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, the authors propose a probabilistic semi-supervised learning approach to reduce the calibration effort and increase the tracking accuracy.