Detail publikace

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

POLÁK, L. ROZUM, S. SLANINA, M. BRAVENEC, T. FRÝZA, T. PIKRAKIS, A.

Originální název

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%

Klíčová slova

Bluetooth; fingerprinting; indoor navigation; machine learning

Autoři

POLÁK, L.; ROZUM, S.; SLANINA, M.; BRAVENEC, T.; FRÝZA, T.; PIKRAKIS, A.

Vydáno

5. 7. 2021

Nakladatel

MDPI

Místo

Basel (Switzerland)

ISSN

1424-8220

Periodikum

SENSORS

Ročník

21

Číslo

13

Stát

Švýcarská konfederace

Strany od

1

Strany do

25

Strany počet

25

URL

https://www.mdpi.com/1424-8220/21/13/4605

Plný text v Digitální knihovně

http://hdl.handle.net/11012/200943

BibTex

@article{BUT172070,
  author="Ladislav {Polák} and Stanislav {Rozum} and Martin {Slanina} and Tomáš {Bravenec} and Tomáš {Frýza} and Aggelos {Pikrakis}",
  title="Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning",
  journal="SENSORS",
  year="2021",
  volume="21",
  number="13",
  pages="1--25",
  doi="10.3390/s21134605",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/21/13/4605"
}