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arXiv 1610.04494cs.NIcs.AI
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基于神经网络的无线传感器网络节点定位

Localization for Wireless Sensor Networks: A Neural Network Approach

Fiji National University(斐济国立大学);University of the South Pacific(南太平洋大学)

Shiu Kumar, Ronesh Sharma, Edwin Vans

摘要:

随着无线传感器网络向工业领域的渗透,出现了许多研究机会。一个这样的基本和具有挑战性的应用是节点定位。本文采用了一种基于前向神经网络的方法。使用锚节点信标的接收信号强度指示符(RSSI)值。锚节点的数量和它们的配置对定位系统的精度有影响,这也是本文所讨论的。评估五种不同的训练算法,以找到最佳结果的训练算法。利用Matlab对多层感知器(MLP)神经网络模型进行训练。为了评估所提出的方法的性能在实时的,然后在Arduino微控制器上实现所获得的模型。在4个锚节点的情况下,使用12-12-2神经网络结构实现了0.2953 m的平均2D定位误差。所提出的方法也可以在任何其他嵌入式微控制器系统上实现。

英文摘要:

As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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