Optical communications and networking are fast becoming the solution to support ever-increasing data traffic across all segments of the network, expanding from core/metro networks to 5G/6G front-hauling. Therefore, optical networks need to evolve towards an efficient exploitation of the infrastructure by overcoming the closed and aggregated paradigm, to enable apparatus sharing together with the slicing and separation of the optical data plane from the optical control. In addition to the advantages in terms of efficiency and cost reduction, this evolution will increase network reliability, also allowing for a fine trade-off between robustness and maximum capacity exploitation. In this work, an optical network architecture is presented based on the physical layer digital twin of the optical transport used within a multi-layer hierarchical control operated by an intent-based network operating system. An experimental proof of concept is performed on a three-node network including up to 1000 km optical transmission, open re-configurable optical add & drop multiplexers (ROADMs) and whitebox transponders hosting pluggable multirate transceivers. The proposed solution is based on GNPy as the optical physical layer digital twin and ONOS as intent-based network operating system. The reliability of the optical control decoupled by the data plane functioning is experimentally demonstrated exploiting GNPy as open lightpath computation engine and software optical amplifier models derived from the component characterization. Besides the lightpath deployment exploiting the modulation format evaluation given a generic traffic request, the architecture reliability is tested mimicking the use case of an automatic failure recovery from a fiber cut.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

We demonstrate the use of existing terrestrial optical networks as a smart sensing grid, employing a bidirectional long short-term memory (Bi-LSTM) model enhanced with an attention mechanism to detect road vehicles. The main idea of our approach is to deploy a fast, accurate and reliable trained deep learning model in each network element that is constantly monitoring the state of polarization (SOP) of data signals traveling through the optical line system (OLS). Consequently, this deployment approach enables the creation of a sensing smart grid that can continuously monitor wide areas and respond with notifications/alerts for road traffic situations. The model is trained on the synthetic dataset and tested on the real dataset obtained from the deployed metropolitan fiber cable in the city of Turin. Our model is able to achieve 99% accuracy for both synthetic and real datasets.

Relying on a two-measurement characterization, in this work a simple and effective gain profile model for dual-stage optical erbium-doped fiber amplifiers (EDFAs) working under full spectral load conditions is presented and validated. Starting from the model of an ideal EDFA, the gain ripple profile is determined as the target parameters of the amplifier vary using a linear combination of two contributions. Being a consequence of the absorption/emission curves of erbium in the two stages, the first represents a characteristic of the specific device and reflects also the residual impact due to the gain flattening filter (GFF), while the second parameter accounts for a feature of the considered amplifier model, scaling according to the set tilt target. The proposed model faithfully reproduces the dynamics of EDFA by varying the total input power and the target gain and tilt parameters, as shown experimentally on a set of 14 devices divided into 4 different models from 2 vendors, of which 10 in C-band and 4 in L-band. The obtained error distributions present an unbiased peak shape with variability between the 25 and 75 percentiles below 0.2 dB for the worst case in C-band and 0.1 dB in L-band, respectively.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting existing terrestrial fiber infrastructure. Employing a Waveplate model, we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data. These simulations help to enhance a Machine-Learning model that is trained and validated to detect Primary waves arrivals that precede earthquakes’ destructive Surface waves. The validation results show that the model achieves over 95% accuracy. The Machine-Learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates by a triangulation method and calculate fibers to epicenter distance. This setup allows early warning generation to municipalities close to epicenter location, and progressing to those further away. The model testing shows 98% of accuracy in detecting primary wave and one-second of detection time, affording nearby areas 21-seconds to take countermeasures, and an extended 57-seconds in more distant areas.

The paradigm shift toward autonomous optical networking is transforming the transport layer into a widespread distributed sensing platform. However, traditional telemetry architectures, designed for low-frequency performance monitoring (typically sampled at 1 Hz or 15 min intervals), fail to meet the stringent requirements of physical sensing applications, which demand kHz-range sampling to capture seismic events by using indirect metrics such as state of polarization (SOP) rotations or the Jones rotation vector. This paper proposes a high-sampling-rate, open-source telemetry architecture designed to bridge this gap. Implemented as a containerized microservices stack orchestrated via Docker Compose, the framework leverages Apache Kafka to decouple high-velocity sensing streams from persistent storage, elevating raw sensor data to a “First-Class Citizen” for immediate consumption by AI/ML agents. This approach facilitates multi-modal sensor fusion by enabling the temporal correlation of heterogeneous metrics in real-time. We validate the architecture through an extensive experimental scalability analysis using a Monte Carlo approach. Results demonstrate that the system sustains a throughput of over 30,000 messages/s—approximately 15 times the requirement for high-fidelity sensing—while maintaining sub-second end-to-end latency ( <200ms ) in the optimal batching regime. These findings confirm the architecture’s suitability for critical early warning scenarios, enabling robust, low-latency intervention in disaggregated optical networks.