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Le Wang MASTER THESIS PRESENTATION

Le Wang MASTER THESIS PRESENTATION. Evaluation of Compression for Energy-aware Communication in Wireless Networks. Master Thesis Presentation. Supervisor: Professor Jukka Manner Instructor: Sebastian Siikavirta Department of Communications and Networks

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Le Wang MASTER THESIS PRESENTATION

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  1. Le WangMASTER THESIS PRESENTATION Evaluation of Compression for Energy-aware Communication in Wireless Networks

  2. Master Thesis Presentation • Supervisor: Professor Jukka Manner • Instructor: Sebastian Siikavirta • Department of Communications and Networks • Faculty of Electronics, Communications, and Automation • Helsinki University of Technology • 25th, May, 2009

  3. Introduction • This study aims to investigate the usages of data compression to reduce the energy consumption in a hand-held device. • By conducting experiments as the methodologies, the impacts of transmission on energy consumption are explored on wireless interfaces. • 9 lossless compression algorithms are examined on popular Internet traffic in the view of compression ratio, speed and consumed energy. • Energy consumption of uplink, downlink and overall system is investigated to achieve a comprehensive understanding of compression in wireless networks.

  4. Why is it needed • Energy Consumption • ICT infrastructure total: power consumption 2.1 TWh -2.3% of all power consumption in Finland • ICT user terminals total: power consumption 4.6 TWh -5.1% of all power consumption in Finland • Greenhouse gas emissions • ICT contribution to Greenhouse Gas emission: 2.5% = 1.0 GtCO2eq • Mobile user energy consumption is approximate 29kWh = 55 kgco2eq • Battery • UMTS, HSDPA, IEEE802.11b/g and Bluetooth • Camera, GPS, music, movies EFORE Oy,2008

  5. Why is it needed • Economics EFORE Oy,2008

  6. Motivation • Energy consumed on a single bit transmission over wireless is over 1000 times greater than a single 32-bit CPU computation • Compression reduces file sizes • Trade-off between computation and communication

  7. Problems • David Salomon-” Data compression is popular for two reasons: • (1) People like to accumulate data and hate to throw anything away. No matter how big a storage device one has, sooner or later it is going to overflow. Data compression seems useful because it delays this inevitability. • (2) People hate to wait a long time for data transfers.” • Data compression is not energy-oriented. • Blind or unconditional compressions for energy-aware communication related to wireless networks may result in wasting of energy and even slowing down transmission rate.

  8. Compression • Lossy compression is one where compressing data and then decompressing it retrieves data that may well be different from the original • G.711, G.726 and AMR • WMA and MP3 • JPEG and PGF • MPEG, H.261, H.263 and H.264 • Lossless compression is in contrast to represent information which can be recovered into the original data without any mismatch. • Text compression

  9. Compression algorithms • Statistical compression • Huffman Coding, Arithmetic Coding • Dictionary Compression • Static Dictionary, Adaptive Dictionary • Predictive Compression • prediction with partial matching, Burrows-Wheeler transform and context mixing

  10. Methodology • Experiment setup

  11. Methodology

  12. Methodology

  13. RESULTS: Transmission Impact Packet Sizes (UDP) Sending Receiving

  14. RESULTS: Transmission Impact Transmission Rate(UDP) Sending Receiving

  15. RESULTS: Compression Impact Hard-to-compress files

  16. RESULTS: Compression Impact Hard-to-compress files Energy required to compress and send JPG, MP3, WMA and EXE files

  17. RESULTS: Compression Impact Hard-to-compress files Energy required to receive and decompress JPG, MP3, WMA and EXE files

  18. RESULTS: Compression Impact Hard-to-compress files Total energy required to transmit JPG, MP3, WMA and EXE files

  19. RESULTS: Compression Impact The best ratio/time of the compression programs and the corresponding ratio

  20. RESULTS: Compression Impact Easy-to-compress files Energy required to send BIN, HTML, BMP and XML files

  21. RESULTS: Compression Impact Easy-to-compress files Energy required to receiveBIN, HTML, BMP and XML files

  22. RESULTS: Compression Impact Easy-to-compree files Total energy required to transmit BIN, HTML, BMP and XML files

  23. RESULTS: Compression Impact Compressible files Energy required to compress and send PDF and SWF files

  24. RESULTS: Compression Impact Compressible files Energy required to receive and decompress PDF and SWF files

  25. RESULTS: Compression Impact Compressible files Total energy required to transmit PDF and SWF files

  26. Examples

  27. Conclusions • Hard-to-compress files <-> Direct sending -JPG, MP3, EXE and WMA • Easy-to-compress files <-> Compressing first -BIN, HTML, BMP and XML • Compressible files <-> Depending on circumstance -PDF and SWF • Generic compression programs providing great energy savings. -gzip, lzma and lzo • Energy saving with proper usage of compression in wireless networks -Uplink: ~57% -Downlink: ~50% -Overall: ~50%

  28. Future Study • Energy efficiency-driven transmission • Other compression algorithms and programs • Other traffic, wireless interface behavior • Energy consumption of 3G devices • Modeling energy consumption of compression

  29. QUESTIONS?

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