# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 2 Related Work  
The objective of fiber monitoring is to detect any anomalies in the optical layer by analyzing the monitoring data. Several techniques are commonly used by engineers to identify fiber faults in Optical Distribution Networks (ODNs). For instance, one study [14] uses a Reference Reflector (RR) placed at the end of each fiber on the ODN and uses Optical Time-Domain Reflectometry (OTDR) to detect, locate, and estimate the reflectance of the connections and mechanical splices in the fiber links. Another approach uses binary-coded Fiber Bragg Granting (FBG) [11]. The FBG binary codes serve as indicators between one ONU to other ONUs by varying the wavelengths used by the FBGs to easily identify fault branches [11]. Some early studies have also proposed embedded OTDR called miniaturized OTDR integrated into the ONUs [15; 16; 17; 18]. Furthermore, to consistently meet the Service Level Agreement (SLA), network operators need a mechanism to maintain service continuity even when there are fiber faults in the ODN. In EPON, network operators usually use protection mechanisms such as trunk protection or tree protection. Trunk protection primarily focuses on protecting the OLT and feeder fiber. In contrast, tree protection covers the entire area but is very costly. Dedicated protection might deliver more reliability for service continuity but cannot provide efficient resource utilization [19]. Several studies have shown the use of ring topology to minimize the cost of establishing redundant paths in traditional EPON while handling any fiber cut or failures within the network [20; 21; 22]. Apart from all the various approaches such as tree, trunk, star, ring, or bus protection mechanisms, some studies also used hybrid topologies that improve EPON network redundancy but increase the network complexity [23]. Moreover, some studies also use SDN capability and a bus protection line to enhance the resilience of the existing EPON system [24]. Recently, Artificial Intelligence (AI) entities have been able to perform operations analogous to human activities, such as learning and decision-making. AI-based techniques are already changing and improving industries, including telecommunications networks. These techniques range from performance monitoring and guaranteeing the transmission to optical network control and management in both transport and access networks [25]. Current studies related to fiber monitoring already use the Machine Learning (ML) approach to detect any anomaly in the optical networks [12; 14; 26; 27]. These studies have shown that ML can detect and localize any fiber faults in the ODN. Although these studies have already proposed AI monitoring mechanisms, to the best of our knowledge, no studies have focused on integrated resilience that not only intelligently localizes any fiber faults in the ODN but also automatically recovers the network using AI mechanisms. Moreover, most studies only proposed the ML model without any simulation or experiment on the working PON systems. Table 1 presents a table of related work contributions. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**References** & **Contributions** & **Approach** & **Gap Addressed** \\ \hline \multirow{3}{*}{[11]} & Proposes Fiber Bragg & \multirow{3}{*}{FBG binary coding, wavelength variation} & Lacks AI integration and automatic fault recovery \\  & Grating (FBG) binary codes for fault branch identification & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work contributions. To realize this, our proposed architecture uses AI-enabled unified platforms to automate and adapt to changing circumstances and business needs. As Cisco's 2024 Global Networking Trends Report stated, in the next two years, network operators will use AI-enabled unified platforms to automate and adapt to changing circumstances and business needs [28]. SDN-Enabled Broadband Access (SEBA) is a unified cloud-native platform, providing scalable and flexible network management.