# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Intelligence Fault Detection and Localization with Intelligence Diagnosis  
As mentioned before, this paper focuses on fault detection and localization through OTDR trace analysis and the eye diagram evaluation. Figure 5 illustrates the comparison between normal and fault conditions from these perspectives. Figure 5 shows a clear eye opening, indicating minimal noise, jitter, and distortion. In contrast, Figure 5 depicts a situation with anomalies. When there are anomalies in noise, jitter, or distortion, the eye diagram shows that the eye opening is reduced vertically and horizontally, distorting the eye shape, which indicates a very high level of noise, higher jitter, and potential issues with the transmission channel. Figure 5 shows the power attenuation for different ONUs located at different distances in a normal trace event, while Figure 5 highlights the scenario where ONU\({}_{1}\) experiences a fiber fault. The OTDR trace results for ONU\({}_{1}\) show a loss, with no peak detected, indicating the presence of a fault. Typically, both the OTDR trace analysis and eye diagram are tested against a predefined mask. Any violation of these masks can indicate potential fiber faults within the ODN.  
Consequently, in our proposed fault detection and localization approach, we use eye diagrams to complement the OTDR in identifying subtle degradation in signal quality, since OTDR alone only detects severe faults such as fiber cuts. The proposed ML model uses this combination of eye diagram and OTDR data to enhance the accuracy of prediction  
\begin{table}
\begin{tabular}{c c c} \hline
**VLAN Tag** & **Source/Destination** & **Handling Instructions** \\ \hline
100 & Downstream to ONU\({}_{1}\) & Forward to ONU\({}_{1}\) via RFoG \\
200 & Downstream to ONU\({}_{2}\) & Process locally (ONU\({}_{2}\) data) \\
101 & Upstream from ONU\({}_{1}\) & Forward to OLT via RFoG \\ \hline \end{tabular}
\end{table}
Table 2: VLAN Tag Example.  
Figure 5: Comparison of normal vs. fault condition.  
and localization. This leads to improved accuracy and efficiency, especially in identifying minor or soft faults that would not be captured by OTDR alone.  
The proposed framework for fault detection and localization with intelligent diagnosis is shown in Figure 6, following the study in [12]. There are five main stages to realize the proposed framework, namely, (1) Data collection: The deployed ODN infrastructure is periodically monitored using OTDR and BER Analyzer. The generated OTDR traces and the eye diagram data are sent to the SDN controller; (2) Data processing: The collected data is pre-processed to normalize and standardize the features to a similar scale; (3) Anomaly detection: The processed data are compiled into a dataset, which is then used to train and evaluate a machine learning model designed to detect anomalies in the network; (4) Fiber fault diagnosis and localization ML model; (5) Mitigation and recovery from fiber failures plan: The plan will be formulated to address and fix the detected faults. Alerts are generated to notify engineers and customers of the issues. The SDN controller facilitates dynamic management and control of the network based on the machine learning model outputs.