Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Jordan Journal of Electrical Engineering
ISSN
2409-9600
e-ISSN
2409-9619
Volume of the periodical
10
Issue of the periodical within the volume
3
Country of publishing house
JO - JORDAN
Number of pages
12
Pages from-to
431-442
UT code for WoS article
001407397600007
EID of the result in the Scopus database
2-s2.0-85202590104