运动传感器间的干扰及相关问题研究

J. Carufel, M. J. Katz, Matias Korman, André van Renssen, Marcel Roeloffzen, Shakhar Smorodinsky
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引用次数: 2

摘要

我们表明,对于$\Re^d$和任何参数$2 \le k \le n$中沿“简单”轨迹移动的任何$n$点集(即,每个坐标都用有边界度的多项式描述),可以选择大小为$O(k \log k)$的点的固定非空子集,这样该子集的Voronoi图在任何给定时间都是“平衡的”(即,每个单元格包含$O(n/k)$点)。我们还表明,即使对于点在时间上线性移动的一维情况,界$O(k \log k)$也是接近最优的。作为应用,我们表明可以为$n$移动传感器网络的传感器分配通信半径,以便在任何给定时间它们的干扰为$O(\sqrt{n\log n})$。我们还给出了动力学近似范围计数和动力学差异的一些结果。为了得到这些结果,我们将众所周知的$\varepsilon$ -net理论的结果推广到动力学环境。
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On Interference Among Moving Sensors and Related Problems
We show that for any set of $n$ points moving along "simple" trajectories (i.e., each coordinate is described with a polynomial of bounded degree) in $\Re^d$ and any parameter $2 \le k \le n$, one can select a fixed non-empty subset of the points of size $O(k \log k)$, such that the Voronoi diagram of this subset is "balanced" at any given time (i.e., it contains $O(n/k)$ points per cell). We also show that the bound $O(k \log k)$ is near optimal even for the one dimensional case in which points move linearly in time. As applications, we show that one can assign communication radii to the sensors of a network of $n$ moving sensors so that at any given time their interference is $O(\sqrt{n\log n})$. We also show some results in kinetic approximate range counting and kinetic discrepancy. In order to obtain these results, we extend well-known results from $\varepsilon$-net theory to kinetic environments.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
4
审稿时长
>12 weeks
期刊介绍: The International Journal of Computational Geometry & Applications (IJCGA) is a quarterly journal devoted to the field of computational geometry within the framework of design and analysis of algorithms. Emphasis is placed on the computational aspects of geometric problems that arise in various fields of science and engineering including computer-aided geometry design (CAGD), computer graphics, constructive solid geometry (CSG), operations research, pattern recognition, robotics, solid modelling, VLSI routing/layout, and others. Research contributions ranging from theoretical results in algorithm design — sequential or parallel, probabilistic or randomized algorithms — to applications in the above-mentioned areas are welcome. Research findings or experiences in the implementations of geometric algorithms, such as numerical stability, and papers with a geometric flavour related to algorithms or the application areas of computational geometry are also welcome.
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