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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3423
Title: Learning-based localization in wireless and sensor networks
Authors: Pan, Junfeng
Issue Date: 2007
Abstract: Accurately tracking mobile devices in wireless and sensor networks using received-signal-strength (RSS) values is a useful task in robotics and activity recognition. It is also a difficult task since radio signals usually attenuate in a highly nonlinear and uncertain way in a complex environment where client devices may be moving. Many existing RSS localization systems suffer from the following problems: first, many of them are inaccurate. Second, to increase their accuracy, many require costly manual calibration. Third, many of them cannot cope with changing data as users move in a dynamic environment. In this thesis, we will describe our learning-based solution using kernels, manifolds and graph Laplacian for solving the these problems. We will demonstrate the effectiveness of our algorithms for tracking static and mobile devices in complex indoor environments using wireless local area network, wireless sensor networks and radio frequency identification networks with much less calibration effort.
Description: Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007
xiii, 124 leaves : ill. ; 30 cm
HKUST Call Number: Thesis CSED 2007 Pan
URI: http://hdl.handle.net/1783.1/3423
Appears in Collections:CSE Doctoral Theses

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