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Advances in Fiber Optic Sensors: Innovations, Challenges and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 309

Special Issue Editor


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Guest Editor
Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore
Interests: fiber optic sensors and applications; sensor packaging; structural health monitoring; predictive maintenance; system integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fiber optic sensors (FOSs) have emerged as a critical technology for real-time, high-precision sensing across diverse fields, including structural health monitoring, biomedical diagnostics, environmental surveillance, and industrial automation. Their inherent advantages—such as high sensitivity, resistance to electromagnetic interference, and adaptability to extreme environments—have driven significant advancements in recent years.

This Special Issue aims to showcase state-of-the-art developments in fiber optic sensing, covering novel sensor designs, advanced interrogation techniques, and new applications in emerging industries. Topics of interest include distributed and point-based sensing, smart fiber sensors for IoT and AI-driven applications, multiplexing strategies for large-scale deployments, and the integration of fiber optics with photonic and nanomaterial-based technologies. Additionally, we encourage the submission of contributions addressing current challenges, such as cost reduction, miniaturization, and improved durability in harsh environments.

We welcome original research articles, reviews, and case studies from both academia and industry to provide a comprehensive outlook on the future of fiber optic sensing. Through this Special Issue, we aim to foster interdisciplinary collaboration and accelerate the translation of cutting-edge research into real-world applications.

Dr. Jianzhong Hao
Guest Editor

Manuscript Submission Information

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Keywords

  • fiber optic sensing technologies
  • distributed and multiplexed sensing
  • smart sensors and iot integration
  • optical signal processing and ai-enhanced analytics
  • structural health monitoring and industrial applications
  • biomedical and environmental sensing
  • advanced materials for fiber sensors
  • harsh environment sensing and durability

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Published Papers (1 paper)

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Research

24 pages, 4120 KiB  
Article
Real-Time Railway Hazard Detection Using Distributed Acoustic Sensing and Hybrid Ensemble Learning
by Yusuf Yürekli, Cevat Özarpa and İsa Avcı
Sensors 2025, 25(13), 3992; https://doi.org/10.3390/s25133992 - 26 Jun 2025
Viewed by 160
Abstract
Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences [...] Read more.
Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article
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