Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022412" target="_blank" >RIV/62690094:18450/25:50022412 - isvavai.cz</a>

Výsledek na webu

<a href="https://hightechjournal.org/index.php/HIJ/article/view/1183" target="_blank" >https://hightechjournal.org/index.php/HIJ/article/view/1183</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.28991/HIJ-2025-06-01-022" target="_blank" >10.28991/HIJ-2025-06-01-022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization

Popis výsledku v původním jazyce

Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems. © 2025, Ital Publication. All rights reserved.

Název v anglickém jazyce

Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization

Popis výsledku anglicky

Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems. © 2025, Ital Publication. All rights reserved.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

HighTech and Innovation Journal

ISSN

2723-9535

e-ISSN

2723-9535

Svazek periodika

6

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

11

Strana od-do

352-362

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105006679283