Enhancing DBSCAN Clustering for Fingerprint-Based Localization With a Context Similarity Coefficient-Based Similarity Measure Metric
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021674" target="_blank" >RIV/62690094:18450/24:50021674 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10643127" target="_blank" >https://ieeexplore.ieee.org/document/10643127</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3446674" target="_blank" >10.1109/ACCESS.2024.3446674</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing DBSCAN Clustering for Fingerprint-Based Localization With a Context Similarity Coefficient-Based Similarity Measure Metric
Original language description
In fingerprint-based localization systems, clustering fingerprint databases is a proposed technique for improving localization accuracy while reducing localization time. Among various clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) stands out for its robustness to outliers and ability to accommodate fingerprint databases of various shapes. However, the clustering performance of the DBSCAN algorithm is heavily influenced by the type of similarity measure metric used, with most researchers using distance-based metrics. This paper aims to enhance DBSCAN clustering by using a pattern-based metric known as the context similarity coefficient (CSC) instead of distance-based metrics. The CSC metric examines received signal strength (RSS) measurement patterns that form fingerprint vectors and assesses both linear and non-linear relationships between these vectors to determine similarity. Four publicly available fingerprint databases were used to evaluate the clustering performance with silhouette scores as a performance metric. The performance of the DBSCAN algorithm with the CSC metric is determined and compared to Euclidean and Manhattan distances as similarity measure metrics. Simulation results indicate that achieving good clustering performance with the DBSCAN algorithm requires generating three or fewer clusters. The proposed CSC metric demonstrated the best clustering performance in two of four fingerprint databases and the second-best in another. However, computational complexity comparisons reveal that the CSC metric is highly computationally intensive and is suggested to be used on small to medium-sized fingerprint databases generated using an odd number of wireless APs deployed in a non-uniform or non-grid-like distribution. © 2013 IEEE.
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
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
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
August
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
117298-117307
UT code for WoS article
001303387700001
EID of the result in the Scopus database
2-s2.0-85201747838