Title: Efficient information compression in sensor networks

Authors: Song Lin, Vana Kalogeraki, Dimitrios Gunopulos, Stefano Lonardi

Addresses: Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ' Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ' Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ' Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

Abstract: In the emerging area of wireless sensor networks, one of the most typical challenges is to retrieve historical information from the sensor nodes. Due to the resource limitations of sensor nodes (processing, memory, bandwidth and energy), the collected information of sensor nodes has to be compressed quickly and precisely for transmission. In this paper, we propose a new technique – the Adaptive Learning Vector Quantisation (ALVQ) algorithm to compress this historical information. The Adaptive LVQ (ALVQ) algorithm constructs a codebook to capture the prominent features of the data and with these features all the other data can be piece-wise encoded for compression. In addition, we extend our ALVQ algorithm to compress multidimensional information by transforming the multidimensional data into one-dimensional data array. Finally, we consider the problem of transmitting data in a sensor network while maximising the precision. We show how we apply our algorithm so that a set of sensors can dynamically share a wireless communication channel.

Keywords: data compression; sensor networks; bandwidth allocation; multidimensional information compression; wireless sensor networks; wireless networks; learning vector quantisation; adaptive LVQ.

DOI: 10.1504/IJSNET.2006.012038

International Journal of Sensor Networks, 2006 Vol.1 No.3/4, pp.229 - 240

Published online: 12 Jan 2007 *

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