A Flexible Stochastic Automaton-Based Algorithm for Network Self-Partitioning

Authors:
Yan Wan;Sandip Roy;Ali Saberi;Bernard Lesieutre
Affiliations:
Washington State University, Pullman, WA, USA;Washington State University, Pullman, WA, USA;Washington State University, Pullman, WA, USA;Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Venue:
International Journal of Distributed Sensor Networks
Year:
2008

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Abstract

This article proposes a flexible and distributed stochastic automaton-based network partitioning algorithm that is capable of finding the optimal k-way partition with respect to a broad range of cost functions, and given various constraints, in directed and weighted graphs. Specifically, we motivate the distributed partitioning (self-partitioning) problem, introduce the stochastic automaton-based partitioning algorithm, and show that the algorithm finds the optimal partition with probability 1 for a large class of partitioning tasks. Also, a discussion of why the algorithm can be expected to find good partitions quickly is included, and its performance is further illustrated through examples. Finally, applications to mobile/sensor classification in ad hoc networks, fault-isolation in electric power systems, and control of autonomous vehicle teams are pursued in detail.