Machine Learning Based Localization in Large-Scale Wireless Sensor Networks.
Machine Learning Based Localization in Large-Scale Wireless Sensor Networks.
复制标题
大规模无线传感器网络中基于机器学习的定位。
DOI：
10.3390/s18124179
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发表时间：
2018-11-28
期刊：
影响因子：
--
通讯作者：
Bhatti G
中科院分区：
文献类型：
--
作者：
Bhatti G
过去几年无线传感器网络的迅速普及给研究人员带来了一些严峻的技术挑战。多跳无线传感器网络 (WSN) 的主要功能是收集传感器数据并将其转发到目标节点。然而，对于许多应用来说，了解传感器节点的位置对于有意义地解释传感器数据至关重要。定位是指估计无线传感器网络中传感器节点位置的过程。在无法手动定位这些节点的大型无线传感器网络中需要自定位。传统方法通过使用三角测量迭代地定位这些节点。然而，无线信号固有的不稳定性会在目标节点的估计位置中引入误差，无论误差多么微小。这导致嵌入误差迅速传播和放大。用于大型无线传感器网络的基于机器学习的定位算法不以迭代方式运行。在本文中，我们研究了其中一些算法的适用性，同时探索不同的权衡。具体来说，我们首先制定了一种定义多个特征向量的新方法，用于将定位问题映射到不同的机器学习模型上。与大多数报告工作中所做的那样将定位视为分类问题相反，我们将其视为回归问题。我们研究了不同的网络参数（例如网络规模、锚点数量、传输信号功率和无线信道质量）对这些模型的定位精度的影响。我们还研究了在网格中部署锚节点而不是在部署区域中随机放置这些节点的影响。我们的结果在使用多元回归模型和带有径向基函数 (RBF) 核的支持向量机 (SVM) 回归模型时揭示了有趣的见解。
The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.
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影响因子：
9.3
作者：
Sivrikaya, F;Yener, B
通讯作者：
Yener, B
影响因子：
1.6
作者：
Rauchenstein, Lynn T.;Vishnu, Abhinav;Deng, Zhiqun Daniel
通讯作者：
Deng, Zhiqun Daniel
DOI：
10.1109/tpds.2007.70800
发表时间：
2008-07-01
影响因子：
5.3
作者：
Tran, Duc A.;Nguyen, Thinh
通讯作者：
Nguyen, Thinh
DOI：
10.1109/89.966097
发表时间：
2001-11-01
期刊：
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING
影响因子：
--
作者：
Huang, YT;Benesty, J;Mersereau, RM
通讯作者：
Mersereau, RM
影响因子：
2.8
作者：
Khatab, Zahra Ezzati;Hajihoseini, Amirhosein;Ghorashi, Seyed Ali
通讯作者：
Ghorashi, Seyed Ali