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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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