GN5-2 Fibre Sensing: Technologies, Users and Use Cases for NRENs

© GÉANT Association on behalf of the GN5 -2 project. The research leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101194278 (GN5 -2). Co -funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them. 11-11-2025 Fibre Sensing: Technologies, Users and Use Cases for NRENs Grant Agreement No.: 101194278 Work Package: WP6 Task Item: T1 Nature of Document: White Paper Dissemination Level: PU Lead Partner: GÉANT Document ID: GN5-2-25-112DBD Authors: Gloria Vuagnin (GARR), Ramaz Kvatadze (GRENA) Chris Atherton (GÉANT), Jan Randil (CESNET), Magnus Bergroth (SUNET), Susanne Naegele-Jackson (FAU), Ivana Golub (PCSS), Pavle Vuletic (AMRES), Piotr Turowicz (PCSS), Leonidas Konstantopoulos (GRNET) and Rasmus Lund (NORDUnet) Abstract This document provides a brief overview of fibre-sensing technologies and the possibilities offered by these techniques in both the structural monitoring of National Research and Education Network (NREN) infrastructure and in Earth observation and monitoring. Studying these new technologies by identifying potential NREN use cases, along with wider applications in the national and GÉANT pan-European infrastructures, offers a useful source of data to NRENs, and to the multiple and heterogeneous research communities connected by NRENs whose research stands to benefit. This document is aimed at NRENs but is comprehensible enough that other audiences, such as research communities and industries, will find it useful. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD ii Contents Executive Summary 1 1 Introduction 2 2 Overview of Fibre-Sensing Technologies 3 2.1 Scattering-Based Sensing 3 2.2 Forward Transmission-Based Sensing 4 2.3 Additional Requirements 5 2.4 Considerations for Deploying DFOS Technologies 5 3 Users Communities and Possible Use Cases 7 3.1 NREN Use Case: Enhancing Infrastructure Health and Security 10 3.2 NREN Support for Research Communities 11 3.3 Overview of Fibre-Sensing Use Cases 12 4 Conclusions 15 Glossary 16 References 17 Figures Figure 3.1: Overview of the DAS and SOP frequency ranges in the logarithmic scale. SOP detectable frequency range is shown in purple; DAS detectable frequency range in yellow; red represents the overlap between the two technologies 14 Tables Table 2.1: Comparison of the different fibre-sensing techniques with respect to the type of technique used, range, deployment considerations and indicative unit cost 6 Table 3.1: Regulatory drivers and benefits of distributed fibre-optic sensing for NRENs 8 Table 3.2: Main areas for fibre-sensing deployment together with potential user groups 9 Table 3.3: Map between the area of interest, frequency range of events and a comparison if the described technology can detect all signals (Y), part of the frequency range (P), or none of the frequency range (N) 13 Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 1 Executive Summary This white paper examines fibre sensing, a transformative emerging technology in optical networking. Traditionally, optical networks have been used to transport huge amounts of data. However, optical networks (more precisely, optical fibres) can be used for sensing purposes as well: proactively monitoring the environment around optical cables not only to protect the network itself, but also to offer insights into the environment and provide alerts for all manner of events ranging from unauthorised intrusions to disasters such as fires in tunnels and tsunamis. This dual functionality is particularly relevant for GÉANT and the NRENs, which have decades of experience in managing extensive terrestrial and submarine fibre infrastructure. This document examines how fibre sensing offers NRENs the opportunity to enhance the monitoring and protection of their networks while simultaneously generating valuable data for scientific research and civil protection. The operational benefits from integrating fibre-sensing technologies into NREN infrastructure include improved fault detection and localisation, enhanced network resilience, and compliance with regulatory frameworks such as the EU’s NIS2 Directive and 5G Security Toolbox. Fibre sensing also supports proactive cable system maintenance and security measures such as intrusion detection. Beyond network operations, fibre sensing has a broad array of applications across industry. It supports research in fields such as geophysics, oceanography, climatology, and biology. Civil engineering applications include structural monitoring and traffic analysis, while the utility sector benefits from leak detection and infrastructure surveillance, making fibre sensing a valuable asset across the public, academic and industrial sectors. This wide range of applications positions NRENs as potential key enablers, supporting both advanced network operations and the advancement of their constituents in research and education. After reviewing these fibresensing user communities with focus on the NREN perspective, this document presents a mapping of specific application domains to their associated frequency ranges and then outlines the appropriate fibre-sensing technology to use in each case. These technologies cover different but complementary frequency ranges, enabling tailored data collection ranging from high-frequency mechanical vibrations and audio surveillance to low-frequency oceanographic monitoring and long-term structural changes, depending on the events that a given user community seeks to observe. The paper concludes that owing to the convergence of communication and sensing technologies, fibre sensing is positioned as a critical component of future network and research infrastructure and thus is of critical importance to NRENs. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 2 1 Introduction Fibre sensing has emerged as a ‘hot’ topic over the last few years [1][2]. NRENs and GÉANT, both of which own and manage both terrestrial and submarine optical fibre pairs and spectrum, are increasingly studying sensing technologies due to the opportunities they provide: protection and proactive monitoring of the physical network infrastructure, as well as significant gains for NREN users engaged in scientific research, civil protection through environmental monitoring capabilities, and in some cases, national security. Fibre-sensing technologies can turn optical fibres into sources of data, measuring the variation of a signal in specific optical transmission parameters (amplitude, phase, polarisation, and frequency) as it passes through an optical fibre. It is now possible to detect minimal deformations experienced by a fibre when the surrounding environment undergoes changes. These environmental changes may be caused by natural events such as earthquakes, landslides, tsunamis or by human activities such as roadwork, construction, or intrusion into the area where the fibre is housed. However, this topic has been made increasingly relevant in recent years by the evolution of sensing technology toward solutions that allow its coexistence with data transmission on the same optical fibres [3]. This development permits the use of Distributed Fibre-Optic Sensing (DFOS) on infrastructure resources that in the past were solely dedicated to transmission. It has become clear that there are multiple uses, by multiple communities, for the vast amounts of data that are generated by fibre-sensing instruments. With the wide adoption of artificial intelligence, the automated selection of datasets from multiple data sources can now be tailored to serve specific research and user communities and generate new perspectives from the amalgamation of different datasets, leading to new scientific discoveries. NRENs, as service providers in an increasingly data-product-driven environment, thus have the opportunity to manage and make this data available to their user communities, as well as benefiting themselves via enhanced monitoring and responding to threats to physical infrastructure. The remainder of this document is structured as follows: Section 2 offers a brief overview of some of these distributed fibre-sensing technologies. Section 3 discusses the range of possibilities these technologies offer to the different user communities and maps the areas of interest, frequency ranges of events to sense, and the appropriate sensing technology to use in each case. The document ends with a conclusion in Section 4. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 3 2 Overview of Fibre-Sensing Technologies There are currently two main methods of sensing using cables: point-based sensing and distributed fibre-optic sensing (DFOS) [4]. The concept behind point-based sensing is the addition and integration of physical equipment along the cable that houses optical fibres, primarily either Science Monitoring and Reliable Telecommunications (SMART) sensors [4][5] or a fibre Bragg grating [1]. SMART sensors use the power and telecommunication connectivity provided by the cable system itself and feature on submarine cable systems as opposed to terrestrial systems. However, there are currently no known deployed or active cable systems that utilise SMART technology (although there are plans to deploy some in the near future). The full realisation of this concept depends on cable manufacturers changing their cable designs to introduce as-of-yet unproven technology into the system. It also means that only newly laid cables can benefit from the technology. In comparison, fibre Bragg gratings are passive devices typically used in optical line systems to provide wavelength-selective filtering, reflecting specific wavelengths back to a source. When used in combination with a laser source, quasi-distributed sensing can be achieved [6]. The point-based sensing technology discussed above focuses on generating data from around specific locations along a cable path, rather than providing information on the status of the cable system itself. DFOS, on the other hand, utilises the actual optical fibre within a cable system as the sensing medium itself. This means that existing cable systems (including decommissioned systems, with some specific techniques) can be retrofitted with this technology. DFOS can be deployed on both submarine and terrestrial cable systems. As the optical fibre is used as the medium, the data obtained can be used to understand the status of the optical fibre while telecoms traffic is passing through it. DFOS can also be used to complement point-based sensors, allowing multiple data sources to operate along a single cable system simultaneously. Within the field of distributed sensing, there are currently two main technical categories: scattering-based sensing and forward-transmission-based sensing. The remainder of this document provides a brief description of the technical approaches of the distributed-sensing technologies with significant potential for successful application in NRENs’ network infrastructures for cable protection and scientific observations. 2.1 Scattering-Based Sensing Scattering-based technologies involve the injection of a laser source into a fibre optic cable, with the returned reflections (backscatter) being recorded and analysed. Backscattering (i.e., the reflection of signals back to the direction from which they came) occurs as a result of tiny changes in the density of the glass core of the optical fibres arising from small imperfections created during its manufacture. Environmental changes in the medium surrounding the fibre, as well as changes to the physical medium of the fibre itself (e.g., stretching, straining, pressure or temperature) can cause changes to (i.e., shifts in the phase of) that backscattered light. Each reflection point becomes a sensing point, meaning that along the length of a fibre, many points can be monitored at varying distances [7]. Taking into account that the speed of light in optical fibre is known, and the time it takes for light to travel along and back through an optical fibre can be measured, this technique allows the detection of the location of those environmental changes along the cable to a precision of around 1 metre [2]. Backscattering occurs mainly in three forms – Rayleigh [8], Raman [9], and Brillouin scattering [10]. Currently, the first two, Raman and Rayleigh scattering, are used to detect changes in cables. For each of these, a particular technical technique is used to extract information. Distributed Acoustic Sensing (DAS), also called Distributed Overview of Fibre-Sensing Technologies Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 4 Dynamic Strain Sensing (DDSS), refers to the Rayleigh-based scattering effect. Despite the word “acoustic” in “DAS” perhaps suggesting the use of sound pressure waves, DAS does not detect pressure directly; it detects strain in the fibre. Raman-based techniques, based on Distributed Temperature Sensing (DTS) [4], are also growing in popularity. In the following sections, only Rayleigh scattering is considered as it is the most widely used. However, the key challenge for any scattering technique is the identification and classification of signals that couple into the infrastructure (e.g. optical cables) as strain and how. This is currently under active investigation by various research communities. Due to attenuation of the optical signal along the fibre, the maximum sensing range for DAS is typically limited to around 50 km, or up to the location of the first repeater unit in Dense Wavelength Division Multiplexing (DWDM) [ 11 ] optical-fibre cable systems, both submarine and terrestrial. While some advanced DAS interrogation techniques can extend the sampling range beyond this, in some cases to as far as 150–170 km, practical sensing often terminates at the first repeater. This is because repeaters, to protect the optical transmission components, commonly contain optical filters and amplifiers that inhibit the backscattered light necessary for DAS operation. Injecting a DAS signal into a live telecommunications system presents challenges, particularly for transmission equipment. However, research [12] has demonstrated simultaneous telecommunication and DAS operation at different wavelengths within the same fibre, known as optical multiplexing, as an effective solution. In May 2024, a four-day DAS dataset was collected using an L-band interrogator multiplexed onto the submarine cables of the Ocean Observatories Initiative’s Regional Cabled Array, offshore central Oregon [13]. The results showed that multiplexed DAS is neither degraded by network traffic nor impacts communications. Furthermore, the quality of DAS data obtained via multiplexing was comparable to that collected on dark fibre. Using a machine-learning event detection workflow, researchers identified 31 T-waves and the S-wave of one regional earthquake, thereby demonstrating the feasibility of continuous earthquake monitoring with offshore multiplexed DAS. 2.2 Forward Transmission-Based Sensing Coherent light is used in telecommunications because maintaining fixed phase relationships between light waves allows advanced modulation formats that greatly increase spectral efficiency, while the ability to separate orthogonal polarisations effectively doubles the capacity of each fibre. As such, coherent detection enables digital signal processing to correct for noise such as chromatic dispersion, polarisation mode dispersion and other non-linear effects. These non-linear effects are in part caused by a range of factors along the whole length of the fibre. However, what may be noise to some is signal to others. It is increasingly recognised that coherent detection and digital signal processing (DSP) techniques now allow telecom fibres to serve as programmable sensors [14]. With respect to polarisation, different polarised optical waves propagate with different propagation constants. In optical fibre, this is due to intrinsic fibre birefringence [15]. Both internal and external factors can influence fibre birefringence. The internal fibre birefringence arises from imperfections in fibre manufacturing, such as core ellipticity, bending, twisting, or even material impurities and inhomogeneities. The external factors are changes in environmental conditions (rain, wind gust, snow, heat, lightning, or temperature variations), mechanical stress and pressure. By monitoring the state of polarisation (SOP), we can monitor the changes in fibre birefringence over time. Polarisation changes are monitored by recording certain parameters, known as Stokes parameters. Stokes parameters are a set of values that describe the polarisation state of electromagnetic radiation [16]. There are two techniques possible for SOP sensing: • Monitoring of the SOP of the transported data signal at the DWDM transceivers. • Dedicated SOP polarimeter over a DWDM monitoring channel. Overview of Fibre-Sensing Technologies Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 5 Alongside these traditional SOP techniques, SOP Optical Time Domain Reflectometry (SOP-OTDR) is another technique, first published in March 2022 and piloted by Infinera and Google [1]. SOP-OTDR in subsea optical communication systems combines point-based sensing with forward transmission-based DFOS sensing and allows monitoring of subsea cable health while simultaneously monitoring physical phenomena around the cable at certain points along the cable. This technique uses the periodic transmission of tones generated by a polarimeter, rather than from the telecoms traffic itself, along a cable and utilises built-in high-loss loopbacks (HLLBs) to obtain reflections of these tones at every repeater. As with the traditional SOP techniques, these reflected signals are filtered to eliminate out-of-band noise, then converted to their corresponding Stokes parameters for further analysis. The same technique can be applied to terrestrial cables [3]. It should be noted that SOP and other forward-transmission-based sensing techniques are at a much lower technology readiness level (TRL) than scattering-based sensing techniques. 2.3 Additional Requirements Alongside the sensing technologies, a consistently precise time source needs to be provided. This could be, for example, via satellite navigation systems, a network time protocol (NTP), a precision time protocol (PTP), or White Rabbit, a technology based on a combination of PTP and Synchronous Ethernet (SyncE) [17]. The time source allows the sensing data generated to be accurately compared with data produced from other locations and using other technologies. The quantity of sensing data generated can be substantial depending on the configurations used. DAS is the most prolific, potentially generating ~7TB of data per day. Typically, sensing instruments come with a Network Attached Storage (NAS), so disks can be shipped directly to research facilities or a centralised storage location. However, for streaming of data, sufficient connectivity is required. For low-throughput, heavily downsampled data and out-of-band access, 5G dongles are used, while Ethernet connectivity via a fixed network provides access to full raw data files. Once the data is extracted from the instrument(s), it then needs to be processed and stored. In certain instances, raw data (i.e., unprocessed) is kept in a secure location for retrieval by authorised parties as required for later processing into derived data products for their domain-specific needs. To account for the needs of domainspecific knowledge production from sensing data, the creation of specific ecosystems by the relevant national research institutes and their communities is suggested [18]. To effectively utilise the large amount of generated data (especially in the DAS context), careful design of data management systems, including computational and storage resources, is essential. Machine learning is playing an important role both in improving optical-fibre sensor performance by introducing innovative problem-solving approaches, and in near-real-time analyses [19]. As such, fibre sensing, although facilitated and supported by the network, requires a full-stack approach to support its operations. 2.4 Considerations for Deploying DFOS Technologies Each fibre-sensing technology sits at a different level of maturity and has its own limitations and trade-offs. As a result, rollout of a given technology and its potential use cases depend on a range of technical and contextual factors. Table 2.1 below summarises the indicative limitations, capabilities, and costs of the most widely used instruments. The figures in the Equipment costs column refer only to the sensing units themselves and not the broader expense of deploying an integrated system. For example, adapting a network to host a DAS unit in a cable landing station or amplifier site — facilities that may have previously served only as pass-through or inline amplifier sites without layer 3 breakout capacity — introduces additional, context-specific costs that are difficult to generalise. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 6 Parameter sensed Maximum recording distance Maximum detected frequency (Hz) Constraints Cable requirements Injected power (dBm) Data produced per day Equipment costs DAS (C Band) Rayleigh backscatter 50-170KM 10,000 Hz Can’t pass first repeater1 Dark fibre 0 to 24 dBm ~7 TB ~200 K€ DAS (L Band) Rayleigh backscatter 50-170KM 10,000 Hz Can’t pass first repeater1 Accessible L band 0 to 14 dBm ~7 TB ~200 K€ Transceiver SOP Stokes variables Cable length 20 Hz Low frequency resolution DWDM system 0 dBm 3 GB ~50 K€ Polarimeter SOP Stokes variables Cable length 20 Hz Low frequency resolution DWDM channels 0 dBm 6-250 GB ~50 K€ Table 2.1: Comparison of the different fibre-sensing techniques with respect to the type of technique used, range, deployment considerations and indicative unit cost Despite these constraints, research and development in fibre sensing is progressing rapidly. This acceleration is driven by the breadth of potential applications and by the diverse user communities eager to exploit the data these instruments can provide. A variety of user communities and their potential fibre-sensing use cases are explored in the following section. 1 DAS operation requires backscattering of light in order to work. The optical repeater / regenerator filters out backscattering for protection, so the laser light isn’t reflected back past the repeater, hence DAS signals can’t pass the first repeater. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 7 3 Users Communities and Possible Use Cases Acquiring data through fibre-sensing techniques on the Research and Education (R&E) optical fibre infrastructure may be of interest to an NREN in two different aspects: using the data generated for the network to increase the health and security of the infrastructure itself, and generating data with the network on behalf of the communities it supports [2][20]. While the raw data and frequency ranges collected can be used by multiple communities, including NRENs, the derived data products for a specific community typically vary and don’t overlap. It is the post-processing of raw data where a community can extract real value for their particular use case. When considering using fibre sensing for the network, the use cases align with a telecom operator’s core priorities: safeguarding service integrity, protecting assets, and minimising user impact – the focus of which (although not the only one) is predominantly operational and with the aim of increasing the speed of reaction to incidents, such as improving fault localisation, and identification of third-party interference in the network. Experiencing delays during outages is costly and raises the overall risk profile on a network through lack of redundancy and resiliency. Thus, an improvement in the time required for fault localisation and restoration of fibre cuts — and even prevention of downtime — are key drivers in the adoption of fibre-sensing technologies and, where appropriate, furnishing the evidential material gathered to support law-enforcement enquiries. While not limited to NRENs, the ability to monitor, detect, and respond to anomalies is not only sound operational practice; it is increasingly a regulatory requirement. Under the NIS2 Directive, providers of public electronic communications networks and services are designated as essential entities, subject to stronger harmonised cybersecurity obligations across the EU [21]. Similarly, investment agencies are now linking funding for fibre-optic infrastructure to security requirements, including conformance with the EU 5G Security Toolbox. Measure TM03 of the Toolbox explicitly calls for monitoring, detection, and timely response to anomalies [22]. Table 3.1 offers an overview of some of the regulatory drivers and benefits of DFOS for NRENs. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 14 Figure 3.1: Overview of the DAS and SOP frequency ranges in the logarithmic scale. SOP detectable frequency range is shown in purple; DAS detectable frequency range in yellow; red represents the overlap between the two technologies What emerges clearly from this mapping is that no single technique covers all use cases. DAS offers excellent sensitivity across a wide spectrum, making it ideal for seismic studies, cetology, infrastructure monitoring and intrusion detection, while being limited in sensing range along a cable. SOP sensing, while lower in sensitivity and technology readiness level, leverages coherent transmission equipment already embedded in many networks, providing a low-cost and scalable solution for monitoring network health and detecting lowerfrequency perturbations such as oceanographic parameters along the whole length of a network segment. For NRENs, the implication is twofold. First, selecting the appropriate sensing technology depends on the user community being supported: geophysicists and oceanographers often favour DAS, whereas telecom operators may gain immediate operational benefits from SOP. Second, there are clear opportunities to combine techniques or expand into newer approaches such as interferometric sensing in order to enrich the range of detectable events [26]. Ultimately, these mappings reinforce the idea that fibre sensing is not a niche capability, but a multi-disciplinary enabler. By understanding how event frequencies align with sensing technologies, NRENs and their partners can prioritise deployments, facilitate data sharing with research communities, and position their networks as platforms for both resilient connectivity and scientific discovery. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 15 4 Conclusions This paper showed that optical fibre sensing is a set of technologies with highly useful applications, not only for NRENs and their intrinsic use case of ensuring the health of their network infrastructures, but also for NRENs as operators and owners of optical telecom fibre in their work of supporting research and education. Among the main benefits to NRENs from using the fibre network as a passive sensor are: • Detection of fibre conditions and monitoring the health status of infrastructure. • Perimeter security, surveillance, and the creation of anti-intrusion systems to protect their Point-ofPresence (PoP) sites. • Monitoring of data integrity in transit. NRENs and users utilising fibre sensing for terrestrial applications may benefit from using SOP methods to keep the costs of such solutions low because of competing terrestrial solutions (simple sensors or CCTV analogues). Less expensive variants of DAS, using inferior and therefore less expensive components (e.g., low-cost coherent lasers), may also be an interesting variant for NRENs to collect the data they require. However, this would need to be balanced with the needs of the research communities that wish to utilise high-quality data in their research and the efficacy of the sensitivity results gathered. The convergence of sensing and communications is becoming increasingly practical. According to the paper Using Global Existing Fiber Networks for Environmental Sensing [14], coherent detection and digital signal processing (DSP) techniques now allow telecom fibres to serve as programmable sensors. This document also mapped the frequency ranges of interest to specific research areas to the appropriate fibresensing technologies. Legal and security aspects that require consideration in the context of fibre sensing were briefly discussed; further work in these areas must include the NREN communities and ecosystem. Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 16 Glossary DDSS Distributed Dynamic Strain Sensing DFOS Distributed Fibre-Optic Sensing DSP Digital Signal Processing DTS Distributed Temperature Sensing DWDM Dense Wavelength Division Multiplexing HLLB High-Loss Loopback ISP Internet Service Provider MTTD Mean Time To Detect MTTR Mean Time To Repair NAS Network Attached Storage NOC Network Operations Centre, NREN National Research and Education Network NTP Network Time Protocol OTDR Optical Time-Domain Reflectometry, PoP Point of Presence PTP Precision Time Protocol R&E Research and Education SMART Science Monitoring and Reliable Telecommunications SOP State of Polarisation SOP-OTDR State of Polarisation - Optical Time Domain Reflectometry SyncE Synchronous Ethernet TRL Technology Readiness Level ϕ-OTDR Phase-Sensitive Optical Time-Domain Reflectometry Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 17 References [1] Mertz, P., ‘Seismic Event Detection and Localization Using Submarine Optical Cables’, in OFC, 9th March 2022, San Diego, California, USA. [2] Wellbrock, G., Xia, T., Huang, M., Han, S., et al., ‘Explore Benefits of Distributed Fiber Optic Sensing for Optical Network Service Providers’, in J. Lightwave Technol. 41 (12), pp. 3758–3766. DOI: 10.1109/JLT.2023.3263795. https://ieeexplore.ieee.org/document/10091246 [3] Brusin, A., Rizzelli, G., Fasano, M., Morosi, J., et al., ‘Overview and analysis of optical sensing techniques over deployed telecom networks’, in 2024 24th International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 7/14/2024 - 7/18/2024: IEEE, pp. 1–4. https://www.researchgate.net/publication/383672948_Overview_and_analysis_of_optical_sensing_t echniques_over_deployed_telecom_networks [4] Hartog, A., (2017). ‘An introduction to distributed optical fibre sensors’, Boca Raton, ISBN 978-1-13808269-4. OCLC 960843043. [5] Howe, B., Arbic, B., Barnes, C., Bayliff N., et al., ‘SMART Cables for Observing the Global Ocean: Science and Implementation’, in Frontiers in Marine Science, Volume 6 – 2019, https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2019.00424, doi:10.3389/fmars.2019.00424 [6] Gao, W., Liu, J., Guo, H. et al. Multi-Wavelength Ultra-Weak Fiber Bragg Grating Arrays for LongDistance Quasi-Distributed Sensing. Photonic Sens 12, 185–195 (2022). https://doi.org/10.1007/s13320-021-0635 [7] Ip, Ezra; Fang, Jian; Li, Yaowen; Wang, Qiang; et al., ‘Distributed fiber sensor network using telecom cables as sensing media: technology advancements and applications’, in J. Opt. Commun. Netw. 14 (1), A61. DOI: 10.1364/jocn.439175. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). Milano, Italy, 10/25/2023 - 10/27/2023: IEEE, pp. 383–388, https://zenodo.org/records/13373490 [8] Rayleigh Scattering and Raman Effect: An overview https://www.sciencedirect.com/topics/medicineand-dentistry/rayleigh-scattering [9] Overview of Raman Scattering https://www.fkf.mpg.de/5471793/07_Raman-Scattering.pdf [10] Brillouin Scattering – nonlinearity, optical fibres, threshold https://www.rpphotonics.com/brillouin_scattering.html [11] Dense Wavelength Division Multiplexing (DWDM) Overview https://www.sciencedirect.com/topics/computer-science/dense-wavelength-division-multiplexing [12] Shi, Q., Williams, E., Lipovsky, B., Denolle, M., et al., ‘Multiplexed Distributed Acoustic Sensing Offshore Central Oregon’, in Seismological Research Letters 2025; 96 (2A): 784–800. doi: https://doi.org/10.1785/0220240460 [13] Shi, Q., Williams, E., Lipovsky, B., Denolle, M., et al., ‘Multiplexed Distributed Acoustic Sensing Offshore Central Oregon’, retrieved from https://par.nsf.gov/biblio/10586593, in Seismological Research Letters 96.2A Web. doi:10.1785/0220240460. [14] E. Ip et al., ‘Using Global Existing Fiber Networks for Environmental Sensing,’ in Proceedings of the IEEE, vol. 110, no. 11, pp. 1853-1888, Nov. 2022, doi: 10.1109/JPROC.2022.3199742 [15] Birefringence – Wikipedia https://en.wikipedia.org/wiki/Birefringence [16] Stokes, G. G., ‘On the composition and resolution of streams of polarized light from different sources’, in Transactions of the Cambridge Philosophical Society, 9, 399 (1851) [17] White Rabbit Official CERN Website https://white-rabbit.web.cern.ch/ [18] Belter, B., Atherton, C., Vuagnin, G., Radil, J., et al., ‘GN5-1 Fibre Sensing Focus Group: Conclusions and Future Work’, GÉANT, https://doi.org/10.5281/zenodo.11190803 References Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 18 [19] Zhou, Y., Zhang, Y., Yu, Q., Ren, L., et al., ‘Application of machine learning in optical fiber sensors’, Measurement, Volume 228, 2024, 114391, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2024.114391 [20] Clare, M., ‘Submarine Cable Protection and the Environment’, in International Cable Protection Committee (ICPC), April 2024, Issue #8 [21] European Parliament; Council of the European Union (2022): Directive (EU) 2022/2555 of 14 December 2022 on measures for a high common level of cybersecurity across the Union, amending Regulation (EU) No 910/2014 and Directive (EU) 2018/1972, and repealing Directive (EU) 2016/1148 (NIS 2 Directive). Official Journal of the European Union L 333, 27.12.2022, pp. 80–152. [22] European Commission (2020): EU Toolbox for 5G Security: A set of robust and comprehensive measures for an EU coordinated approach to secure 5G networks. Publications Office of the European Union, 30 June 2021 (latest edition). [23] SUBMarine cablEs for ReSearch and Exploration – SUBMERSE https://submerse.eu/ [24] GÉANT GN 5-2 https://geant.org/gn5-2/ [25] Polar Connect https://nordu.net/polar-connect/ [26] Smart European Networks for Sensing the Environment and Internet Quality https://senseiproject.eu/ [27] Gritsenko, T., Chesnokov, G., Koshelev, K., Khan, R., et al. (2023), ‘Optical Fiber Sensor for Real-Time Monitoring of Industrial Structures and Application to Urban Telecommunication Networks’ in 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). doi:10.1109/MetroXRAINE58569.2023.10405703 [28] Carver, C., Zhou, X., (2024) ‘Polarization sensing over terrestrial optical fiber networks’, https://doi.org/10.21203/rs.3.rs-3838279/v1 [29] Gong, A., Huang-Chang, L., Chuang, Y., Li, T., et al., ‘Design and Implementation of Acoustic Sensing System for Online Early Fault Detection in Industrial Fans’, in Journal of Sensors, 2018, https://doi.org/10.1155/2018/4105208 [30] Killeen, J., Davis, I., Wang, J., Bennett, G., ‘Fan-noise reduction of data centre telecommunications’ server racks, with an acoustic metamaterial broadband, low-frequency sound-absorbing liner’, in Applied Acoustics, Volume 203, 2023, 109229, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2023.109229 [31] Wüstrich, L., Gallenmüller, S., Pahl, M.O. and Carle, G., ‘AC/DCIM: Acoustic Channels for Data Center Infrastructure Monitoring’, in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium (pp. 1-5), IEEE [32] Boever, J., ‘When Security is Hanging by a Thread – The Lonely (but Urgent) Case for Physical Infrastructure Protection," Broadband Coverage in Germany; 14. ITG Symposium, Berlin, Germany, 2020, pp. 1-5. [33] Mazur, M., Parkin, N., et. al., ‘Continuous Fiber Sensing over Field-Deployed Metro Link using RealTime Coherent Transceiver and DAS’, European Conference on Optical Communication © Optica Publishing Group 2022, 18 September 2022, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9979393&tag=1 [34] Thiriet, P., Lecoulant, J., Labat, V., Boudraa, A., et al., ‘Distributed Acoustic Sensing for Ship Detection and Identification’, in OCEANS, 2025, pp. 1-6, doi: 10.1109/OCEANS58557.2025.11104529 [35] Landrø, M., Bouffaut, L., Kriesell, H.J. et al. ‘Sensing whales, storms, ships and earthquakes using an Arctic fibre optic cable’ in Sci Rep 12, 19226 (2022). https://doi.org/10.1038/s41598-022-23606-x [36] Gurevich, B., Isaenkov, R., Erbe, C. et al., ‘Detection of aircraft noise using distributed acoustic sensing with a buried telecommunication cable’ in npj Acoust. 1, 2 (2025). https://doi.org/10.1038/s44384025-00007-8 [37] A. Baird, et al, ‘Velocity Model Analysis and Relative Event Locations Using DAS: a Case Study from Iceland’ in European Association of Geoscientists & Engineers, 85th EAGE Annual Conference & Exhibition, Jun 2024, Volume 2024, p.1 – 5. https://doi.org/10.3997/2214-4609.2024101365 [38] Xie, T., Zhang, C., Shi, B., Wang, Z., et al., ‘Seismic monitoring of rockfalls using distributed acoustic sensing’ in Engineering Geology, Volume 325, 2023, 107285, ISSN 0013-7952. https://doi.org/10.1016/j.enggeo.2023.107285 [39] Fang, G., Li, Y., Zhao, Y., Martin, E., ‘Urban Near-Surface Seismic Monitoring Using Distributed Acoustic Sensing’ in Geophysical Research Letters, Volume 47, issue 6, March 2020, https://doi.org/10.1029/2019GL086115 References Fibre Sensing: Technologies, Users and Use Cases for NRENs Document ID: GN5-2-25-112DBD 19 [40] Saverio Pellegrini, Leonardo Minelli, Lorenzo Andrenacci, Giuseppe Rizzelli, et al., ‘Overview on the state of polarization sensing: application scenarios and anomaly detection algorithm’, in J. Opt. Commun. Netw. 17, A196-A209 (2025), https://opg.optica.org/jocn/abstract.cfm?URI=jocn-17-2-A196 [41] Khacef, Y., (2024) ‘Advanced road traffic monitoring with distributed acoustic sensing and deep learning’ (Doctoral dissertation, Université Côte d'Azur) [42] Bednarski, L., Howiacki, T., Sieńko, R., Zuziak, K., ‘Distributed Fibre Optic Sensors (DFOS) in Measurements of Rail Strain and Displacements’ in Procedia Structural Integrity, volume 64, 2024, p1681-1688, https://doi.org/10.1016/j.prostr.2024.09.173 [43] Liu, J., Yuan, S., Luo, B., Biondi, B., et al., ‘Turning Telecommunication Fiber-Optic Cables into Distributed Acoustic Sensors for Vibration-Based Bridge Health Monitoring’ in Structural Control and Health Monitoring, April 2023, https://doi.org/10.1155/2023/3902306 [44] Tejedor, J., Macias-Guarasa, J., Martins, H.F., Pastor-Graells, J., et al., ‘Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review’ in Appl. Sci. 2017, 7, 841, https://doi.org/10.3390/app7080841 [45] Zhu, H.-H., Liu, W., Wang, T., Su, J.-W., Shi, B., ‘Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends’ in Sensors, 2022, 22, 7550, https://doi.org/10.3390/s22197550 [46] W. Huang et al., ‘DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique,’ in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 4093-4107, 2025, doi: 10.1109/JSTARS.2024.3525082 [47] Beslo, R., Mihai, H., Pedersen, M., Tilmann, F., Atherton, C. ‘Fibre Optic Sensing Security Architecture - A SUBMERSE White Paper’, GÉANT, https://zenodo.org/records/14998707