1.
Juan Carlos Avilés and Diego Hernan Peluffo-Ordóñez
Trilateration-based Indoor Location using Supervised
Learning Algorithms
Jerry Landívar , Carolina Ormaza , Víctor Asanza , Verónica Ojeda ,
Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
Facultad de Ingeniería en Electricidad y Computación, FIEC
Smart Data Analysis Systems Group
Modeling, Simulation and Data Analysis Research Program

2.
Published in:
https://ieeexplore.ieee.org/document/9920073

3.
Introduction
The coronavirus can
enter to the
organism of people
through the mucous
membranes of the
nose, mouth or
eyes.
Not all infected
people usually
show the
characteristic
symptoms of COVID
19.
The risk of
contagion is greater
in enclosed spaces.
Indoor Positioning
System based on
trilateration.
Distance monitoring
bewteen people
within an enclosed
space.

4.
Related Works
• Bluetooth Low Energy based approaches [1]
• Wi-Fi based approaches [2]
• Radio Frequency Identification based approaches [3]
• Ultra-wideband based approaches
• Visible Light Communication based approaches
[1] Guoquan Li, Enxu Geng, Zhouyang Ye, Yongjun Xu, Jinzhao Lin, and Yu Pang. Indoor positioning algorithm based on the improved rssi distance model. Sensors, 18(9):2820, 2018.
[2] Carolina Aguilar Aravena and Luciene Stamato Delazaria. Solution for indoor positioning using wifi networks. In Proceedings of the ICA, volume 2, pages NA–NA. Copernicus GmbH, 2019
[3] Eduardo Luis Gomes, Mauro Fonseca, André Eugenio Lazzaretti, Anelise Munaretto, and Carlos Guerber. Clustering and hierarchical classification for high-precision rfid indoor location
systems. IEEE Sensors Journal, 2021

5.
Methodology
System devices
Device Function
Access point 1
It’s programmed to emit a WiFi signal
Access point 2
Access point 3
Mobile device It scans for nearby WiFi networks and
updates the RSSI vector.

6.
Methodology
System components
• Access points (APs)
• Mobile device (MD)

7.
Methodology
Access point
Component Description
DC power
connector
Gets power from AC-DC adapter
(9-12V)
Buck converter Reduces input voltage to 3.3V
ESP01 Module ESP8266 based module (emitting
WiFi signal)

8.
Methodology
Mobile device
Component Description
Lithium Battery Gets power from AC-DC
adapter (9-12V)
Buck converter Reduces battery voltage from
3.7V to 3.3V
RSSI receptor ESP01 scanning the RSSI of
each AP network
Local Server Host ESP01 sending data to
computer

9.
Methodology
Data acquisition

10.
Results
RMSE by positions
Algorithm X position Y position
SVM - Coarse Gaussian 105.85 46.781
Ensemble - Boosted Trees 94,493 43.23
Ensemble - Bagged Trees 77.991 34.684
GPR - Squared Exponential 76.756 32.752
GPR - Matern 5/2 73.77 31.445
GPR - Exponential 70.454 30.212
GPR - Rational Quadratic 100.75 30.169
NN - Narrow 103.63 45.149
NN - Medium 98.19 41.587
NN - Wide 99.32 36.766
NN - Bilayered 95.533 41.739
NN - Trilayered 98.52 40.966
RMSE by positions
Algorithm X position Y position
LR - Linear 107.57 50.962
LR - Interactions Linear 107.1 49.793
LR - Robust Linear 107.58 51.004
LR - Stepwise Linear 107.08 49.793
Tree - Fine Tree 75.055 32.39
Tree - Medium Tree 77.284 33.967
Tree - Coarse Tree 82.509 36.798
SVM - Linear 108.04 51.323
SVM - Quadratic 108.79 48.441
SVM - Cubic 105.63 47.26
SVM - Fine Gaussian 78.583 33.299
SVM - Medium Gaussian 94.489 42.45

11.
Results
70.322
30.1508
0
10
20
30
40
50
60
70
80
Error
X
and
Y
position
RMSE by position
X position Y position

12.
Discussion and Conclusions
• The algorithm selected for the position prediction on the ‘x’ axis (400 [cm] length) is
GPR-Exponential with a validation error of 70.322 [cm].
• The algorithm selected for the position prediction on the ‘y’ axis (200 [cm] length) with a
validation error of 30.1508 [cm].
• The model is not over-trained and is able to generalize with new data, obtaining even a
lower error during validation.
• As future work, we propose to perform measurements in larger areas, quantifying how
the number of squares used affects the prediction accuracy. Also, to increase the number
of APs to analyze how it affects the accuracy of the prediction on each axis.

13.
For more information

14.
For more information
Víctor Asanza
Mail: vasanza@espol.edu.ec
Facultad de Ingeniería en Electricidad y Computación, FIEC
Escuela Superior Politécnica del Litoral, ESPOL
Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863
090150 Guayaquil, Ecuador

15.
Thank you!