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

Abstract

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 dataset. The results show that the K-Nearest Neighbors (KNN)-based model obtained the highest accuracy of 89% followed by the Feed-Forward Artificial Neural Network (FF-ANN)-based model with 86%. In addition, the identified anomalies from the dataset affecting service degradation and performance include FTTH access issues, ONU issues, and faults in customer premises equipment.