Over the past few decades, optical fibers have been widely deployed to implement various applications in high-speed long-distance telecommunication, optical imaging, ultrafast lasers, and optical sensors. Distributed optical fiber sensors characterized by spatially resolved measurements along a single continuous strand of optical fiber have undergone significant improvements in underlying technologies and application scenarios, representing the highest state of the art in optical sensing. This work is focused on a review of three types of distributed optical fiber sensors which are based on Rayleigh, Brillouin, and Raman scattering, and use various demodulation schemes, including optical time-domain reflectometry, optical frequency-domain reflectometry, and related schemes. Recent developments of various distributed optical fiber sensors to provide simultaneous measurements of multiple parameters are analyzed based on their sensing performance, revealing an inherent trade-off between performance parameters such as sensing range, spatial resolution, and sensing resolution. This review highlights the latest progress in distributed optical fiber sensors with an emphasis on energy applications such as energy infrastructure monitoring, power generation system monitoring, oil and gas pipeline monitoring, and geothermal process monitoring. This review aims to clarify challenges and limitations of distributed optical fiber sensors with the goal of providing a pathway to push the limits in distributed optical fiber sensing for practical applications.

The latest digital revolution involves the rise of smart devices composed of sensor hardware and artificial intelligence (AI) software for performing intelligent tasks. Smart sensors have become ubiquitous in our lives with varied applications ranging from voice-enabled home devices (Google Home, Alexa, etc.) to the Industrial Internet of Things (IIoT). This revolution has been fueled by 1) miniaturization of sensing hardware, 2) easy access to cloud and high-performance computing, 3) development of big data storage and analytics technologies, and 4) the latest breakthroughs in machine learning (ML) and AI technologies. The emergence of AI since 2012 and its major breakthroughs can be attributed to the research and development (R&D) in deep learning, a subfield of ML that uses biologically inspired neural networks to perform learning tasks. [1] The performance of conventional ML algorithms depends on the individual selection of specific features, while deep neural networks (DNN) automatically generate features as part of the learning process. Deep learningbased AI technologies are increasingly showing performance

This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) 1 Considering the input pump power limitation and the complexity of the receiver bandwidth reduction, a novel wavelength diversity technique is employed together with a BRL in a conventional BOTDR system. The proposed system Abstract-In this paper, a wavelength diversity technique is employed in a Brillouin optical time domain reflectometry (BOTDR) using a Brillouin ring laser (BRL) as a local oscillator. In the wavelength diversity technique, multiple wavelengths are injected into the sensing fiber, while the peak power of each wavelength is set below the nonlinear threshold level. This technique significantly maximizes the overall launch pump power, without activating the non-negligible nonlinear effects, which overcomes the limitation of the conventional BOTDR system. The BRL, which is simple and cost-effective, that can be used to reduce the receiver bandwidth in the order of few MHz. In addition, a passive depolarizer is used to reduce the polarization noise. The proposed system is validated experimentally over a 50 km sensing fiber with a 5 m spatial resolution. The experimental results demonstrate a signal-to-noise ratio improvement of 5.1 dB, which corresponds to 180% improvement compared to a conventional BOTDR system. Index Terms-Wavelength diversity, distributed fibre sensors, Brillouin scattering.

In this paper, a phase-sensitive optical time domain reflectometry (Φ-OTDR) based on Rayleigh enhanced optical fiber is proposed to improve the system signal-to-noise ratio (SNR). In the experiment, the vibration performance of the Rayleigh enhanced optical fiber is analyzed and compared with the standard single-mode telecom fiber. The results demonstrate that the measured SNRs of peaks in frequency spectra have been improved by 14 dB (at 1 kHz vibration frequency) at the end of the 2 km specialty sensing fiber. A wide range of vibration frequencies from 50 Hz to 15 kHz are demonstrated experimentally. The proposed method overcomes the inherent limitations of the conventional Φ-OTDR system and significantly enhances the vibration sensing performance.

A high sensitivity refractive index sensor based on a single mode-small diameter no core fiber structure is proposed. In this structure, a small diameter no core fiber (SDNCF) used as a sensor probe, was fusion spliced to the end face of a traditional single mode fiber (SMF) and the end face of the SDNCF was coated with a thin film of gold to provide reflective light. The influence of SDNCF diameter and length on the refractive index sensitivity of the sensor has been investigated by both simulations and experiments, where results show that the diameter of SDNCF has significant influence. However, SDNCF length has limited influence on the sensitivity. Experimental results show that a sensitivity of 327 nm/RIU (refractive index unit) has been achieved for refractive indices ranging from 1.33 to 1.38, which agrees well with the simulated results with a sensitivity of 349.5 nm/RIU at refractive indices ranging from 1.33 to 1.38.