首页> 外文会议>Proceedings of the 34th IASTED International Conference on Modelling, Identification and Control >COVER SET FORMATION FOR TARGET COVERAGE USING GENETIC ALGORITHM IN DIRECTIONAL SENSOR NETWORKS
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COVER SET FORMATION FOR TARGET COVERAGE USING GENETIC ALGORITHM IN DIRECTIONAL SENSOR NETWORKS

机译:方向传感器网络中基于遗传算法的目标覆盖率覆盖集形成

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Maximizing the network lifetime is one important factor in covering a set of targets in directional sensor networks (DSNs). The targets may take time to be located, and a good tracking management system is necessary to locate them. Target coverage problem arises due to limitation in the sensing angle and battery power of directional sensors. The problem becomes more challenging when the targets have different coverage quality requirements. In the present study, this problem is referred to as Priority-based Target Coverage (PTC) that has been proven to be an NP-complete problem. As sensors are often deployed densely, a promising solution to this problem is the use of scheduling technique through which the sensors are partitioned into several cover sets, then the cover sets are activated successively. In this paper, we propose a genetic-based scheduling algorithm to solve the problem. In order to examine the impact of different factors on the size of the resulting subset, three different experiments were performed to test the effectiveness of our algorithm. The results demonstrated that the proposed algorithm was able to contribute to solving the problem significantly.
机译:最大化网络寿命是覆盖定向传感器网络(DSN)中的一组目标的重要因素。定位目标可能会花费一些时间,并且需要一个良好的跟踪管理系统来定位它们。由于方向传感器的感应角度和电池电量的限制,出现了目标覆盖问题。当目标具有不同的覆盖质量要求时,问题将变得更具挑战性。在本研究中,此问题被称为基于优先级的目标覆盖率(PTC),已被证明是NP完全问题。由于传感器通常被密集部署,因此解决该问题的一种有希望的解决方案是使用调度技术,通过该技术将传感器划分为多个封面集,然后依次激活封面集。在本文中,我们提出了一种基于遗传的调度算法来解决该问题。为了检查不同因素对结果子集大小的影响,进行了三个不同的实验来测试我们算法的有效性。结果表明,该算法能够为解决该问题做出重要贡献。

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