Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1281192.1281304 | KDD |
Keywords | Field | DocType |
domain-constrained semi-supervised mining,crossbow mica2 sensor,training data,major research problem,localization problem,abundant unlabeled data,client device,important data mining application,accurate localization,sensor network,multiple beacon sensor,conventional data mining,sensor networks,random field,localization,calibration,em,data mining,tracking,semi supervised learning,object tracking,em algorithm,machine learning | Conditional random field,Data mining,Semi-supervised learning,Computer science,Expectation–maximization algorithm,Exploit,Artificial intelligence,Probabilistic logic,Small set,Wireless sensor network,Machine learning,Calibration | Conference |
Citations | PageRank | References |
4 | 0.43 | 8 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Pan | 1 | 2630 | 185.22 |
| Junhui Zhao | 2 | 52 | 9.03 |
| Vincent W. Zheng | 3 | 1273 | 62.14 |
| Jeffrey Junfeng Pan | 4 | 469 | 24.73 |
| Dou Shen | 5 | 1224 | 59.46 |
| Sinno Jialin Pan | 6 | 3128 | 122.59 |
| Qiang Yang | 7 | 17039 | 875.69 |