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Greedy Convex Embeddings for Sensor Networks

机译:传感器网络的贪婪凸嵌入

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摘要

Recent advances in systems of networked sensors have set the stage for smart environments which will have wide-ranging applications from intelligent wildlife monitoring to social applications such as health and elderly care service provisioning. Perhaps the most natural problem in sensor systems is the ¿efficient¿ propagation of a sensed local event. In order to address this problem, the notion of greedy embedding was defined by Papadimitriou and Ratajczak, where the authors conjectured that every 3-connected planar graph has a greedy embedding (possibly planar and convex) in the Euclidean plane. Recently, the greedy embedding conjecture was proved by Leighton and Moitra. However, their algorithm does not result in a drawing that is planar and convex in the Euclidean plane for all 3-connected planar graphs. Here we give a random algorithm for embedding 3-connected planar graphs a greedy convex embedding. Our convex embedding is especially useful for the case of sensor networks, where the position assigned to each sensor is the midpoint of the positions of its neighbors.
机译:网络传感器系统的最新进展为智能环境奠定了基础,智能环境将具有广泛的应用范围,从智能野生生物监控到社会应用,例如健康和老人护理服务提供。传感器系统中最自然的问题也许就是感应局部事件的传播。为了解决这个问题,贪婪嵌入的概念由Papadimitriou和Ratajczak定义,作者推测每个3个连接的平面图在欧几里得平面中都有贪婪嵌入(可能是平面的和凸的)。最近,Leighton和Moitra证明了贪婪嵌入猜想。但是,对于所有3个相连的平面图,他们的算法都不会得出在欧几里得平面上是平面且凸的图形。在这里,我们给出了一个用于将3个相连的平面图嵌入贪婪凸嵌入的随机算法。我们的凸嵌入对于传感器网络尤其有用,在传感器网络中,分配给每个传感器的位置是其相邻位置的中点。

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