Adaptive Temporal Radio Maps for Indoor Location Estimation

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	Seminar on Data Mining for Pervasive Computing
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Speaker:	Jie YIN and Xiaoyong CHAI
		Department of Computer Science
		Hong Kong University of Science & Technology

Date:		Monday, 28 February 2005

Time:		4:00 pm - 5:00 pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lifts nos. 25/26)
		The Hong Kong University of Science & Technology

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This talk consists of two conference presentations to be given at the 3rd
Annual IEEE International Conference on Pervasive Computing and
Communications (IEEE Percom 2005) in Hawaii USA, March 2005.  It should
serve as a good overview of our research activities in data mining for
pervasive computing.
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First Presentation (30 min):
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Title:  	"Adaptive Temporal Radio Maps for Indoor Location
		 Estimation"

Speaker: 	 Jie YIN, (http://cse.hkust.edu.hk/~yinjie)

Abstract:

We present a novel method to adapt the temporal radio maps for indoor
location estimation by offsetting the varying environmental factors using
data mining techniques and reference points. Environmental variations,
which cause the signals to change from time to time even at the same
location, present a challenging task for indoor location estimation in the
IEEE 802.11b infrastructure. In such a dynamic environment, the radio maps
obtained in one time period may not be applicable in other time periods.
To solve this problem, we apply a regression analysis to learn the
temporal predictive relationship between the signal-strength values
received by sparsely located reference points and that received by the
mobile device. This temporal prediction model can then be used for online
localization based on the newly observed signal strength values at the
client side and the reference points. We show that this technique can
effectively accommodate the variations of signal-strength values over
different time periods without the need to rebuild the radio maps
repeatedly.  This is joint work with Professors Qiang Yang and Lionel Ni.


Biography:

Jie Yin is currently a Ph. D. student in the Department of Computer
Science, HKUST. Her research interests include artificial intelligence and
pervasive computing. Currently she is working on learning and recognizing
human behaviors from sensory data in pervasive environments.


Second Presentation (30 min):
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Title: 		"Reducing the Calibration Effort for Location Estimation
		 Using Unlabeled Samples"

Speaker: 	 Xiaoyong CHAI (http://cse.hkust.edu.hk/~carnamel)

Abstract:

WLAN location estimation based on 802.11 signal strength is becoming
increasingly prevalent in today's pervasive computing applications. As an
alternative to the well established deterministic approaches,
probabilistic location determination techniques show good performance and
become more and more popular. However, in order for these techniques to
achieve a high level of accuracy, adequate training samples should be
collected offline for calibration. As a result, a great amount of manual
effort is incurred. In this work, we aim to solve the problem by reducing
both the sampling time and the number of locations sampled in constructing
the radio map. A learning algorithm is proposed to build location
estimation systems based on a small fraction of the calibration data
traditional techniques require and a collection of user traces that can be
cheaply obtained. Our experiments show that unlabeled user traces can be
used to compensate the effects of reducing calibration effort and even
improve the system performance.  Consequently, manual effort can be
significantly reduced while a high level of accuracy is still achieved.
This is joint work with Professor Qiang Yang.


Biography:

Xiaoyong Chai is currently an MPhil student at the Department of Computer
Science, the Hong Kong University of Science and Technology. He received
his bachelor degree in Computer Science from Fudan University, Shanghai,
China in 2002. His research interests include location-aware computing and
human behavior recognition.