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LEARNING-BASED LOCALIZATION IN WIRELESS AND SENSOR NETWORKS
The Hong Kong University of Science and Technology
Department of Computer Science and Engineering
PhD Thesis Defence
Title: "LEARNING-BASED LOCALIZATION IN WIRELESS AND SENSOR NETWORKS"
By
Mr. Junfeng Pan
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.
Date: Wednesday, 28 November 2007
Time: 10:00a.m.-12:00p.m.
Venue: Room 3304
Lifts 17-18
Chairman: Prof. Yan XU (ISMT)
Committee Members: Prof. Qiang YANG (Supervisor)
Prof. James KWOK
Prof. Lionel NI
Prof. Andrew LIM, (IELM)
Prof. Jeffrey Xu YU (Sys. Engg & Engg. Mgmt, CUHK)
**** ALL are Welcome ****
