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Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation
July 2008 (vol. 7 no. 7)
pp. 869-883
ASCII Text
x 
 
Jie Yin, Qiang Yang, Lionel M. Ni, "Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation," IEEE Transactions on Mobile Computing, vol. 7, no. 7, pp. 869-883, July, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TMC.2007.70764,
author = {Jie Yin and Qiang Yang and Lionel M. Ni},
title = {Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation},
journal ={IEEE Transactions on Mobile Computing},
volume = {7},
number = {7},
issn = {1536-1233},
year = {2008},
pages = {869-883},
doi = {http://doi.ieeecomputersociety.org/10.1109/TMC.2007.70764},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Mobile Computing
TI - Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation
IS - 7
SN - 1536-1233
SP869
EP883
EPD - 869-883
A1 - Jie Yin,
A1 - Qiang Yang,
A1 - Lionel M. Ni,
PY - 2008
KW - Location Estimation
KW - Signal Strength
KW - Reference Points
KW - Temporal Radio Maps
VL - 7
JA - IEEE Transactions on Mobile Computing
ER -
 
In wireless networks, a client's locations can be estimated using the signals received from various signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when the signal-strength values change with time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal- strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the variations of signal strength over different time periods without the need to rebuild the radio maps repeatedly. We demonstrate the effectiveness of our proposed technique on realistic data sets collected from an 802.11b wireless network and a RFID-based network.

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Index Terms:
Location Estimation, Signal Strength, Reference Points, Temporal Radio Maps
Citation:
Jie Yin, Qiang Yang, Lionel M. Ni, "Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation," IEEE Transactions on Mobile Computing, vol. 7, no. 7, pp. 869-883, July 2008, doi:10.1109/TMC.2007.70764
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