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Quadrant-Based Weighted Centroid Algorithm for Localization in Underground Mines

机译:基于象限的加权质心算法在地下矿山的定位

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Location sensing in wireless sensor networks (WSNs) is a critical problem when it comes to rescue operation in underground mines. Most of the existing research on node localization uses traditional centroid algorithm-based approach. However, such approaches have higher localization error, which leads to inaccurate node precision. This paper proposes a novel quadrant-based solution on weighted centroid algorithm that uses received signal strength indicator for range calculation and distance improvement by incorporating alternating path loss factor according to the mine environment. It also makes use of four beacon nodes instead of traditional three with weights applied to reflect the impact of each node for the centroid position. The weight factor applied is the inverse of the distance estimated. Simulation results show higher localization accuracy and precision as compared to traditional weighted centroid algorithms.
机译:无线传感器网络(WSN)中的位置感应是在地下矿山救援运行时的关键问题。大多数现有节点本地化研究采用传统的基于质心算法的方法。但是,这种方法具有更高的本地化误差,这导致不准确的节点精度。本文提出了一种关于加权质心算法的基于象限的解决方案,通过结合根据矿井环境的交替路径损耗因子来使用接收的信号强度指示器进行范围计算和距离改进。它还利用了四个信标节点而不是传统三个,其重量应用于反映每个节点对质心位置的影响。施加的重量因子是估计距离的倒数。与传统的加权质心算法相比,仿真结果表明了更高的定位精度和精度。

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