Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021482" target="_blank" >RIV/62690094:18450/24:50021482 - isvavai.cz</a>

Result on the web

<a href="https://www.ijournalse.org/index.php/ESJ/article/view/2192" target="_blank" >https://www.ijournalse.org/index.php/ESJ/article/view/2192</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.28991/ESJ-2024-08-02-02" target="_blank" >10.28991/ESJ-2024-08-02-02</a>

Result language

angličtina

Original language name

Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

Original language description

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. © 2024 by the authors.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Emerging Science Journal

ISSN

2610-9182

e-ISSN

2610-9182

Volume of the periodical

8

Issue of the periodical within the volume

2

Country of publishing house

IT - ITALY

Number of pages

13

Pages from-to

394-406

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85192378780