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A Self-Stabilizing K-Clustering Algorithm Using an Arbitrary Metric (Revised Version of RR2008-31)

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Eddy Caron
Ajoy Datta
  • Function : Author
  • PersonId : 830069
Lawrence Larmore
  • Function : Author
  • PersonId : 856333

Abstract

Mobile ad hoc networks as well as grid platforms are distributed, changing, and error prone environments. Communication costs within such infrastructure can be improved, or at least bounded, by using k-clustering. A k-clustering of a graph, is a partition of the nodes into disjoint sets, called clusters, in which every node is distance at most k from a designated node in its cluster, called the clusterhead. A self-stabilizing asynchronous distributed algorithm is given for constructing a k-clustering of a connected network of processes with unique IDs and weighted edges. The algorithm is comparison-based, takes O(nk) time, and uses O(log n + log k) space per process, where n is the size of the network. This is the first distributed solution to the k-clustering problem on weighted graphs.
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Dates and versions

ensl-00440266 , version 1 (10-12-2009)

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  • HAL Id : ensl-00440266 , version 1

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Eddy Caron, Ajoy Datta, Benjamin Depardon, Lawrence Larmore. A Self-Stabilizing K-Clustering Algorithm Using an Arbitrary Metric (Revised Version of RR2008-31). 2009. ⟨ensl-00440266⟩
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