# WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks

### Cite as:

M. Chatterjee, S.K. Das, and D. Turgut. WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks. Journal of Cluster Computing (Special Issue on Mobile Ad hocNetworks), 5(2):193–204, April 2002.

### BibTeX:

@article{Turgut-2002-JCC,
author = "M. Chatterjee and S.K. Das and D. Turgut",
title = "WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks",
journal = "Journal of Cluster Computing (Special Issue on Mobile Ad hoc
Networks)",
volume = "5",
number = "2",
month = "April",
year = "2002",
pages = "193-204",
abstract = {In this paper, we propose an on-demand distributed clustering
algorithm for multi-hop packet radio networks. These types of networks, also
known as {\em ad hoc} networks, are dynamic in nature due to the mobility of
nodes. The association and dissociation of nodes to and from {\em clusters}
perturb the stability of the network topology, and hence a reconfiguration of
the system is often unavoidable. However, it is vital to keep the topology
stable as long as possible. The {\em clusterheads}, form a {\em dominant set}
in the network, determine the topology and its stability. The proposed
weight-based distributed clustering algorithm takes into consideration the
ideal degree, transmission power, mobility, and battery power of mobile
nodes. The time required to identify the clusterheads depends on the diameter
of the underlying graph. We try to keep the number of nodes in a cluster
around a pre-defined threshold to facilitate the optimal operation of the
medium access control (MAC) protocol. The non-periodic procedure for
clusterhead election is invoked on-demand, and is aimed to reduce the
computation and communication costs. The clusterheads, operating in ''dual''
power mode, connects the clusters which help in routing messages from a node
to any other node. We observe a trade-off between the uniformity of the load
handled by the clusterheads and the connectivity of the network. Simulation
experiments are conducted to evaluate the performance of our algorithm in
terms of the number of clusterheads, {\em reaffiliation} frequency, and
dominant set updates. Results show that our algorithm performs better than
existing ones and is also tunable to different kinds of network conditions.},
}


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