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Fast Self-stabilization for Gradients

机译:梯度的快速自稳定

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摘要

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.
机译:梯度是分布式距离估计,在许多传感器网络应用中用作构建块。在大型或长期部署中,对估计值进行自我稳定以响应网络变化或正在进行的计算非常重要,但是现有算法可能修复得非常慢,产生失真的估计值或遭受较大的瞬变。 CRF-Gradient算法[1]解决了这些缺点,在本文中,我们证明了它在O(直径)时间内更稳定,更具体地说,在4·直径/ c + k秒内,其中k是一个小常数,并且c是多跳消息传播的最小速度。

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