Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Using proximity and quantized RSS for sensor localization in wireless networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
IEEE Transactions on Knowledge and Data Engineering
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
A Novel Virtual Anchor Node-Based Localization Algorithm for Wireless Sensor Networks
ICN '07 Proceedings of the Sixth International Conference on Networking
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.