On the Optimality of Non-Uniform Clustering in Wireless Sensor Networks
Date
2010-08-09T16:09:01Z
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Abstract
In wireless sensor networks, cluster-based data gathering has been pursued as a means to achieve
network scalability as well as energy efficiency. By dividing a network into clusters, data aggregation
and compression can be conveniently implemented in each cluster resulting in significant reduction
in overall network energy consumption. Although many clustering algorithms have been proposed in
the literature for minimizing energy consumption in sensor networks, a comprehensive and systematic
analysis of optimal clustering subject to inherent network attributes such as data correlation, node
density, and distance to the sink is still lacking. In particular, existing clustering schemes are designed
to form uniform clusters in the network, where, on average, clusters have the same size. In this paper,
we exploit spatial data correlation present among sensor readings to form optimal-sized clusters that
minimize the total energy cost of the network. We develop a generalized multi-region network model
that captures the interplay between clustering and data correlation, and postulate that a heterogeneous
clustering scheme, with larger clusters at further distances from the sink, is more energy-efficient than
uniform clustering. Based on this model, we develop a distributed randomized clustering algorithm that
generates optimal-sized non-uniform clusters throughout the network. Simulation results confirm the
superiority of our novel algorithm against uniform clustering schemes.
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Keywords
energy-efficiency, clustering, data correlation, wireless sensor networks