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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE Internet of Things Journal

  • ISSN

    2327-4662

  • e-ISSN

    2327-4662

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    20

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    15

  • Pages from-to

    18067-18081

  • UT code for WoS article

    001081924500036

  • EID of the result in the Scopus database

    2-s2.0-85160219984