Improved indoor localization using k-medoids and k-nearest neighbour algorithms with context similarity coefficient-based fingerprint similarity metric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021943" target="_blank" >RIV/62690094:18450/24:50021943 - isvavai.cz</a>

Výsledek na webu

<a href="https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/tje2.70023" target="_blank" >https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/tje2.70023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1049/tje2.70023" target="_blank" >10.1049/tje2.70023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improved indoor localization using k-medoids and k-nearest neighbour algorithms with context similarity coefficient-based fingerprint similarity metric

Popis výsledku v původním jazyce

Fingerprint database clustering and localization using k-medoids and k-nearest neighbour (k-NN) algorithms respectively typically use distance-based fingerprint similarity metrics, with their performances dependent on the type of distance metric used. This paper proposes employing a pattern-based metric, the context similarity coefficient (CSC), for both algorithms instead of traditional distance-based metrics. The CSC accounts for fingerprint behaviour and the non-linear relationships among fingerprints during the similarity measurement. The performance of both algorithms with the CSC as the similarity metric is evaluated on four publicly available fingerprint databases, using position root mean square error (RMSE) and silhouette score as performance metrics. These results are compared to those of the same algorithms using five distance-based metrics: Euclidean, square Euclidean, Manhattan, cosine, and Chebyshev distances. The k-medoids algorithm with CSC shows moderate clustering performance compared to the five distance-based metrics considered. However, when combined with the k-NN algorithm also using CSC, it achieves the highest localization accuracy, with at least a 29% improvement in position RMSE across all four databases. The results indicate that while k-medoids with CSC may not create well-separated clusters, combining it with the k-NN algorithm with CSC as its similarity metric significantly enhances localization accuracy compared to distance-based metrics.

Název v anglickém jazyce

Improved indoor localization using k-medoids and k-nearest neighbour algorithms with context similarity coefficient-based fingerprint similarity metric

Popis výsledku anglicky

Fingerprint database clustering and localization using k-medoids and k-nearest neighbour (k-NN) algorithms respectively typically use distance-based fingerprint similarity metrics, with their performances dependent on the type of distance metric used. This paper proposes employing a pattern-based metric, the context similarity coefficient (CSC), for both algorithms instead of traditional distance-based metrics. The CSC accounts for fingerprint behaviour and the non-linear relationships among fingerprints during the similarity measurement. The performance of both algorithms with the CSC as the similarity metric is evaluated on four publicly available fingerprint databases, using position root mean square error (RMSE) and silhouette score as performance metrics. These results are compared to those of the same algorithms using five distance-based metrics: Euclidean, square Euclidean, Manhattan, cosine, and Chebyshev distances. The k-medoids algorithm with CSC shows moderate clustering performance compared to the five distance-based metrics considered. However, when combined with the k-NN algorithm also using CSC, it achieves the highest localization accuracy, with at least a 29% improvement in position RMSE across all four databases. The results indicate that while k-medoids with CSC may not create well-separated clusters, combining it with the k-NN algorithm with CSC as its similarity metric significantly enhances localization accuracy compared to distance-based metrics.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF ENGINEERING-JOE

ISSN

2051-3305

e-ISSN

2051-3305

Svazek periodika

2024

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

"Article Number: e70023"

Kód UT WoS článku

001368768900001

EID výsledku v databázi Scopus

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