K. Robinson, D. Turgut, and M. Chatterjee

Entropy based Clustering in Mobile Ad Hoc Networks


Cite as:

K. Robinson, D. Turgut, and M. Chatterjee. Entropy based Clustering in Mobile Ad Hoc Networks. In Proceedings of IEEE International Conference on Networking, Sensing and Control (ICNSC), pp. 1–5, April 2006.

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Abstract:

The distributiveness of mobile ad hoc networks makes resource allocation strategies very challenging since there is no central node to coordinate and monitor the activities of all the nodes in the network. Since a single node cannot be delegated to act as a centralized authority due to limitations in the transmission range, several delegated nodes may coordinate the activities in certain zones. This methodology is generally referred to as clustering and the nodes are called clusterheads. The clusterheads employ centralized algorithms in its cluster; however, the clusterheads themselves are distributive in nature. In this paper, we propose a clustering method i.e., identify the clusterheads among all the nodes. Though there are several clustering algorithms that have been proposed; however, to the best of our knowledge, there is none that characterizes the different node parameters in terms of entropy. Entropy is a measure of information. We use the local information available to every node to determine the mutual information. We considered two parameters in the selection procedure, namely, energy and mobility. Extensive simulations have been conducted and the performance of the proposed clustering scheme has been shown in terms of the average number of clusterheads or clusters, the average number of cluster changes, and the average connectivity. The results demonstrate that the mutual information captured through entropy is very effective in determining the most suitable clusterheads.

BibTeX:

@inproceedings{Kimberly-2006-ICNSC,
    author = "K. Robinson and D. Turgut and M. Chatterjee",
    title = "Entropy based Clustering in Mobile Ad Hoc Networks",
    booktitle = "Proceedings of IEEE International Conference on Networking, Sensing and Control (ICNSC)",
    location = "Ft. Lauderdale",
    month = "April",
    year = "2006",
    pages = "1-5",
    abstract = {
    The distributiveness of mobile ad hoc networks makes resource
    allocation strategies very challenging since there is no central node to
    coordinate and monitor the activities of all the nodes in the network. Since
    a single node cannot be delegated to act as a centralized authority due to
    limitations in the transmission range, several delegated nodes may
    coordinate the activities in certain zones. This methodology is generally
    referred to as clustering and the nodes are called clusterheads. The
    clusterheads employ centralized algorithms in its cluster; however, the
    clusterheads themselves are distributive in nature. In this paper, we
    propose a clustering method i.e., identify the clusterheads among all the
    nodes. Though there are several clustering algorithms that have been
    proposed; however, to the best of our knowledge, there is none that
    characterizes the different node parameters in terms of entropy. Entropy is
    a measure of information. We use the local information available to every
    node to determine the mutual information. We considered two parameters in
    the selection procedure, namely, energy and mobility. Extensive simulations
    have been conducted and the performance of the proposed clustering scheme
    has been shown in terms of the average number of clusterheads or clusters,
    the average number of cluster changes, and the average connectivity. The
    results demonstrate that the mutual information captured through entropy is
    very effective in determining the most suitable clusterheads. 
    },
}

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