Improved indoor localization using k-medoids and k-nearest neighbour algorithms with context similarity coefficient-based fingerprint similarity metric
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improved indoor localization using k-medoids and k-nearest neighbour algorithms with context similarity coefficient-based fingerprint similarity metric
Original language description
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.
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
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
JOURNAL OF ENGINEERING-JOE
ISSN
2051-3305
e-ISSN
2051-3305
Volume of the periodical
2024
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
"Article Number: e70023"
UT code for WoS article
001368768900001
EID of the result in the Scopus database
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