document: 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 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 .
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