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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141244" target="_blank" >RIV/00216305:26220/21:PU141244 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/21/13/4605" target="_blank" >https://www.mdpi.com/1424-8220/21/13/4605</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21134605" target="_blank" >10.3390/s21134605</a>

Result language

angličtina

Original language name

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

Original language description

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%

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20202 - Communication engineering and systems

Project

<a href="/en/project/LTC18021" target="_blank" >LTC18021: Future Wireless Radio Communication Networks in Real Scenarios (FEWERCON)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SENSORS

ISSN

1424-8220

e-ISSN

1424-3210

Volume of the periodical

21

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

1-25

UT code for WoS article

000671202400001

EID of the result in the Scopus database

2-s2.0-85111858852