Applications of Multipoint Distributed Fiber Optic Sensors for Monitoring in Critical Systems

Preprint Applications of Multipoint Distributed Fiber Optic Sensors for Monitoring in Critical Systems J.A. Flores-Bravo, Jes´ us L´ azaro, Carlos Cuadrado, Aitzol Zuloaga, and Armando Astarloa Department of Electronic Technology, Faculty of Engineering University of the Basque Country (UPV/EHU) Bilbao, Spain Email: josea.floresbrav[email protected] Abstract—Multipoint Distributed Fiber Optic Sensors (DFOS) have emerged as a transformative technology for monitoring critical systems across diverse industries, including aerospace, energy, environmental and infrastructure sectors. These sensors leverage advanced optical techniques, such as Rayleigh, Brillouin, Raman scattering, and interferometry, to provide high-resolution real-time data over vast distances. When integrated with edge computing systems, DFOS achieves enhanced real-time data processing capabilities, allowing efficient monitoring of multiple parameters such as temperature, strain, pressure, and vibration simultaneously and at discrete locations. This integration is particularly beneficial for critical applications that require rapid response times and robust performance in extreme environments. This paper presents a comparative analysis of multipoint DFOS architectures across diverse critical systems. Introduces a cross-sector technical evaluation based on key performance metrics, including spatial resolution, latency, and energy consumption. Provides actionable insights for matching sensor technologies with application-specific requirements in aerospace, civil infrastructure, industrial automation, environmental monitoring, transport, and chemical process control. This work proposes a novel integration framework that combines DFOS with edge computing architectures to improve responsiveness, scalability, and energy efficiency. This vision sets the foundation for nextgeneration innovative sensing systems that support real-time decision making in critical environments. Index Terms—Distributed fiber optic sensors, critical systems, edge computing. I. INTRODUCTION The demand for advanced monitoring solutions capable of operating under challenging conditions has been driven by modern engineering systems and strict safety, efficiency, and sustainability requirements. Critical sectors such as aerospace, energy, environmental management, and infrastructure require innovative sensing technologies that ensure accurate, real-time, low-cost monitoring without compromising robustness and operational efficiency. In this context, DFOS have emerged as a revolutionary solution, offering unique capabilities to monitor physical parameters such as temperature [1], strain This work has been supported by ‘Ministerio de Asuntos Econ´ omicos y Transformaci´ on Digital’ and ‘Union Europea-NextGenerationEU’ through the C´ atedras Chip program, SOC4SENSING TSI-069100-2023-0004, by Basque Government within the fund for research groups of the Basque university system IT1440-22, S4C-II KK-2025/00013 and FIRMAR KK-2025/00073 and by ‘Ministerio de Ciencia, Innovaci´ on y Universidades’ (PID2024-157752OBI00 financiado por MICIU/AEI/10.13039/501100011033/FEDER, UE). [2], pressure [3], and vibration [4] [5], even over long distances and in extreme environments. One of the main advantages of DFOS is their intrinsically robust design, which provides superior resistance to corrosion, electromagnetic interference, and harsh environmental conditions. In addition, their low weight and compact size make them an ideal choice for aerospace and energy applications, where space and weight constraints are critical factors. However, integrating DFOS with edge computing technologies significantly amplifies their potential, enabling the decentralized, real-time processing of large volumes of data. This approach reduces latency, improves system efficiency, and facilitates critical decision-making in high-demand applications. This paper examines the key applications of multipoint DFOS for critical system monitoring, highlighting the impact of their integration with edge computing. This analysis seeks to highlight the transformative role of DFOS in the design of safe, efficient, and sustainable monitoring solutions for critical systems. II. PRINCIPLES OF MULTIPOINT DISTRIBUTED FIBER OPTIC SENSORS DFOS operate based on the principle of light propagation through optical fibers, where interactions between light and the surrounding environment allow for precise measurements of physical parameters. Within DFOS technology, two main categories are widely developed: A. Distributed Technologies Optical fiber operates as a distributed sensor, shifting away from the classical concept of a discrete measurement point to enable the acquisition of multiple spatially distributed measurements along the fiber length. Rayleigh scattering measures strain and temperature by analyzing changes in backscattered light intensity [6]. Distributed Acoustic Sensing (DAS) is an advanced optical fiber technique that uses Rayleigh backscattering to offer real-time monitoring and data collection across a wide range of applications [7]. Brillouin scattering enables the determination of strain and temperature through frequency shifts in backscattered light [8]. Raman scattering is primarily applied for temperature sensing by examining the intensity ratio of anti-Stokes and Stokes components [9, 10]. This document is the Manuscript version of a conference poster to be presented at the XL Conference on Design of Circuits and Integrated Systems, 26-28 November, 2025, Santander, Spain Preprint B. Quasi-distributed Technologies These systems are based on fiber grating sensors (longperiod fiber gratings) [11, 12], tilted fiber Bragg gratings (FBG) [13]), fiber interferometers [14, 15, 16], and surface plasmon resonance (SPR) [17, 18]. In these cases, the sensitive element is at the tip of the fiber, which makes the fiber sensitive only at these points, allowing many of these sensitive elements to be inscribed along the same fiber, thus obtaining a quasi-distributed system. Sensors can be designed to measure acceleration, vibration, pressure, inclination, displacement, or even corrosion, pH, and other parameters. III. DFOS IN CRITICAL SYSTEMS This section provides a detailed overview of the applications of distributed fiber optic sensors in critical systems. It highlights their role in the real-time monitoring of essential parameters such as vibration, impact, temperature, pressure, and deformation. These sensors are transforming multiple sectors, including civil infrastructure, aerospace, industrial automation, environmental monitoring, and transportation, by improving safety, performance, and operational efficiency. Table I presents a detailed analysis of their applications in these domains, focusing on specific requirements such as latency, robustness, and resolution. In civil infrastructure, monitoring bridges, tunnels, dams, and buildings is crucial to prevent structural failures. Applications such as vibration monitoring in bridges require latencies below 1 ms to assess dynamic loads and resonance effects in real-time [19]. Similarly, crack detection in tunnels and building settlement evaluation require high precision (0.1 mm - 0.5 mm) to identify early-stage deformations before compromising structural integrity [20, 21]. Robustness is essential in these systems, as sensors must withstand extreme temperature, humidity, and pressure conditions, particularly in outdoor or underground environments. Furthermore, in monitoring dams and reservoirs, sensors must operate reliably under high hydrostatic pressures (±0.1 MPa) to prevent leaks and structural failures [22]. Energy consumption is another critical factor, particularly for sensors deployed in remote locations. Low-power and autonomous solutions, integrated with edge computing, minimize maintenance requirements and optimize real-time decisionmaking without relying on external communication infrastructure. In industrial automation, sensors are crucial for predictive maintenance and process optimization. Applications such as vibration monitoring in machinery require latencies below 1 ms and resolutions as fine as 0.1 Hz, enabling early detection of mechanical failures before they lead to operational downtime [23]. Similarly, temperature control in manufacturing processes demands high accuracy (0.1°C) to ensure consistent product quality [24]. Robustness is vital in industrial environments, where sensors are exposed to high temperatures, pressure variations, and corrosive chemicals. For example, in pressure monitoring in industrial pipelines, where pressure can reach up to 100 MPa, sensors must be highly durable and maintain long-term accuracy [25]. Energy consumption is a key concern in smart factories and connected industrial environments. The integration of sensors with edge computing reduces data transmission and power usage while enhancing monitoring efficiency without compromising productivity. Environmental monitoring greatly benefits from fiber optic sensors, particularly in applications such as air quality surveillance, groundwater level monitoring, and soil moisture detection. In these cases, latency is less critical (≤0.10 ms), but measurement accuracy and long-term stability are paramount. For example, air quality monitoring requires measurements of concentration levels up to 500 ppm with a resolution of 1 ppm [26]. In contrast, monitoring groundwater level requires a resolution of 0.1 cm to detect minor fluctuations [27]. Sensors for environmental applications must be highly, robust as they operate under harsh conditions, including humidity, solar radiation, and temperature fluctuations. In soil moisture detection for agricultural applications, sensors must measure values between 0% and 100% moisture content with high precision to ensure efficient and sustainable irrigation practices. Energy consumption is a significant consideration, as these sensors are often deployed in remote or rural areas, where frequent maintenance is impractical. In autonomous vehicles, real-time monitoring of suspension systems, battery temperature, chassis integrity, and wheel positioning is crucial to ensuring safe and efficient operation. In these applications, latency must be exceptionally low (<1 ms to 10 ms) to respond instantly to changes in vehicle dynamics [28]. Robustness is critical, as sensors must withstand high vibrations, extreme temperature variations (-40°C to 85°C), and exposure to dust or moisture. For example, precise control with a resolution of 0.1°C is essential in battery temperature monitoring, as overheating can compromise system safety [29]. Energy consumption is a crucial challenge in this sector, as autonomous vehicles rely on limited energy resources. The integration of sensors with edge computing minimizes power demand by processing data locally, optimizing resource management, and extending battery life. In the chemical industry, monitoring pressure, temperature, mechanical strain, and flow rate in pipelines and reactors ensures safety and operational efficiency. Applications such as pressure monitoring in chemical reactors require latencies below 1 ms and pressure ranges up to 100 MPa, enabling immediate responses to critical process fluctuations [30]. Robustness is key, as sensors must operate in highly corrosive environments with extreme temperatures and pressures. For example, long-term stability is necessary to maintain process quality in temperature monitoring in distillation columns, where measurement ranges extend from -50°C to 300°C. Energy consumption is another challenge in chemical plants. Fiber optic sensors combined with edge computing can reduce the need for constant data transmission, optimize energy resources, and enhance operational safety. In aerospace, ensuring safety, operational efficiency, and optimal performance necessitates deploying advanced monitoring and diagnostic technologies. Many aerospace applica- Preprint tions demand ultra-low latency to ensure immediate response to critical conditions. For instance, rocket engine vibration monitoring requires a latency of less than 1 ms to 5 ms to detect resonances and prevent catastrophic failures [31]. Similarly, impact detection due to micrometeoroids must operate with sub-millisecond latency to identify orbital damage and inform corrective actions before system failure [32]. Other applications, such as satellite thermal control and solar panel deformation monitoring, have relatively higher latency tolerances (10 ms - 50 ms) due to the slower nature of thermal dynamics and mechanical deformations. Thermal control in cryogenic systems requires sensors capable of accurately measuring temperatures ranging from -200°C to 700°C, essential for rocket propulsion systems and fuel storage [33]. Likewise, wing fatigue monitoring involves measuring strain up to ±10,000 µε in aircraft wings, ensuring early detection of microcracks and material degradation over time [34]. FBG sensor technology significantly reduces energy demands compared to traditional electronic sensors by leveraging optical fiber networks requiring minimal data acquisition and transmission power. IV. INTEGRATION OF DFOS WITH EDGE COMPUTING ARCHITECTURES The growing demand for high-resolution real-time monitoring in critical systems presents new challenges related to data volume, processing latency, and energy consumption. DFOS capable of capturing rich spatio-temporal data across extended infrastructures, require efficient data management strategies to avoid bottlenecks in centralized processing architectures [45]. To address this, integrating DFOS with edge computing architectures emerges as a transformative approach that significantly enhances system responsiveness, scalability, and energy efficiency. Traditional DFOS systems rely on centralized data acquisition and processing units, which can introduce latency, increase communication overhead, and limit scalability, especially in geographically distributed or remote deployments. By shifting data processing closer to the sensing points through edge computing nodes, DFOS systems: •Reduce latency by performing real-time signal processing and anomaly detection at the network edge. •Lower data transmission requirements by filtering, compressing, or summarizing data before sending it to the cloud or control centers. •Improve energy efficiency by minimizing the need for high-bandwidth, continuous data streaming. •Enhance scalability by supporting distributed architectures with multiple independent or semi-autonomous sensing nodes. Fig. 1 Shows the conceptual diagram of the proposed architecture of DFOS integrated with edge computing for realtime monitoring [29]. The architecture is organized into three layers: •Sensing Layer, which comprises multipoint DFOS elements (Rayleigh, Brillouin, FBG, etc.) distributed along Fig. 1. Edge computing DFOS Architecture for Real-Time Monitoring in Critical Systems. optical fibers, captures real-time data on parameters such as strain, temperature, pressure, and vibration across critical infrastructures. •Located physically close to the sensing layer, this layer integrates interrogation with tunable lasers and photodetectors for signal acquisition. System-on-Chip (SoC) based processing units perform signal spectral analysis, local anomaly detection using thresholding or machine learning algorithms. This layer ensures low-latency, energy-efficient processing, significantly reducing data transmission loads. •Cloud Integration Layer. This layer provides long-term data storage, cross-system analytics, and remote visualization. It receives processed events or summarized datasets from multiple edge nodes, enabling fleetwide monitoring, predictive maintenance, and strategic decision-making. Fig. 2. DFOS Interrogator proposed in [42] Fig. 2 Shows the functional diagram of the DFOS interrogation Unit (IU), designed for fast and precise monitoring of FBG sensors. The IU supports up to six redundant optical channels, enabling the interrogation of up to 120 sensors with protection against single-point fiber failures. Its architecture balances high-speed performance and power efficiency, integrating key components such as a power supply, control imple- Preprint TABLE I APPLICATIONS IN CRITICAL SYSTEMS. Critical System Application Latency Range Resolution Description Ref. Civil Infrastructure Vibration monitoring in bridges <1 ms 0 Hz – 1 kHz, 0.1 Hz Real-time evaluation of dynamic loads and resonances in bridge structures. [19] Crack detection in tunnels <10 ms 0.1 mm – 100 mm 0.1 mm Identification of deformations and cracks in tunnels caused by settlements or tectonic stresses. [20] Monitoring of dams and reservoirs <10 ms 0 MPa – 10 MPa ±0.1 MPa Real-time pressure control and leakage detection to prevent structural failures. [21] Settlement evaluation in buildings <1 ms up to 10 cm 0.5 mm Monitoring of differential displacements and deformations in critical urban structures. [22] Industrial Automation Vibration monitoring in machinery <1 ms 0 Hz – 10 kHz 0.1 Hz Real-time monitoring of machinery vibrations for predictive maintenance. [23] Temperature control in manufacturing processes <10 ms -200°C to 500°C, 0.1°C Accurate temperature monitoring in production lines to ensure consistent product quality. [24] Pressure monitoring in pipelines <10 ms 0 MPa to 100 MPa ±0.1 MPa Monitoring pressure in pipelines and tanks for leak detection and system integrity. [25] Strain monitoring in structural components <1 ms up to 1000µm 0.5 µm Detecting structural strain in heavy machinery and factory infrastructure. [35] Environmental Monitoring Air quality monitoring <10 ms 0 - 500 ppm 1 ppm Continuous air quality monitoring in urban and industrial environments to detect pollutants. [26] Groundwater level monitoring <1 ms 0 to 100 m 0.1 cm Monitoring groundwater levels in wells and underground reservoirs. [27] Soil moisture detection <10 ms 0 - 100 m 0.1 cm Measuring soil moisture content for agricultural and environmental research. [36] Structural health monitoring of dams <10 ms up to 1000 µm 0.5 µm Detecting strain and structural deformations in dams and levees to prevent failure. [21] Transport (Autonomous Vehicles) Vibration monitoring for vehicle suspension <1 ms 0 Hz - 1000 Hz 1 Hz Monitoring vibrations in the vehicle suspension system for safety and performance analysis. [28] Structural integrity monitoring of the chassis <10 ms up to 1000 µm 0.5 µm Detecting structural deformations or cracks in the vehicle’s chassis during operation. [37] Temperature monitoring of battery systems <1 ms -40°C to 85°C 0.1°C Monitoring battery temperature in electric autonomous vehicles to prevent overheating or failure. [29] Wheel position and movement monitoring <10 ms up to 500 mm 0.1 mm Detecting movement and position of the wheels in autonomous vehicles for precise navigation. [38] Chemical Process Control Pressure monitoring in reactors <1 ms 0 MPa - 100 MPa 0.1 MPa Continuous pressure monitoring in chemical reactors to ensure safety and optimal process conditions. [30] Temperature monitoring in distillation columns <10 ms -50°C to 300°C 0.1°C Monitoring temperature in distillation processes for precise control and quality of the output. [39] Strain monitoring of reactor vessels <1 ms up to 2000 µm 0.5 µm Detecting strain or deformations in reactor vessels to prevent structural failure. [40] Flow rate monitoring in pipelines <10 ms 0 to 100 L/min 0.1 L/min Monitoring flow rate in chemical pipelines to optimize production and ensure smooth operation. [41] Aeroespace Wing Fatigue Monitoring 1 ms - 10 ms ±10,000 µε ±10,000 µε Real-time detection and analysis of microcracks and structural fatigue in aircraft wings. [34] Thermal Control in Cryogenic Systems 5 ms - 50 ms -200 °C to 700 °C 0.1 °C Temperature monitoring in cryogenic rocket engine systems and propellant storage. [33] Rocket Engine Vibration Monitoring <1ms - 5 ms 1 Hz - 100 kHz 1 Hz Monitoring vibrations and resonances in engines to prevent catastrophic failures. [31] Satellite Thermal Monitoring 10ms - 50 ms -150 °C to 150 °C ±0.5 °C Control of temperature distribution in satellites to prevent overheating. [42] Fuselage Impact Detection <10 ms 1 g – 100 g <1 g Detection of impacts on aircraft fuselages for immediate damage assessment. [43] Solar Panel Monitoring 10 ms - 50 ms 0.1 m – 10 km m to km Monitoring deformations in satellite solar panels to prevent loss of functionality. [44] Impact Detection by Micrometeoroids <1 ms 1 g – 100 g <1 g Detection of micrometeoroid impacts on satellites and spacecraft, critical for assessing orbital damage. [32] Preprint mented FPGA, single sideband (SSB) interface, photodiodes, acquisition electronics, tunable laser, and an internal optical network, ensuring reliable, low-latency, and energy-efficient operation [42]. V. CHALLENGES AND FUTURE PERSPECTIVES DFOS have exceptional potential for monitoring critical systems; however, several technical challenges must be addressed to facilitate broader adoption and integration. The main challenges: •Power: Minimizing the power demand of DFOS systems is a key priority, especially for deployment in resource-constrained environments such as aerospace or remote monitoring applications. Advances in energyefficient light sources and the development of optimized signal processing algorithms are essential to mitigate this limitation. •Robustness: DFOS requires improvements in durability to withstand mechanical shock, severe thermal fluctuations, and high-vibration scenarios. Innovations in advanced fiber materials and encapsulation techniques will be necessary to improve their long-term reliability under such conditions. •Miniaturization: Reducing size and weight remains a major challenge, particularly in applications where space and mass constraints are critical, such as aerospace systems. Research should focus on miniaturizing interrogation units and associated ancillary components, to ensure that these reductions do not compromise accuracy or detection performance. •Cost: The high production and deployment costs currently limit large-scale adoption. Developing cost-effective manufacturing methods and alternative materials is essential. •Spatial Resolution: Enhancing spatial resolution to detect finer changes along the fiber is critical to improving the accuracy of monitoring applications. Future advancements in DFOS technology will likely emerge from the integration of advanced materials, sophisticated signal processing algorithms, and miniaturized SoC systems. Additionally, incorporating machine learning and artificial intelligence into data analysis workflows holds great promise in improving the functionality and efficiency of DFOS systems while reducing operational costs. CONCLUSIONS DFOS represent an advanced and highly versatile technological solution for monitoring critical systems. Their capability to deliver precise, real-time measurements, even in extreme environments, makes them indispensable tools for ensuring safety and optimizing operational efficiency across various industries. Integrating DFOS with cutting-edge technologies, such as edge computing, significantly enhances their performance. By processing large volumes of data locally, edge computing improves responsiveness and enables immediate decision-making in applications where low latency is a critical factor. This synergy paves the way for more agile, robust, and adaptable solutions in critical sectors. 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