Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021673" target="_blank" >RIV/62690094:18450/24:50021673 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.ejmanager.com/mnstemps/204/204-1703256698.pdf?t=1725871548" target="_blank" >https://www.ejmanager.com/mnstemps/204/204-1703256698.pdf?t=1725871548</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5455/jjee.204-1703256698" target="_blank" >10.5455/jjee.204-1703256698</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Popis výsledku v původním jazyce

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time. © 2024, Tafila Technical University. All rights reserved.

Název v anglickém jazyce

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Popis výsledku anglicky

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time. © 2024, Tafila Technical University. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Jordan Journal of Electrical Engineering

ISSN

2409-9600

e-ISSN

2409-9619

Svazek periodika

10

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

JO - Jordánské hášimovské království

Počet stran výsledku

12

Strana od-do

431-442

Kód UT WoS článku

001407397600007

EID výsledku v databázi Scopus

2-s2.0-85202590104