大规模无线传感器网络节点定位的分布式梯度下降方法

Mou Ma;Shasha Xu;Junzheng Jiang
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引用次数: 0

摘要

利用图拓扑分解和梯度下降方法,提出了一种分布式迭代方法来解决大规模无线传感器网络的节点(传感器)定位问题。首先,将表示WSN的无向图划分为几个重叠的子图。基于分解子图,定位问题被分解为一系列子问题,每个子问题都存在于一个子图上。迭代过程在子图上进行,每次迭代由两个算子组成。第一个算子是使用计算成本较低的梯度下降法来求解每个子图中的子问题,第二个算子是融合并平均相邻子图重叠区域中节点的局部位置。为了丰富定位的可用信息,将定位精度高的目标节点的位置用作后续迭代的(伪)锚节点。由于算子是在小尺寸的子图上完成的,因此所提出的分布式迭代方法具有较低的计算成本,适用于大规模的无线传感器网络。数值结果证明了该定位方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Distributed Gradient Descent Method for Node Localization on Large-Scale Wireless Sensor Network
A distributed iterative method is proposed to solve the problem of node (sensor) localization for large-scale wireless sensor network (WSN), by leveraging the graph topology decomposition and gradient descent method. First, the undirected graph representing the WSN is divided into several overlapping subgraphs. Based on the decomposition subgraphs, the localization problem is splitting into a series of subproblems each of which resides on one subgraph. The iterative procedure is proceeded on the subgraphs and each iteration consists of two operators. The first operator is solving the subproblem in every subgraph by using the gradient descent method which possesses light computational cost, and the second operator is to fuse and average the local positions of nodes in the overlapping region of adjacent subgraphs. In order to enrich the available information of localization, the positions of the target nodes with high localization accuracy are used as the (pseudo) anchor nodes for the subsequent iteration. Owing to that the operators are accomplished on subgraphs with small sizes, the proposed distributed iterative method possesses low computational cost, making it suitable for large-scale WSN. Numerical results are included to demonstrate the effectiveness of the proposed localization method.
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