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Efficient algorithms for network localization using cores of underlying graphs

机译:使用基础图核心进行网络本地化的高效算法

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摘要

Network localization is important for networks with no prefixed positions of network nodes such as sensor networks. We are given a subset of the set of ({up}n{down}2) pairwise distances among n sensors in some Euclidean space. We want to determine the positions of each sensor from the (partial) distance information. The input can be seen as an edge weighted graph. In this paper, we present some efficient algorithms that solve this problem using the structures of input graphs, which we call their cores. For instance, we present a polynomial-time algorithm solving the network localization problem for graphs with connected dominating sets of bounded size. This algorithm allows us to have fixed-parameter tractable algorithms for some restricted instances such as graphs with connected vertex covers of bounded size.
机译:网络本地化对于没有前缀网络节点(例如传感器网络)的网络非常重要。在某些欧几里得空间中,我们得到了n个传感器之间成对的({up} n {down} 2)对的子集。我们想根据(部分)距离信息确定每个传感器的位置。输入可以看作是边缘加权图。在本文中,我们提出了一些有效的算法,这些算法使用输入图的结构(称为核心)来解决此问题。例如,我们提出了一种多项式时间算法,该算法可以解决具有连接的有界控制集的图形的网络本地化问题。该算法使我们能够对某些受限实例(例如具有连接的有界大小的顶点覆盖的图形)具有固定参数可处理的算法。

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