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Advisor(s)
Abstract(s)
This paper presents a novel approach to WLAN
propagation models for use in indoor localization. The major
goal of this work is to eliminate the need for in situ data
collection to generate the Fingerprinting map, instead, it
is generated by using analytical propagation models such
as: COST Multi-Wall, COST 231 average wall and Motley-
Keenan. As Location Estimation Algorithms kNN (K-Nearest
Neighbour) and WkNN (Weighted K-Nearest Neighbour) were
used to determine the accuracy of the proposed technique.
This work is based on analytical and measurement tools to
determine which path loss propagation models are better
for location estimation applications, based on Receive Signal
Strength Indicator (RSSI).This study presents different proposals
for choosing the most appropriate values for the models
parameters, like obstacles attenuation and coefficients. Some
adjustments to these models, particularly to Motley-Keenan,
considering the thickness of walls, are proposed. The best found
solution is based on the adjusted Motley-Keenan and COST
models that allows to obtain the propagation loss estimation
for several environments.Results obtained from two testing
scenarios showed the reliability of the adjustments, providing
smaller errors in the measured values values in comparison
with the predicted values.
Description
Keywords
LBS Location estimation algorithms Fingerprinting Motley keenan COST
Citation
Publisher
International Association of Engineers