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The Computer Journal Advance Access published online on July 11, 2007
The Computer Journal, doi:10.1093/comjnl/bxm046
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© The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Reducing Power Consumption in Wireless Sensor Networks Using a Novel Approach to Data Aggregation

Silvio Croce1, Francesco Marcelloni1,* and Massimo Vecchio2

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

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

Received 13 November 2006; revised 28 February 2007

Accepted for publication 25 May 2007.

Saving energy is a very critical issue in wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with a limited capacity. Since the radio is the main cause of power consumption in a sensor node, transmission/reception of data should be limited as much as possible. To this aim, we propose a novel distributed approach to data aggregation based on fuzzy numbers and weighted average operators to reduce data communication in WSNs when we are interested in the estimation of an aggregated value such as maximum or minimum temperature measured in the network. The basic point of our approach is that each node maintains an estimate of the aggregated value. Based on this estimate, the node decides whether a new value measured by the sensor on board the node or received through a message has to be propagated along the network. We show how the lifetime of the network can be estimated through the datasheet of the sensor node and the number of received and transmitted messages. We discuss and evaluate the application of our approach to the monitoring of the maximum temperature in a 100-node simulated WSN and a 12-node real WSN. Finally, we compute the estimates of the lifetimes for both the networks.

Key Words: data aggregation • wireless sensor networks • fuzzy numbers • network operation


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