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International Journal of Distributed Sensor Networks
Volume 1 (2005), Issue 3-4, Pages 329-344
http://dx.doi.org/10.1080/15501320500330745
Original Article

Self-Stabilizing Global Optimization Algorithms for Large Network Graphs

Department of Computer Science, Clemson University, Clemson, SC, USA

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The paradigm of self-stabilization provides a mechanism to design efficient localized distributed algorithms that are proving to be essential for modern day large networks of sensors. We provide self-stabilizing algorithms (in the shared-variable ID-based model) for three graph optimization problems: a minimal total dominating set (where every node must be adjacent to a node in the set) and its generalizations, a maximal k-packing (a set of nodes where every pair of nodes are more than distance k apart), and a maximal strong matching (a collection of totally disjoint edges).