Fast Self-stabilization for Gradients

Authors:
Jacob Beal;Jonathan Bachrach;Dan Vickery;Mark Tobenkin
Affiliations:
BBN Technologies, Cambridge, USA MA 02138;MIT CSAIL, Cambridge, USA MA 02139;MIT CSAIL, Cambridge, USA MA 02139;MIT CSAIL, Cambridge, USA MA 02139
Venue:
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Year:
2009

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Abstract

Gradients are distributed distance estimates used as a building block in many sensor network applications. In large or long-lived deployments, it is important for the estimate to self-stabilize in response to changes in the network or ongoing computations, but existing algorithms may repair very slowly, produce distorted estimates, or suffer large transients. The CRF-Gradient algorithm[1] addresses these shortcomings, and in this paper we prove that it self-stabilizes in O (diameter ) time--more specifically, in 4 ·diameter /c + k seconds, where k is a small constant and c is the minimum speed of multi-hop message propagation.