An Entropy-based Clustering in Mobile Ad hoc Networks

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