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

Entropy based Clustering in Mobile Ad Hoc Networks


Cite as:

D. Turgut, K. Robinson, and M. Chatterjee. Entropy based Clustering in Mobile Ad Hoc Networks. Journal of Ubiquitous Computing and Intelligence (JUCI), 1(1):101–109, April 2007.

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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 monitor and coordinate the activities of all the nodes in the network. Since a single node cannot be delegated to act as a centralized authority because of 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 scheme i.e., identify a subset of nodes among all the nodes that are best suited to be clusterheads. Though there are several clustering algorithms previously proposed; however, to the best of our knowledge, there is none that characterizes the different node parameters in terms of an information theoretic metric. We use entropy as a measure of local and mutual information available to every node. We considered three parameters in the selection procedure, namely, mobility, energy, and degree. Extensive simulations have been conducted and the performance of the proposed clustering scheme has been compared with the Highest Degree and Lowest ID heuristics in terms of the average number of 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:

@article{Turgut-2007-JUCI, 
   author = "D. Turgut and K. Robinson and M. Chatterjee",
   title = "Entropy based Clustering in Mobile Ad Hoc Networks",
   journal = "Journal of Ubiquitous Computing and Intelligence (JUCI)",
   volume = "1",
   number = "1",
   pages = "101-109",
   month = "April",
   year = "2007",
   abstract = {
      The distributiveness of mobile ad hoc networks makes resource allocation
      strategies very challenging since there is no central node to monitor and
      coordinate the activities of all the nodes in the network. Since a single
      node cannot be delegated to act as a centralized authority because of
      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 scheme i.e., identify a subset of nodes among all the
      nodes that are best suited to be clusterheads. Though there are several
      clustering algorithms previously proposed; however, to the best of our
      knowledge, there is none that characterizes the different node parameters
      in terms of an information theoretic metric. We use entropy as a measure
      of local and mutual information available to every node. We considered
      three parameters in the selection procedure, namely, mobility, energy, and
      degree. Extensive simulations have been conducted and the performance of
      the proposed clustering scheme has been compared with the Highest Degree
      and Lowest ID heuristics in terms of the average number of 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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