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Best and worst-case coverage problems for arbitrary paths in wireless sensor networks

机译:无线传感器网络中任意路径的最佳和最坏情况覆盖问题

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The best-case and the worst-case coverage were proposed originally for a single source and destination pair in a sensor network. In this paper, we propose a new coverage measure of the sensor network considering arbitrary paths. Surprisingly, this new measure captures both the best-case and the worst-case coverage of the sensor network simultaneously, enabling us to evaluate the given network in a global viewpoint. Accordingly, we pose the evaluation and the deployment problems; the former is to evaluate the new coverage measure of a given sensor network, and the latter is to find an optimal placement of k additional sensor nodes to improve the coverage for a given positive integer k. We present several algorithms solving the problems that are either centralized or localized with theoretical proofs and simulation results, showing that our algorithms are efficient and easy to implement in practice while the quality of outputs is guaranteed by formal proofs. Our algorithms are based on an interesting relation between our new coverage measure and a certain quantity of a point set, called the bottleneck, which has been relatively well studied in other disciplines. In doing so, we prove that a maximal support path can always be found in the minimum spanning tree; this is another contribution of ours.
机译:最佳情况和最坏情况的覆盖范围最初是针对传感器网络中的单个源对和目标对提议的。在本文中,我们提出了一种考虑任意路径的传感器网络的新覆盖措施。令人惊讶的是,这项新措施同时捕获了传感器网络的最佳情况和最坏情况,从而使我们能够从全局的角度评估给定的网络。因此,我们提出了评估和部署问题;前者是评估给定传感器网络的新覆盖率度量,后者是找到k个其他传感器节点的最佳位置,以改善给定正整数k的覆盖率。我们通过理论证明和仿真结果提出了几种解决集中式或局部化问题的算法,这表明我们的算法高效且易于实现,而输出的质量则由形式证明来保证。我们的算法基于我们新的覆盖率度量与一定数量的点集(称为瓶颈)之间的有趣关系,该点集已在其他学科中进行了比较充分的研究。通过这样做,我们证明了最大的支持路径总能在最小生成树中找到。这是我们的另一贡献。

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