. M. Sadler, M. Martonosi. "Data compression algorithms for energy-constrained devices in delay tolerant networks", SenSys '06: Proceedings of the 4th international conference on Embedded networked sensor systems (31 October 2006-3 November 2006, Boulder, Colorado, USA), ACM, New York, 2006, ISBN 978-1-59593-343-0, pp. 265-278.
  • S. Arrabi, J. Lach. "Adaptive lossless compression in wireless body sensor networks", BodyNets '09: Proceedings of the Fourth International Conference on Body Area Networks (April 1-3, 2009, Los Angeles, CA, USA), ICST, Brussels, 2010, ISBN 978-963-9799-41-7.
  • J. G. Kolo, S. A. Shanmugam, D. W. G. Lim, L. M. Ang. "Fast and efficient lossless adaptive compression scheme for wireless sensor networks", Computers & Electrical Engineering, 41 (2015), pp. 275-287
  • F. Huang, Y. Liang. "Towards energy optimization in environmental wireless sensor networks for lossless and reliable data gathering", IEEE International Conference on Mobile Adhoc and Sensor Systems (08-11 October 2007, Pisa, Italy), 2007, pp. 1-6.
  • J. Coalson, Description of the FLAG format
  • T. Robinson. SHORTEN: Simple lossless and near-lossless waveform compression, Technical report CUED/F-INFENG/TR.156, 1994, 16 pp.
  • T. Pelkonen, P. Cavallaro, Q. Huang, S. Franklin, J. Meza, J. Teller, K. Veerara.ghfl.van. "Gorilla.: a. fast, scalable, in-memory time series database", Proceedings of the VLDB Endowment, 8:12 (2015), pp. 1816-1827.
  • E. Lazin. Compression algorithms in Akumuli.
  • F. Jazizadeh, M. Afzalan, B. Becerik-Gerber, L. Soibelman. "EMBED: A dataset for energy monitoring through building electricity disaggregation", e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems (June 12-15, 2018, Karlsruhe, Germany), ACM, New York, 2018, ISBN 978-1-4503-5767-8, pp. 230-235.
  • Digital Humidity Sensor SHTW2 (RH/T), Datasheet, Sensirion AG, Switzerland, 14 pp.
  • D. Vatolin, A. Ratushnyak, M. Smirnov, V. Yukin. Data compression techniques. Archiver internals, compressing images and video, DIALOG-MIFI, M., 2002, isbn 5-86404-170-X (in Russian), 384 pp.
  • P. Ratanaworabhan, J. Ke, M. Burtscher. "Fast lossless compression of scientific floating-point data", Data Compression Conference, DCC'06 (28-30 March 2006, Snowbird, UT, USA), IEEE Computer Society, 2006, ISBN 0-7695-2545-8, pp. 133-142.
  • M. Burtscher, P. Ratanaworabhan. "High throughput compression of double-precision floating-point data", Data Compression Conference, DCC'07 (27-29 March, 2007, Snowbird, UT, USA), IEEE Computer Society, 2007, ISBN 0-7695-2791-4, pp. 293-302.
  • M. Hans, R. W. Schafer. Lossless compression of digital audio, HPL-1999-144, Hewlett-Packard Company, 1999, 37 pp.
  • Yu. A. Reznik. "Coding of prediction residual in MPEG-4 standard for lossless audio coding (MPEG-4 ALS)", 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (17-21 May 2004, Montreal, QC, Canada), 2004, ISBN 0-7803-8484-9.
  • C. C. Cutler. Differential quantization of communication signals, U.S. patent 2605361, 1950.
  • D. Salomon. Data Compression. The Complete Reference, 4th ed., SpringerVerlag, London, 2007, ISBN 978-1-84628-602-5, xxviii+1092 pp.
  • S.W. Golomb. "Run-length encodings", IEEE Transactions on Information Theory, 12:3 (1966), pp. 399-401.
  • R. F. Rice, J. R. Plaunt. "Adaptive variable-length coding for efficient compression of spacecraft television data", IEEE Transactions on Communication Technology, 19:6 (1971), pp. 889-897.
  • N. Faller. "An Adaptive System for Data Compression", 7th Asilomar Conference on Circuits, Systems, and Computers, 1973, pp. 593-597.
  • D. Marpe, H. Schwarz, T. Wiegand. "Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard", IEEE Transactions on Circuits and Systems for Video Technology, 13:7 (2003), pp. 620-636.
  • B.Ya. Ryabko. "Szhatiye dannykh s pomoshch'yu stopki knig", Prob-lemy peredachi informormatsii, XVI:4 (1980), pp. 16-20 (in Russian); B.Ya. Ryabko. "Data compression by means of a "book stack"", Problems of Information Transmission, 16:4 (1980), pp. 265-269.
  • H. M. Malvar. "Adaptive run-length/Golomb-Rice encoding of quantized generalized Gaussian sources with unknown statistics", Data Compression Conference, DCC'06 (28-30 March 2006, Snowbird, UT, USA), IEEE Computer Society, 2006, ISBN 0-7695-2545-8, pp. 23-32.
  • J. Durbin. "The fitting of time-series models", JSTOR: Revue de l'Institut International de Statistique, 28:3 (1960), pp. 233-344.
  • I. Schiopu, A. Munteanu. "Deep-learning-based lossless image coding", IEEE Transactions on Circuits and Systems for Video Technology, 30:7 (2020), pp. 1829-1842.
  • M. Goyal, K. Tatwawadi, S. Chandak, I. Ochoa. "DZip: improved generalpurpose loss less compression based on novel neural network modeling", 2021 Data Compression Conference, DCC (23-26 March 2021, Snowbird, UT, USA), IEEE, 2021, pp. 153-162.
  • Y. Collet, Kucherawy M. (eds.). Zstandard compression and the 'applica-tion/zstd' media type, RFC 8878, 2018, 54 pp.
  • J. Ziv, A. Lempel. "A universal algorithm for sequential data compression", IEEE Transactions on Information Theory, 23:3 (1977), pp. 337-343.
  • J. Duda. Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding, 2014, 24 pp.
  • R. F. Rice, P. S. Yeh, W. Miller. Algorithms for a very high speed universal noiseless coding module, JPL Publication 91-1, Jet Propulsion Laboratory, Pasadena, 1991, 30 pp. url;
  • P. Elias. "Universal codeword sets and representations of the integers", IEEE Transactions on Information Theory, 21:2 (1975), pp. 194-203.
  • J.L. Bentley, D.D. Sleator, R. E. Tarjan, V. K. Wei. "A locally adaptive data compression scheme", Communications of the ACM, 29:4 (1986), pp. 320-330.
  • Yu. Shevchuk. "Vbinary: variable length integer coding revisited", Program, Systems: Theory and Applications, 9:4(39) (2018), pp. 239-252.
  • H. G. Dietz. The Aggregate Magic Algorithms, Aggregate.Org online technical report, LTniversity of Kentucky, 2021.
  • A. Kiely. Selecting the Golomb parameter in Rice coding, IPN Progress Report 42-159, 2004, 18 pp.
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