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Maximum Coverage and Flexible Convergence based 3-D Localization Approach for Wireless Sensor Networks

机译:无线传感器网络的最大覆盖和基于灵活收敛的3D定位方法

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Objectives: Sensor network consists of sensor nodes, which independently organize the network and used in various applications like medical services, fire detection, military operations etc. This paper presents the 3-Dimensional GA based DV-Hop localization algorithm, which identifies the position of hidden nodes and distance from anchor node to the hidden node. The proposed model utilizes the swarm intelligence to localize the network. Methods: The proposed model simulation is entirely based upon the topology design with the randomly deployed network topology. The variable number of nodes has been obtained and the random positions are calculated to define the x and y coordinates of the nodes in the given topology. The proposed model has been tested with the different transmission ranges in order to understand the performance of the proposed model. Findings: The planned technique for localization empowers the wireless network to correctly analyze the solutions for the specific problems associated with the localization errors or positioning coverage. The performance parameters which compared with existing technique are average error and average localization error. Novelty of the Study: The experimental observations obtained from the proposed model shows the significant difference than the existing model, which shows the enhanced performance of planned model.
机译:目标:传感器网络由传感器节点组成,这些传感器节点独立地组织网络,并用于医疗服务,火灾探测,军事行动等各种应用中。本文提出了一种基于3维GA的DV-Hop定位算法,该算法可确定传感器的位置。隐藏节点以及锚点到隐藏节点的距离。提出的模型利用群体智能来定位网络。方法:所提出的模型仿真完全基于具有随机部署的网络拓扑的拓扑设计。已经获得了可变数量的节点,并计算了随机位置以定义给定拓扑中节点的x和y坐标。为了理解所提出模型的性能,已对所提出的模型进行了不同传输范围的测试。发现:计划中的定位技术使无线网络能够针对与定位错误或定位范围相关的特定问题正确分析解决方案。与现有技术相比,性能参数为平均误差和平均定位误差。研究的新颖性:从提出的模型获得的实验观察结果表明,与现有模型相比存在显着差异,这表明计划模型的性能得到了增强。

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