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Weighted Centroid Localization algorithm based on least square for wireless sensor networks

机译:基于最小二乘的无线传感器网络加权质心定位算法

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Localization algorithm is an important and challenging topic in today's wireless sensor networks (WSNs). In order to improve the localization accuracy, a weighted centroid localization algorithm based on least square to predict the location of any sensor in a WSNs is proposed in this paper. The proposed algorithm proposes a Least-Square-based weight model which can reasonably weigh the proportion of each anchor node in the unknown node. In the weight model, we utilize least square method to compute the weight. Then, we increase the weight of anchor nodes closer to the unknown node, introduce the parameter k into the proposed likelihood model, and we determine the optimal value of the parameter k through our experiments. Experimental results show that the proposed weighted centroid algorithm is better than WCL (Weighted Centroid Localization) and AMWCL-RSSI (anchor-optimized modified weighted centroid localization based on RSSI) in terms of the localization accuracy.
机译:定位算法是当今无线传感器网络(WSN)中一个重要且具有挑战性的主题。为了提高定位精度,提出了一种基于最小二乘的加权质心定位算法来预测传感器在无线传感器网络中的位置。该算法提出了一种基于最小二乘的加权模型,该模型可以合理地加权未知节点中每个锚节点的比例。在权重模型中,我们利用最小二乘法计算权重。然后,我们增加锚节点的权重,使其更接近未知节点,将参数k引入拟议的似然模型中,并通过实验确定参数k的最佳值。实验结果表明,所提出的加权质心算法在定位精度方面优于WCL(加权质心定位)和AMWCL-RSSI(基于RSSI的锚定优化修正加权质心定位)。

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