Article,
A Cellular Learning Automata Based Clustering Algorithm for Wireless Sensor Networks
Sensor Letters, 6 (5): 723--735 (2008)
DOI: 10.1166/sl.2008.m146
Abstract
In the first part of this paper, we propose a
generalization of cellular learning automata (CLA)
called irregular cellular learning automata (ICLA)
which removes the restriction of rectangular grid
structure in traditional CLA. In the second part of the
paper, based on the proposed model a new clustering
algorithm for sensor networks is designed. The proposed
clustering algorithm is fully distributed and the nodes
in the network don't need to be fully synchronized with
each other. The proposed clustering algorithm consists
of two phases; initial clustering and reclustering.
Unlike existing methods in which the reclustering phase
is performed periodically on the entire network,
reclustering phase in the proposed method is performed
locally whenever it is needed. This results in a
reduction in the consumed energy for reclustering phase
and also allows reclustering phase to be performed as
the network operates. The proposed clustering method in
comparison to existing methods produces a clustering in
which each cluster has higher number of nodes and
higher residual energy for the cluster head. Local
reclustering, higher residual energy in cluster heads
and higher number of nodes in each cluster results in a
network with longer lifetime. To evaluate the
performance of the proposed algorithm several
experiments have been conducted. The results of
experiments have shown that the proposed clustering
algorithm outperforms existing clustering methods in
terms of quality of clustering measured by the total
number of clusters, the number of sparse clusters and
the remaining energy level of the cluster heads.
Experiments have also shown that the proposed
clustering algorithm in comparison to other existing
methods prolongs the network lifetime.
