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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

Le WangMASTER THESIS PRESENTATION

Evaluation of Compression for Energy-aware Communication in Wireless Networks


Master thesis presentation
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


Introduction
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.


Why is it needed
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


Why is it needed1
Why is it needed

  • Economics

EFORE Oy,2008


Motivation
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


Problems
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.


Compression
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


Compression algorithms
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


Methodology
Methodology

  • Experiment setup




Results transmission impact
RESULTS: Transmission Impact

Packet Sizes (UDP)

Sending

Receiving


Results transmission impact1
RESULTS: Transmission Impact

Transmission Rate(UDP)

Sending

Receiving


Results compression impact
RESULTS: Compression Impact

Hard-to-compress files


Results compression impact1
RESULTS: Compression Impact

Hard-to-compress files

Energy required to compress and send JPG, MP3, WMA and EXE files


Results compression impact2
RESULTS: Compression Impact

Hard-to-compress files

Energy required to receive and decompress JPG, MP3, WMA and EXE files


Results compression impact3
RESULTS: Compression Impact

Hard-to-compress files

Total energy required to transmit JPG, MP3, WMA and EXE files


Results compression impact4
RESULTS: Compression Impact

The best ratio/time of the compression programs and the corresponding ratio


Results compression impact5
RESULTS: Compression Impact

Easy-to-compress files

Energy required to send BIN, HTML, BMP and XML files


Results compression impact6
RESULTS: Compression Impact

Easy-to-compress files

Energy required to receiveBIN, HTML, BMP and XML files


Results compression impact7
RESULTS: Compression Impact

Easy-to-compree files

Total energy required to transmit BIN, HTML, BMP and XML files


Results compression impact8
RESULTS: Compression Impact

Compressible files

Energy required to compress and send PDF and SWF files


Results compression impact9
RESULTS: Compression Impact

Compressible files

Energy required to receive and decompress PDF and SWF files


Results compression impact10
RESULTS: Compression Impact

Compressible files

Total energy required to transmit PDF and SWF files



Conclusions
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%


Future study
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