Domain-constrained semi-supervised mining of tracking models in sensor networks

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 extens...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Rong Pan, Junhui Zhao, Vincent Wenchen Zheng, Jeffrey Junfeng Pan, Dou Shen, Sinno Jialin Pan, Qiang Yang
• Keywords: Algorithms Keywords Localization, Conventional Data Mining, Data Mining, KDD 2007, Probabilistic Semi-supervised Learning