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The Computer Journal Advance Access originally published online on April 30, 2009
The Computer Journal 2009 52(8):969-987; doi:10.1093/comjnl/bxp035
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© The Author 2009. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

This article appears in the following The Computer Journal issue: Incorporating Systems, communications and services in smart homes and Software engineering for e-business Special Issues [View the issue table of contents]

An Efficient Lossless Compression Algorithm for Tiny Nodes of Monitoring Wireless Sensor Networks

Francesco Marcelloni1,* and Massimo Vecchio2

1 Dipartimento di Ingegneria dell’Informazione, University of Pisa, Via Diotisalvi 2, 56122 Pisa, Italy
2 IMT Lucca Institute for Advanced Studies, Piazza S. Ponziano 6, 55100 Lucca, Italy

* Corresponding author: f.marcelloni{at}ing.unipi.it

Received 16 May 2008; revised 9 February 2009

Energy is a primary constraint in the design and deployment of wireless sensor networks (WSNs), since sensor nodes are typically powered by batteries with a limited capacity. Energy efficiency is generally achieved by reducing radio communication, for instance, limiting transmission/reception of data. Data compression can be a valuable tool in this direction. The limited resources available in a sensor node demand, however, the development of specifically designed compression algorithms. In this paper, we propose a simple lossless entropy compression (LEC) algorithm which can be implemented in a few lines of code, requires very low computational power, compresses data on the fly and uses a very small dictionary whose size is determined by the resolution of the analog-to-digital converter. We have evaluated the effectiveness of LEC by compressing four temperature and relative humidity data sets collected by real WSNs, and solar radiation, seismic and ECG data sets. We have obtained compression ratios up to 70.81% and 62.08% for temperature and relative humidity data sets, respectively, and of the order of 70% for the other data sets. Then, we have shown that LEC outperforms two specifically designed compression algorithms for WSNs. Finally, we have compared LEC with gzip, bzip2, rar, classical Huffman and arithmetic encodings.

Key Words: wireless sensor networks • data compression • power saving

Handling editor: Ing-Ray Chen


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