Publication detail

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

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

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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%

Keywords

Bluetooth; fingerprinting; indoor navigation; machine learning

Authors

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

Released

5. 7. 2021

Publisher

MDPI

Location

Basel (Switzerland)

ISBN

1424-8220

Periodical

SENSORS

Year of study

21

Number

13

State

Swiss Confederation

Pages from

1

Pages to

25

Pages count

25

URL

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

Full text in the Digital Library

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"
}