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QGAC: Quantum Genetic Based-Clustering Algorithm for WSNs

机译:QGAC:用于WSN的量子遗传聚类算法

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In this paper, we present a novel approach for clustering based on quantum genetic computing and complex systems. The main idea is the use of Wireless Sensor Networks (WSNs) as complex system, and Quantum Computing algorithms (QC) as research strategy. WSNs are a set of sensors that operate in parallel and interact with their neighbors using single hop or multi-hops communication. The problem with WSNs is to find, within a large set of sensors randomly deployed, the subset of best clusters and their Cluster Heads (CHs) and ensure their balanced distribution in network. To cope with this NP-hard problem, we propose a new Quantum Genetic Clustering Algorithm (QGCA) which is based on Quantum Genetic Algorithm (QGA) for CHs selection to reduce energy consumption and extend the network lifetime. A comparison is made between classical routing protocol LEACH and the proposed QGCA. Experiments show that the efficiency of QGCA is significantly better and clearly indicate that the proposed approach outperforms random CHs selection and leads to significant increase in network lifetime.
机译:在本文中,我们提出了一种基于量子遗传计算和复杂系统的新型聚类方法。主要思想是使用无线传感器网络(WSN)作为复杂系统,并使用量子计算算法(QC)作为研究策略。 WSN是一组传感器,它们并行运行并使用单跳或多跳通信与其邻居交互。 WSN的问题在于在大量随机部署的传感器中找到最佳集群的子集及其集群头(CH),并确保它们在网络中的均衡分布。为了解决这个NP难题,我们提出了一种基于量子遗传算法(QGA)的量子遗传聚类算法(QGCA),用于CH的选择,以减少能耗并延长网络寿命。在经典路由协议LEACH和拟议的QGCA之间进行了比较。实验表明,QGCA的效率明显更高,并且清楚地表明,所提出的方法优于随机CH的选择,并显着提高了网络寿命。

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