Intelligent Network Maintenance Modeling for Fixed Broadband Networks in Sustainable Smart Homes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00369869" target="_blank" >RIV/68407700:21230/23:00369869 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/JIOT.2023.3277590" target="_blank" >https://doi.org/10.1109/JIOT.2023.3277590</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JIOT.2023.3277590" target="_blank" >10.1109/JIOT.2023.3277590</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent Network Maintenance Modeling for Fixed Broadband Networks in Sustainable Smart Homes

Popis výsledku v původním jazyce

Due to the emergence of sustainable smart homes, each smart device requires more bandwidth putting pressure on the existing home networks. A very good solution to ensure high-bandwidth home networks is the fiber-to-the-home (FTTH) technology. FTTH delivers high-speed Internet from a central point directly to the home through fiber optic cables. This fixed broadband network can transmit information at virtually unlimited speed and capacity enabling homes to be smarter. Hence, a well-monitored and well-maintained FTTH broadband network is necessary to obtain a high level of service availability and sustainability in smart homes. This study aims to develop a predictive model that will proactively monitor and maintain FTTH networks through the use of sophisticated modeling techniques such as machine learning (ML). The predictive model targets to classify the proposed technician resolution based on the historical FTTH field data set. The results show that the K-nearest neighbors (KNN)-based model obtained the highest accuracy of 89% followed by the feedforward artificial neural network (FF-ANN)-based model with 86%. In addition, the identified anomalies from the data set affecting service degradation and performance include FTTH access issues, optical network unit issues, and faults in customer premises equipment.

Název v anglickém jazyce

Intelligent Network Maintenance Modeling for Fixed Broadband Networks in Sustainable Smart Homes

Popis výsledku anglicky

Due to the emergence of sustainable smart homes, each smart device requires more bandwidth putting pressure on the existing home networks. A very good solution to ensure high-bandwidth home networks is the fiber-to-the-home (FTTH) technology. FTTH delivers high-speed Internet from a central point directly to the home through fiber optic cables. This fixed broadband network can transmit information at virtually unlimited speed and capacity enabling homes to be smarter. Hence, a well-monitored and well-maintained FTTH broadband network is necessary to obtain a high level of service availability and sustainability in smart homes. This study aims to develop a predictive model that will proactively monitor and maintain FTTH networks through the use of sophisticated modeling techniques such as machine learning (ML). The predictive model targets to classify the proposed technician resolution based on the historical FTTH field data set. The results show that the K-nearest neighbors (KNN)-based model obtained the highest accuracy of 89% followed by the feedforward artificial neural network (FF-ANN)-based model with 86%. In addition, the identified anomalies from the data set affecting service degradation and performance include FTTH access issues, optical network unit issues, and faults in customer premises equipment.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Svazek periodika

10

Číslo periodika v rámci svazku

20

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

18067-18081

Kód UT WoS článku

001081924500036

EID výsledku v databázi Scopus

2-s2.0-85160219984