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Weak Coverage of a Rectangular Barrier

机译:矩形障碍物的覆盖范围较弱

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摘要

Assume n wireless mobile sensors are initially dispersed in an ad hoc manner in a rectangular region. They are required to move to final locations so that they can detect any intruder crossing the region in a direction parallel to the sides of the rectangle, and thus provide weak barrier coverage of the region. We study three optimization problems related to the movement of sensors to achieve weak barrier coverage: minimizing the number of sensors moved (MinNum), minimizing the average distance moved by the sensors (MinSum), and minimizing the maximum distance moved by the sensors (MinMax). We give an O(n~(3/2)) time algorithm for the MinNum problem for sensors of diameter 1 that are initially placed at integer positions; in contrast we show that the problem is NP-hard even for sensors of diameter 2 that are initially placed at integer positions. We show that the MinSum problem is solvable in O(n log n) time for homogeneous range sensors in arbitrary initial positions for the Manhattan metric, while it is NP-hard for heterogeneous sensor ranges for both Manhattan and Euclidean metrics. Finally, we prove that even very restricted homogeneous versions of the MinMax problem are NP-hard.
机译:假设最初以临时方式将n个无线移动传感器分散在一个矩形区域中。他们需要移动到最终位置,以便他们可以检测到在平行于矩形侧面的方向上越过该区域的任何入侵者,从而提供对该区域的较弱的屏障覆盖。我们研究了与传感器的移动相关的三个优化问题,以实现较弱的障碍物覆盖范围:最小化移动的传感器数量(MinNum),最小化传感器移动的平均距离(MinSum)和最小化传感器移动的最大距离(MinMax )。对于最初放置在整数位置的直径为1的传感器的MinNum问题,我们给出了O(n〜(3/2))时间算法。相反,我们表明,即使对于最初放置在整数位置的直径2的传感器,问题也很难解决。我们显示,对于曼哈顿度量的任意初始位置中的均质范围传感器,MinSum问题可在O(n log n)时间内解决,而对于曼哈顿度量和欧几里得度量而言,对于异构传感器范围而言,MinSum问题是NP-难的。最后,我们证明即使是非常有限的MinMax问题的同质版本也是NP-hard。

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