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Bounds on minimum number of anchors for iterative localization and its connections to bootstrap percolation

机译:迭代定位的最小锚点界限及其与引导渗透的联系

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Iterated localization is considered where each node of a network needs to get localized (find its location on 2-D plane), when initially only a subset of nodes have their location information. The iterated localization process proceeds as follows. Starting with a subset of nodes that have their location information, possibly using global positioning system (GPS) devices, any other node gets localized if it has three or more localized nodes in its radio range. The newly localized nodes are included in the subset of nodes that have their location information for the next iteration. This process is allowed to continue, until no new node can be localized. The problem is to find the minimum size of the initially localized subset to start with so that the whole network is localized with high probability. There are intimate connections between iterated localization and bootstrap percolation, that is well studied in statistical physics. Using results known in bootstrap percolation, we find a sufficient condition on the size of the initially localized subset that guarantees the localization of all nodes in the network with high probability.
机译:最初只有一小部分节点具有位置信息时,才考虑在网络的每个节点都需要进行本地化(在2D平面上找到其位置)的地方进行迭代定位。迭代的本地化过程如下进行。从具有位置信息的节点子集开始,可能使用全球定位系统(GPS)设备,如果任何其他节点在其无线电范围内具有三个或更多本地化节点,则将对其进行本地化。新定位的节点包含在节点子集中,这些子集的位置信息用于下一次迭代。允许该过程继续进行,直到无法定位任何新节点为止。问题在于找到开始时最初定位的子集的最小大小,以便整个网络以较高的概率定位。迭代定位和自举渗透之间存在密切的联系,这在统计物理学中已得到很好的研究。使用引导渗滤已知的结果,我们在初始定位的子集的大小上找到了充分的条件,可以保证网络中所有节点的定位概率很高。

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