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

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 paper › Conference paper › peer-review

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 language	English
Title of host publication	Soft Computing Models in Industrial and Environmental Applications
Subtitle of host publication	Proceedings of the 7th International Conference, SOCO’12, Ostrava, Czech Republic, September 5th-7th, 2012
Editors	Václav Snášel, Ajith Abraham, Emilio S. Corchado
Place of Publication	Berlin
Publisher	Springer
Pages	327-336
Number of pages	10
Volume	188
ISBN (Print)	9783642329210
Publication status	Published - 2012
Event	International 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 Sept 2012 → 07 Sept 2012 https://web.archive.org/web/20130713161527/http://einformatica.usal.es/~soco2012/ (Conference website)

Publication series

Name	Advances in Intelligent Systems and Computing

Conference

Conference	International Joint Conference on SOCO, CISIS, ICEUTE 2012
Country/Territory	Czech Republic
City	Ostrava
Period	05/09/12 → 07/09/12
Other	Soft 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	https://web.archive.org/web/20130713161527/http://einformatica.usal.es/~soco2012/ (Conference website)

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, Ostrava, Czech Republic, September 5th-7th, 2012 (Vol. 188, pp. 327-336). (Advances in Intelligent Systems and Computing). Springer.

Kolo, Jonathan Gana ; Ang, Li-Minn ; Shanmugam, S. Anandan et al. / 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, Ostrava, Czech Republic, September 5th-7th, 2012. 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}",

year = "2012",

language = "English",

isbn = "9783642329210",

volume = "188",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "327--336",

editor = "Sn{\'a}{\v s}el, {V{\'a}clav } and Ajith Abraham and {Corchado }, { Emilio S.}",

booktitle = "Soft Computing Models in Industrial and Environmental Applications",

address = "United States",

note = "International Joint Conference on SOCO, CISIS, ICEUTE 2012 : 7th International Conference on Soft Computing Models in Industrial and Environmental Applications ; Conference date: 05-09-2012 Through 07-09-2012",

url = "https://web.archive.org/web/20130713161527/http://einformatica.usal.es/~soco2012/",

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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, Ostrava, Czech Republic, September 5th-7th, 2012. 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 et al.
Soft Computing Models in Industrial and Environmental Applications: Proceedings of the 7th International Conference, SOCO’12, Ostrava, Czech Republic, September 5th-7th, 2012. 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 paper › Conference paper › peer-review

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T1 - A simple data compression algorithm for wireless sensor networks

AU - Kolo, Jonathan Gana

AU - Ang, Li-Minn

AU - Shanmugam, S. Anandan

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

SN - 9783642329210

VL - 188

T3 - Advances in Intelligent Systems and Computing

SP - 327

EP - 336

BT - Soft Computing Models in Industrial and Environmental Applications

A2 - Snášel, Václav

A2 - Abraham, Ajith

A2 - Corchado , Emilio S.

PB - Springer

CY - Berlin

T2 - International Joint Conference on SOCO, CISIS, ICEUTE 2012

Y2 - 5 September 2012 through 7 September 2012

ER -

Kolo JG, Ang LM, 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, Ostrava, Czech Republic, September 5th-7th, 2012. Vol. 188. Berlin: Springer. 2012. p. 327-336. (Advances in Intelligent Systems and Computing).

A simple data compression algorithm for wireless sensor networks

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Conference

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