Enhancing DBSCAN clustering with fuzzy system to improve IoT-based WBAN performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F25%3A%230001253" target="_blank" >RIV/04274644:_____/25:#0001253 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-025-13293-9" target="_blank" >https://www.nature.com/articles/s41598-025-13293-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-13293-9" target="_blank" >10.1038/s41598-025-13293-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing DBSCAN clustering with fuzzy system to improve IoT-based WBAN performance

Popis výsledku v původním jazyce

Wireless Body Area Networks (WBANs) play a vital role in IoT-based healthcare, yet their dynamic conditions and resource constraints pose significant challenges to efficient data clustering and energy management. Traditional clustering methods, such as DBSCAN with static parameters, often fail to adapt to these challenges, leading to suboptimal network performance. WBANs networks face challenges such as a large number of nodes, limited energy resources, and diverse data types, which impact data clustering and energy optimization. This paper proposes a novel approach that enhances DBSCAN with a fuzzy system to dynamically optimize its parameters (Epsilon and MinPts) based on real-time inputs like node speed and RSSI. By adapting to varying network conditions, the proposed method achieves superior clustering accuracy, energy efficiency, and stability compared to conventional techniques. Simulations demonstrate significant improvements in network lifetime and cluster quality, making this approach a promising solution for real-time health monitoring in resourceconstrained WBANs. For example, the proposed approach exhibits significant superiority in cluster stability, with improvements of 80% over Classical DBSCAN, 28.57% over PSO Clustering, 38.46% over LEACH, and 20% over PEGASIS.

Název v anglickém jazyce

Enhancing DBSCAN clustering with fuzzy system to improve IoT-based WBAN performance

Popis výsledku anglicky

Wireless Body Area Networks (WBANs) play a vital role in IoT-based healthcare, yet their dynamic conditions and resource constraints pose significant challenges to efficient data clustering and energy management. Traditional clustering methods, such as DBSCAN with static parameters, often fail to adapt to these challenges, leading to suboptimal network performance. WBANs networks face challenges such as a large number of nodes, limited energy resources, and diverse data types, which impact data clustering and energy optimization. This paper proposes a novel approach that enhances DBSCAN with a fuzzy system to dynamically optimize its parameters (Epsilon and MinPts) based on real-time inputs like node speed and RSSI. By adapting to varying network conditions, the proposed method achieves superior clustering accuracy, energy efficiency, and stability compared to conventional techniques. Simulations demonstrate significant improvements in network lifetime and cluster quality, making this approach a promising solution for real-time health monitoring in resourceconstrained WBANs. For example, the proposed approach exhibits significant superiority in cluster stability, with improvements of 80% over Classical DBSCAN, 28.57% over PSO Clustering, 38.46% over LEACH, and 20% over PEGASIS.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001544985800024

EID výsledku v databázi Scopus

2-s2.0-105012464828