A Simple Data Compression Algorithm for Wireless Sensor Networks

Jonathan Gana Kolo, Li-Minn Ang, S. Anandan Shanmugam, David Wee Gin Lim, Kah Phooi Seng

Research output: Book chapter/Published conference paperConference paper

10 Citations (Scopus)

Abstract

The energy consumption of each wireless sensor node is one of critical issues that require careful management in order to maximize the lifetime of the sensor network since the node is battery powered. The main energy consumer in each node is the communication module that requires energy to transmit and receive data over the air. Data compression is one of possible techniques that can reduce the amount of data exchanged between wireless sensor nodes. In this paper, we proposed a simple lossless data compression algorithm that uses multiple Huffman coding tables to compress WSNs data adaptively. We demonstrate the merits of our proposed algorithm in comparison with recently proposed LEC algorithm using various real-world sensor datasets.
Original languageEnglish
Title of host publicationSoft Computing Models in Industrial and Environmental Applications
Subtitle of host publicationProceedings of the 7th International Conference, SOCO’12
EditorsVáclav Snášel, Ajith Abraham, Emilio S. Corchado
Place of PublicationBerlin
PublisherSpringer
Pages327-336
Number of pages10
Volume188
ISBN (Print)9783642329210
Publication statusPublished - 2012
EventInternational Joint Conference on SOCO, CISIS, ICEUTE 2012: 7th International Conference on Soft Computing Models in Industrial and Environmental Applications - Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava, Czech Republic
Duration: 05 Sep 201207 Sep 2012
https://web.archive.org/web/20130713161527/http://einformatica.usal.es/~soco2012/ (Conference website)

Publication series

NameAdvances in Intelligent Systems and Computing

Conference

ConferenceInternational Joint Conference on SOCO, CISIS, ICEUTE 2012
CountryCzech Republic
CityOstrava
Period05/09/1207/09/12
OtherSoft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate and analyze very complex issues and phenomena. This workshop is mainly focused on its industrial applications.
SOCO 2012 provides interesting opportunities to present and discuss the latest theoretical advances and real world applications in this multidisciplinary research field.
SOCO 2012 will be held in the beautiful and historic city of Ostrava, Czech Republic. With exceptional opportunities for sightseeing and gastronomy, Ostrava is a wonderful venue for a great conference.
Internet address

Cite this

Kolo, J. G., Ang, L-M., Shanmugam, S. A., Lim, D. W. G., & Seng, K. P. (2012). A Simple Data Compression Algorithm for Wireless Sensor Networks. In V. Snášel, A. Abraham, & E. S. Corchado (Eds.), Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12 (Vol. 188, pp. 327-336). (Advances in Intelligent Systems and Computing). Berlin: Springer.
Kolo, Jonathan Gana ; Ang, Li-Minn ; Shanmugam, S. Anandan ; Lim, David Wee Gin ; Seng, Kah Phooi. / A Simple Data Compression Algorithm for Wireless Sensor Networks. Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12. editor / Václav Snášel ; Ajith Abraham ; Emilio S. Corchado . Vol. 188 Berlin : Springer, 2012. pp. 327-336 (Advances in Intelligent Systems and Computing).
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title = "A Simple Data Compression Algorithm for Wireless Sensor Networks",
abstract = "The energy consumption of each wireless sensor node is one of critical issues that require careful management in order to maximize the lifetime of the sensor network since the node is battery powered. The main energy consumer in each node is the communication module that requires energy to transmit and receive data over the air. Data compression is one of possible techniques that can reduce the amount of data exchanged between wireless sensor nodes. In this paper, we proposed a simple lossless data compression algorithm that uses multiple Huffman coding tables to compress WSNs data adaptively. We demonstrate the merits of our proposed algorithm in comparison with recently proposed LEC algorithm using various real-world sensor datasets.",
keywords = "Wireless Sensor Networks, Energy Efficiency, Data Compression, Signal Processing, Adaptive Entropy Encoder, Huffman Coding",
author = "Kolo, {Jonathan Gana} and Li-Minn Ang and Shanmugam, {S. Anandan} and Lim, {David Wee Gin} and Seng, {Kah Phooi}",
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booktitle = "Soft Computing Models in Industrial and Environmental Applications",
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Kolo, JG, Ang, L-M, Shanmugam, SA, Lim, DWG & Seng, KP 2012, A Simple Data Compression Algorithm for Wireless Sensor Networks. in V Snášel, A Abraham & ES Corchado (eds), Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12. vol. 188, Advances in Intelligent Systems and Computing, Springer, Berlin, pp. 327-336, International Joint Conference on SOCO, CISIS, ICEUTE 2012, Ostrava, Czech Republic, 05/09/12.

A Simple Data Compression Algorithm for Wireless Sensor Networks. / Kolo, Jonathan Gana; Ang, Li-Minn; Shanmugam, S. Anandan; Lim, David Wee Gin; Seng, Kah Phooi.

Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12. ed. / Václav Snášel; Ajith Abraham; Emilio S. Corchado . Vol. 188 Berlin : Springer, 2012. p. 327-336 (Advances in Intelligent Systems and Computing).

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - A Simple Data Compression Algorithm for Wireless Sensor Networks

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AU - Lim, David Wee Gin

AU - Seng, Kah Phooi

PY - 2012

Y1 - 2012

N2 - The energy consumption of each wireless sensor node is one of critical issues that require careful management in order to maximize the lifetime of the sensor network since the node is battery powered. The main energy consumer in each node is the communication module that requires energy to transmit and receive data over the air. Data compression is one of possible techniques that can reduce the amount of data exchanged between wireless sensor nodes. In this paper, we proposed a simple lossless data compression algorithm that uses multiple Huffman coding tables to compress WSNs data adaptively. We demonstrate the merits of our proposed algorithm in comparison with recently proposed LEC algorithm using various real-world sensor datasets.

AB - The energy consumption of each wireless sensor node is one of critical issues that require careful management in order to maximize the lifetime of the sensor network since the node is battery powered. The main energy consumer in each node is the communication module that requires energy to transmit and receive data over the air. Data compression is one of possible techniques that can reduce the amount of data exchanged between wireless sensor nodes. In this paper, we proposed a simple lossless data compression algorithm that uses multiple Huffman coding tables to compress WSNs data adaptively. We demonstrate the merits of our proposed algorithm in comparison with recently proposed LEC algorithm using various real-world sensor datasets.

KW - Wireless Sensor Networks

KW - Energy Efficiency

KW - Data Compression

KW - Signal Processing

KW - Adaptive Entropy Encoder

KW - Huffman Coding

M3 - Conference paper

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A2 - Snášel, Václav

A2 - Abraham, Ajith

A2 - Corchado , Emilio S.

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Kolo JG, Ang L-M, Shanmugam SA, Lim DWG, Seng KP. A Simple Data Compression Algorithm for Wireless Sensor Networks. In Snášel V, Abraham A, Corchado ES, editors, Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12. Vol. 188. Berlin: Springer. 2012. p. 327-336. (Advances in Intelligent Systems and Computing).