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Optimal Online and Offline Algorithms for Robot-Assisted Restoration of Barrier Coverage

机译:用于机器人辅助恢复障碍覆盖的最佳在线和离线算法

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Cooperation between mobile robots and wireless sensor networks is a line of research that is currently attracting a lot of attention. In this context, we study the following problem of barrier coverage by stationary wireless sensors that are assisted by a mobile robot with the capacity to move sensors. Assume that n sensors are initially arbitrarily distributed on a line segment barrier. Each sensor is said to cover the portion of the barrier that intersects with its sensing area. Owing to incorrect initial position, or the death of some of the sensors, the barrier is not completely covered by the sensors. We employ a mobile robot to move the sensors to final positions on the barrier such that barrier coverage is guaranteed. We seek algorithms that minimize the length of the robot's trajectory, since this allows the restoration of barrier coverage as soon as possible. We give an optimal linear-time offline algorithm that gives a minimum-length trajectory for a robot that starts at one end of the barrier and achieves the restoration of barrier coverage. We also study two different online models: one in which the online robot does not know the length of the barrier in advance, and the other in which the online robot knows it in advance. For the case when the online robot does not know the length of the barrier, we prove a tight bound of 3/2 on the competitive ratio, and we give a tight lower bound of 5/4 on the competitive ratio in the other case. Thus for each case we give an optimal online algorithm.
机译:移动机器人和无线传感器网络之间的合作是研究的一个行目前吸引了大量的关注。在这种情况下,我们通过由移动机器人与移动传感器的能力辅助固定无线传感器研究屏障覆盖的以下问题。假设n个传感器最初任意分布上的线段屏障。每个传感器被说成覆盖所述阻挡层的部分,其与它的感测区域相交。由于不正确的初始位置,或一些传感器的死亡,屏障没有完全由传感器覆盖。我们采用一个移动机器人来移动传感器到最终位置上的屏障,使得屏障覆盖有保证。我们寻求的算法,最大限度地减少了机器人的轨迹的长度,因为这一旦允许屏障覆盖的恢复成为可能。我们给出一个最优线性时间离线算法给出一个机器人,在阻挡的一端开始并实现屏障覆盖的恢复的最小长度的轨迹。我们也研究了两种不同的在线模式:一个在网上机器人不能预先知道屏障的长度,和其他在该线上机器人知道它提前。用于当在线机器人不知道阻挡层的长度的情况下,我们证明在竞争比结合的3/2一个紧,我们给出一个紧在另一种情况下的竞争比下限的5/4。因此,对于每一种情况下,我们给出一个最优的在线算法。

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