Domain-constrained semi-supervised mining of tracking models in sensor networks
Domain-constrained semi-supervised mining of tracking models in sensor networks
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DOI：
10.1145/1281192.1281304
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发表时间：
2007-08
期刊：
影响因子：
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通讯作者：
Rong Pan;Junhui Zhao;V. Zheng;Jeffrey Junfeng Pan;Dou Shen;Sinno Jialin Pan;Qiang Yang
中科院分区：
文献类型：
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作者：
Rong Pan;Junhui Zhao;V. Zheng;Jeffrey Junfeng Pan;Dou Shen;Sinno Jialin Pan;Qiang Yang
移动物体的精确定位是传感器网络的主要研究问题和重要的数据挖掘应用。具体地，定位问题是在给定客户端设备处从多个信标传感器或接入点接收到的无线电信号强度值的情况下准确地确定客户端设备的位置。传统的数据挖掘和机器学习方法可以用来解决这个问题。然而，所有这些都需要大量标记的训练数据，这可能非常昂贵。在本文中，我们提出了一种概率半监督学习方法来减少校准工作并提高跟踪精度。我们的方法基于半监督条件随机场，可以有效地从具有大量未标记数据的一小组训练数据中增强学习模型。为了使我们的方法更加高效，我们利用了广义 EM 算法和域约束。我们使用 Crossbow MICA2 传感器在真实传感器网络中进行大量实验来验证我们的方法。结果证明了该方法与其他最先进的对象跟踪算法相比的优势。
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.