International Optical & Subsea Network Engineer | Professor & Speaker | Master’s Student in Computer Engineer & Telecom | Top 15 Favikon Creator in Mobile & Telecom Brazil | ITIL4 | HDI_SCM
DWDM Telemetry & Monitoring: Evolving with AI, ML, and IoT Telemetry and monitoring have long been essential for ensuring signal integrity and efficient operations. This technology is already well established. So, why are we discussing it? First, let me introduce how telemetry and monitoring tools can empower network operations: 1: Real-Time Optical Power Monitoring: Track power levels for each wavelength at every amplifier, line card, or multiplexer. 2: Automatic Fault Isolation and Root Cause Analysis (RCA): DWDM networks often use telemetry data to detect anomalies and isolate faults. When a wavelength shows unexpected loss or OSNR (Optical Signal-to-Noise Ratio) issues, RCA algorithms can pinpoint the exact section or component in need of attention, minimizing diagnostic time. 3: Latency and Jitter Analysis: Tracking latency for each channel can help monitor data integrity and ensure SLA compliance, particularly in high-demand services like video streaming or financial data transmission. Any increase in jitter or latency is logged and analyzed, ensuring quick response and quality maintenance. 4: Predictive Maintenance via Data Analytics: Telemetry data can be analyzed over time to identify trends and potential hardware degradation, enabling proactive replacements before failures occur—especially useful for high-traffic channels where unplanned downtime is costly. But if this is a well-known topic, what’s changing? Advanced AI and ML algorithms are now being used to analyze vast amounts of telemetry data, uncovering patterns that reveal network health insights far beyond traditional methods. This shift allows for: Predictive Maintenance: ML models identify potential issues before they impact network performance, replacing reactive troubleshooting with proactive, automated interventions. Anomaly Detection with Precision: AI-driven analytics can spot even subtle performance changes across wavelengths and channels, flagging potential issues early without relying on preset thresholds. IoT for Distributed Monitoring The use of IoT sensors across network components enables real-time data collection on temperature, vibration, and other physical parameters that affect optical performance. This additional layer of telemetry supports enhanced fault detection and optimizes power levels dynamically based on environmental conditions. The telemetry on DWDM networks is no longer just about monitoring channels; it’s a data-driven, AI-enhanced strategy for smarter, more resilient networks. Who else sees AI as the key to unlocking the full potential of optical networks? #DWDM #Telemetry #AI #MachineLearning #IoT #NetworkMonitoring #OpticalNetworks #Telecom