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首页> 外文会议>International Conference on Measuring Technology and Mechatronics Automation >Three-Dimensional Mobile Node Localization Method of WSNs Based on Improved LSSVR Algorithm
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Three-Dimensional Mobile Node Localization Method of WSNs Based on Improved LSSVR Algorithm

机译：基于改进LSSVR算法的无线传感器网络三维移动节点定位方法

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摘要

The traditional least squares support vector regression (LSSVR) node localization algorithm for wireless sensor networks (WSNs) uses the average hop distance to calculate the actual distance, which may result larger localization error in the obstacle conditions. An improved LSSVR WSNs three-dimensional mobile node localization method in an obstacle conditions was proposed in this paper. The average per hop distance of four anchor nodes closest was used to replace the average distance per hop of traditional LSSVR algorithm in the proposed method, and the new average per hop distance was used to calculate the measurement distance of each unknown node to anchor nodes. The LSSVR localization model was built through sampling of the grid and constructing the training sets. According to mean square deviation of predicted location of virtual nodes and their actual location, fitness function was constructed, and LSSVR kernel function and regularization parameters were optimized by the PSO algorithm. The simulation results show that, compared with the conventional LSSVR localization algorithm, the proposed localization algorithm has a higher localization accuracy, smaller localization errors and lower localization cost in the obstacle conditions.
机译：无线传感器网络（WSN）的传统最小二乘支持向量回归（LSSVR）节点定位算法使用平均跳距来计算实际距离，这可能会在障碍情况下导致较大的定位误差。提出了一种改进的LSSVR WSN在障碍条件下的三维移动节点定位方法。提出的方法用四个最接近的锚节点的平均每跳距离来代替传统的LSSVR算法的平均每跳距离，并使用新的平均每跳距离来计算每个未知节点到锚节点的测量距离。通过对网格进行采样并构建训练集来构建LSSVR本地化模型。根据虚拟节点的预测位置及其实际位置的均方差，构造适应度函数，并通过PSO算法对LSSVR内核函数和正则化参数进行优化。仿真结果表明，与传统的LSSVR定位算法相比，在障碍情况下，该定位算法具有较高的定位精度，较小的定位误差和较低的定位成本。

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