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An optimal algorithm for solving partial target coverage problem in wireless sensor networks

机译:解决无线传感器网络中部分目标覆盖问题的最佳算法

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This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well-known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights.
机译:本文研究了新型覆盖模型下无线传感器网络的部分目标覆盖问题。在以前的文献中,关于目标覆盖率问题的最常用方法是将连续时间划分为不同长度的离散时隙,每个时隙由一部分传感器组成,同时将所有其他传感器设置为睡眠状态以节省能量。然而,该方法具有诸如高计算复杂度和无性能限制的缺点。我们证明了部分目标覆盖问题可以在多项式时间内得到最佳解决。首先,我们建立了一个线性规划公式,该公式考虑了传感器花费在覆盖目标上的总时间,以便获得生命周期的上限。基于先前公式中得出的信息,我们开发了一种传感器分配算法,以寻求满足寿命上限的最优计划。提供了最优性的正式证明。我们将提出的算法与著名的列生成算法进行了比较,结果表明,提出的算法在计算时间上显着提高了性能。进行实验以研究不同网络参数对网络寿命的影响,其结果使我们得出了一些有趣的见解。

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