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A new range-free localisation in wireless sensor networks using support vector machine

机译:支持向量机在无线传感器网络中的新无范围定位

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摘要

Location information of sensor nodes is of vital importance for most applications in wireless sensor networks (WSNs). This paper proposes a new range-free localisation algorithm using support vector machine (SVM) and polar coordinate system (PCS), LSVM-PCS. In LSVM-PCS, two sets of classes are first constructed based on sensor nodes' polar coordinates. Using the boundaries of the defined classes, the operation region of WSN field is partitioned into a finite number of polar grids. Each sensor node can be localised into one of the polar grids by executing two localisation algorithms that are developed on the basis of SVM classification. The centre of the resident polar grid is then estimated as the location of the sensor node. In addition, a two-hop mass-spring optimisation (THMSO) is also proposed to further improve the localisation accuracy of LSVM-PCS. In THMSO, both neighbourhood information and non-neighbourhood information are used to refine the sensor node location. The results obtained verify that the proposed algorithm provides a significant improvement over existing localisation methods.
机译:传感器节点的位置信息对于无线传感器网络(WSN)中的大多数应用至关重要。本文提出了一种新的基于支持向量机(SVM)和极坐标系(PCS)的无范围定位算法,即LSVM-PCS。在LSVM-PCS中,首先根据传感器节点的极坐标构造两组类。使用定义的类的边界,将WSN字段的操作区域划​​分为有限数量的极坐标网格。通过执行基于SVM分类开发的两种定位算法,可以将每个传感器节点定位到一个极坐标网格中。然后将常驻极点栅格的中心估计为传感器节点的位置。此外,还提出了两跳质量弹簧优化(THMSO),以进一步提高LSVM-PCS的定位精度。在THMSO中,邻居信息和非邻居信息都用于完善传感器节点的位置。获得的结果证明,所提出的算法与现有的定位方法相比具有明显的改进。

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