Mobile ad hoc networks as well as grid platforms are distributed, changing and error prone environments. Communication costs within such infrastructures 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(logn + logk) space per process, where n is the size of the network. To the best of our knowledge, this is the first distributed solution to the k-clustering problem on weighted graphs. © 2009 Springer.
CITATION STYLE
Caron, E., Datta, A. K., Depardon, B., & Larmore, L. L. (2009). A self-stabilizing K-clustering algorithm using an arbitrary metric. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5704 LNCS, pp. 602–614). https://doi.org/10.1007/978-3-642-03869-3_57
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