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Review

Scientometric Analysis and Research Trends in Optical Fiber Grating Sensors: A Review

by
Yiqiang Deng
1,
Wen Ren
2 and
Xiaoyan Wang
2,*
1
Network Technology Center, Sanming University, Sanming 365004, China
2
School of Mechanical and Electrical Engineering, Sanming University, Sanming 365004, China
*
Author to whom correspondence should be addressed.
Photonics 2025, 12(4), 349; https://doi.org/10.3390/photonics12040349
Submission received: 5 March 2025 / Revised: 29 March 2025 / Accepted: 31 March 2025 / Published: 8 April 2025
(This article belongs to the Special Issue Emerging Trends in Optical Fiber Sensors and Sensing Techniques)
Browse Figures
Review Reports Versions Notes

Abstract

:
The increasing demand for high-precision, real-time sensing in various fields has spurred the development of optical fiber grating sensors (OFGSs). This study reviews the research field of OFGSs, exploring their historical development, current trends, and future opportunities through scientometric analysis utilizing CiteSpace. The research landscape has grown exponentially since the early studies on fiber Bragg gratings and long-period fiber gratings in the 1990s. Bibliometric data reveal that engineering, optics, and instrumentation dominate OFGS research, with emerging interdisciplinary applications in environmental, biological, and medical sensing. Key contributors have advanced OFGSs through femtosecond laser inscription, novel materials, and intelligent system integration, as reflected in co-citation and keywords analyses. Trends such as AI-driven optimization, surface plasmon resonance, and 3D printing signal shift toward adaptive, multifunctional sensing systems capable of addressing diverse challenges. This review also maps the evolution of OFGS research, transitioning from foundational strain and temperature sensing to sophisticated systems for structural health monitoring, biomedical diagnostics, and robotics. Despite global disruptions, the field’s recovery highlights its critical role in advancing sensing technologies. By combining thematic insights from co-citation and keyword analyses, this study identifies both established directions and transformative opportunities, providing a holistic understanding of OFGS research and its trajectory.

1. Introduction

Discovered by Hill et al. in 1978 [1], fiber Bragg grating (FBG) technology has revolutionized the field of optical sensing. Originally developed for telecommunications, FBGs later found applications in various industries, owing to their remarkable sensitivity to strain and temperature changes. These industries include aerospace [2,3], civil engineering [4,5,6,7,8,9,10], and biological sensing [11]. Their unique capability to reflect specific wavelengths and notable advantages, including compact size, immunity to electromagnetic interference, remote sensing capability, and high sensitivity, allow for precise measurements in diverse environments.
To systematically analyze fiber grating technologies, we classify them based on their key optical characteristics, including periodicity, structural modifications, sensing principles, and fiber integration. This classification helps in understanding their diverse functionalities and applications.
(1)
Periodicity-Based Classification
The grating period, which defines the spacing between refractive index modulations within the fiber, significantly influences light propagation and interaction. Based on this, fiber gratings can be categorized as follows.
  • Fiber Bragg gratings (FBGs): These gratings operate by reflecting specific wavelengths of light based on Bragg’s law, with the reflected wavelength determined by the grating period. Their short-period structure ensures high precision in sensing applications, particularly for measuring strain [12,13], temperature [14,15], and pressure [16,17]. FBGs are widely applied in distributed sensing networks for structural health monitoring, aerospace, and energy sectors.
  • Long-period fiber gratings (LPFGs): Unlike FBGs, LPFGs have a longer grating period, which couples light from the core into cladding modes, leading to enhanced sensitivity to external refractive index changes. This makes LPFGs particularly suitable for applications in biochemical sensing [18,19] and environmental monitoring [20,21]. Unlike FBGs, which reflect specific wavelengths, LPFGs transmit light and modulate specific wavelengths.
(2)
Structural Enhancements
Various structural modifications enhance the functionality of fiber gratings, making them suitable for specialized applications.
  • Phase-shift fiber gratings (PS-FGs): These introduce a phase shift at a specific point within the grating, resulting in a narrowband reflection peak. By interrupting the uniformity of refractive index modulation, PS-FGs are ideal for high-resolution optical filtering and ultra-narrow bandwidth sensor applications [22,23,24].
  • Tilted fiber Bragg gratings (TFBGs): The grating fringes in TFBGs are slanted relative to the fiber axis, enabling the coupling of core modes into cladding modes. This design enhances sensitivity to transverse strain, bending, and surrounding refractive index changes, making TFBGs useful for polarization-sensitive [25,26,27] and multi-parameter sensing [28,29].
  • Chirped fiber gratings: These gratings feature a varying period along their length, allowing them to reflect a range of wavelengths. This property makes them suitable for dispersion compensation in telecommunications and broadband sensing [30,31] applications.
  • D-Shaped fiber gratings: These are created by polishing one side of the fiber, exposing the core and enabling the evanescent field to interact with the surrounding environment. This enhances sensitivity to surface perturbations, making them particularly effective for biochemical [32,33] and environmental sensing [34,35]. However, the polishing process reduces the mechanical strength of the fiber.
(3)
Advanced Sensing Mechanisms
Beyond structure and periodicity, advanced sensing principles expand the capabilities of fiber gratings.
  • Surface plasmon resonance (SPR)-based gratings: These sensors incorporate a metallic coating, allowing interaction between the evanescent field and free electrons in the metal. When the resonance condition is met, a sharp drop in transmitted light intensity occurs, making SPR highly sensitive to refractive index changes. This technology is widely used in chemical [36,37] and biological sensing [38,39].
  • Vernier-effect fiber gratings: This effect is achieved by combining two gratings with slightly different periods, resulting in an interference pattern that amplifies sensor sensitivity. The Vernier effect is particularly valuable in applications requiring ultra-high precision, such as strain [40,41], temperature [42,43], or pressure sensing [44].
  • Polarization-sensitive fiber gratings: Gratings such as TFBGs interact differently with light polarization states. By exploiting polarization-dependent loss or birefringence, these sensors can detect multiple parameters simultaneously, including stress and temperature [45,46].They are widely used in applications where multi-axis strain or anisotropic environmental changes need to be monitored.
(4)
Integration with Optical Fiber Types
Fiber gratings have also been integrated with various optical fiber types to enhance performance in specialized applications:
  • Single-mode fiber (SMF) gratings: The integration of FBGs with SMF remains the standard for reliable strain and temperature sensing due to their low-loss transmission characteristics.
  • Few-mode fiber (FMF) gratings: FMF-based FBGs increase sensing capacity by supporting multiple propagation modes, allowing for advanced multiplexing while maintaining manageable complexity.
  • Multi-core fiber (MCF) gratings: MCFs combined with FBGs enable distributed sensing over multiple cores, improving spatial resolution and multi-parameter monitoring capabilities.
  • Photonic crystal fiber (PCF) gratings: These gratings leverage a microstructured arrangement of air holes to enhance sensitivity by allowing for tailored light propagation, leading to improved detection capabilities.
  • D-shaped fiber gratings: As previously mentioned, D-shaped fibers enable evanescent field interaction, making them highly sensitive to surface changes and suitable for biochemical sensing applications.
Despite these advancements, challenges remain in the deployment of fiber grating sensors. For instance, FBGs inscribed on SMFs may experience cross-sensitivity between temperature and strain, complicating data interpretation. However, this limitation can be addressed by using more advanced structures such as polarization-maintaining fibers [45,46,47] or PCFs [48,49], which offer greater control over polarization and light propagation. Nonetheless, these technologies bring their own challenges, including increased fabrication complexity and higher costs in the case of PCFs, or potential mode-coupling issues in FMFs and MCFs that may affect signal clarity.
While many review articles exist in the field, most focus on specific applications or sensor types, and a comprehensive overview of fiber grating sensors is lacking. This review addresses the gap by using CiteSpace, a scientometric analysis tool that utilizes bibliometric data, to systematically explore the literature on fiber grating sensors. Scientometric tools like CiteSpace analyze research trends, keyword evolution, and frontier dynamics, providing a clear view of how the field has evolved. By leveraging big data and statistical methods, CiteSpace maps key developments and research clusters, helping to identify trends and future directions [50,51].
Therefore, by utilizing CiteSpace (Version 6.4.R2), we conducted a comprehensive scientometric review of fiber grating sensor research. By analyzing all relevant documents, we mapped the scientific knowledge and visualized the field’s development. This study aims to (1) reveal global research patterns in fiber grating sensors, (2) provide a detailed overview of the field’s scientific evolution, (3) identify current trends and key research areas, and (4) offer actionable insights to shape future strategies for advancing fiber grating sensor technology.

2. Data and Methods

2.1. Data Collection and Processing

The protocol for this scoping review has been registered on the Open Science Framework (OSF) to ensure transparency and reproducibility of the research methods. The detailed protocol outlines the objectives, eligibility criteria, information sources, search strategy, data extraction methods, and analysis plan. The registration information is as follows:
  • Registration Platform: Open Science Framework (OSF);
  • Registration Date: 4 March 2025;
  • Registration Number: 10.17605/OSF.IO/M3C9D;
  • Protocol Access: https://osf.io/m3c9d/.
This registration ensures that the methodology of the review is publicly available and can be referenced by other researchers. Any updates or amendments to the protocol will be documented on the OSF platform to maintain the integrity and transparency of the research process.

2.1.1. Data Source and Selection

The bibliometric data for this study were sourced from the Web of Science (WoS) core collection, and the most recent search date was 5 January 2025. While other databases, such as Scopus, also archive academic publications, WoS was chosen for three main reasons. First, it is widely recognized for indexing high-quality, peer-reviewed research. Second, it provides detailed bibliometric metadata, making it highly compatible with analysis tools like CiteSpace. Third, WoS contains an extensive collection of publications related to fiber grating sensors, enabling a comprehensive examination of the field’s structure and trends.
To maximize the inclusion of relevant studies, the search query used was as follows: Topic = (“fiber grating” OR “fiber Bragg grating”) AND Topic = (“*sensor” OR “*sensing”). The wildcard “*” was included to allow for truncation searches, capturing variations of the keywords. A broad-term search strategy was employed to ensure comprehensive coverage of all potentially relevant studies in the database. Irrelevant studies were subsequently excluded through manual screening. For statistical analysis, we included as many relevant studies as possible. This approach ensures that even if some relevant studies were missed due to mismatched keywords, their absence was mitigated by the inclusion of other similar studies from the database. The search results were restricted to documents written in English.

2.1.2. Data Coverage and Selection Criteria

The search was limited to publications from 1992 to 2024, as the first relevant entry in WoS appeared in 1992. This yielded a dataset of 9216 documents, comprising 286 review articles and 8920 research articles. To ensure the dataset’s suitability for scientometric analysis, only documents classified as research or review articles containing essential bibliometric information—such as author, title, source, abstract, and references—were included. All relevant data fields, including Author, Title, Source, Abstract, Keywords, Addresses, Cited References, Usage, and Funding, were included in the analysis. All data were independently assessed for usability by two researchers, compared, and then confirmed by a third researcher. Figure 1 shows the flow diagram of study selection process based on PRISMA 2020 Guidelines.

2.2. Scientometrics Analysis Method

Scientometric tools are essential for analyzing and visualizing trends in scientific literature. Among the most widely used tools are CiteSpace, VOSviewer, and Biblioshiny, each offering distinct features for evaluating research landscapes. For this study, CiteSpace was selected as the primary analytical tool.
Developed by Dr. Chaomei Chen at Drexel University, CiteSpace is a robust software package for scientometric and bibliometric analysis. It enables the visualization of co-citation networks, the clustering of research topics, and the exploration of trends over time [50,51,52,53,54]. One of its key features is co-citation network analysis, which highlights influential studies and their relationships within a research domain. Additionally, CiteSpace employs clustering algorithms, such as the Log-Likelihood Ratio, to group related studies, offering valuable insights into the structure of the research field. Moreover, its capability to detect citation bursts—identifying papers with sudden spikes in citations—makes it an effective tool for uncovering research frontiers and emerging trends.
By leveraging these functionalities, CiteSpace offers a dynamic perspective on the development and evolution of research topics, enabling researchers to gain valuable insights and identify future directions for their work. In this research, we used CiteSpace to perform scientometric analysis on the extracted data. The data were cleaned and normalized before analysis. We generated keyword co-occurrence networks, co-citation networks, and timeline views to identify research hotspots and trends. The results were summarized in tables and visualized using network graphs and timelines.

3. Scientometric Analysis

3.1. Statistical Characteristics

Although fiber grating technology was first discovered in 1978, it was only after W.W. Morey thoroughly investigated the sensing characteristics of FBGs in 1990 [55], followed by A.M. Vengsarkar’s study on LPFGs in 1996 [56], that fiber grating-based sensing technology began to attract significant scientific attention. These pivotal works marked the onset of a rapid development phase in the field.
Figure 2 illustrates the publication trends for OFGSs from 1992 to 2024, showing the annual number of publication records (blue bars) and their corresponding citations (red diamonds). The number of publications exhibits an exponential increase, particularly after 2000, underscoring the growing importance and continuous advancements in this research area. A curve fitting of the publication records reveals a strong polynomial correlation (R2 = 0.9884), reflecting the consistency of this growth trajectory. Similarly, citation counts have risen significantly over time, highlighting the expanding influence and recognition of fiber optic grating-based sensor research.
The decrease in publications during 2020 coincides with the global disruption caused by the COVID-19 pandemic, which may have affected research activities due to factors such as funding reallocation, laboratory closures, and project delays. While some studies suggest that these disruptions led to a temporary decline in scientific output across multiple disciplines, comprehensive quantitative analyses specifically addressing non-pandemic-related research fields remain limited. Similarly, the dip observed in 2014 may be associated with geopolitical tensions and economic constraints, which could have influenced research funding and international collaborations. Further studies are needed to comprehensively assess the long-term impact of such global events on research productivity. Despite these occasional setbacks, the field has demonstrated remarkable resilience. Both publication records and citation counts have rebounded in recent years, surpassing 700 annual publications and over 25,000 citations. These temporary declines underscore the sensitivity of academic research to global crises and economic fluctuations. Nevertheless, the field’s strong recovery reflects its foundational role in advancing sensing technologies.

3.2. Subject Structure Analysis and Influential Sources

Figure 3 illustrates the top 30 main research areas related to OFGS studies in WoS from 1992 to 2024, categorized by their respective proportions. The dominant fields are Engineering (26%), Optics (22%), and Instruments and Instrumentation (15%), which collectively account for over 60% of the total studies, reflecting the technological focus of OFGS research. These fields emphasize the role of OFGSs in advancing sensing technologies, with significant applications in engineering and optical systems, particularly for precise measurements and instrumentation. Other fields, such as Physics (12%) and Chemistry (6%), demonstrate the interdisciplinary nature of OFGSs, highlighting their use in exploring fundamental physical phenomena and chemical sensing. Additional fields, such as Telecommunications and Materials Science, emphasize the integration of OFGSs in communication systems and novel materials research. Emerging fields like Environmental Sciences, Biotechnology, and Applied Microbiology suggest a growing application of OFGSs in environmental monitoring, biological sensing, and medical diagnostics. Furthermore, the fields of Nanoscience and Robotics point to the expanding potential of miniaturized and automated OFGS technologies.
The dominance of Engineering and Optics underscores the technical backbone of OFGS development, focusing on fiber design, grating fabrication, and optical signal processing. The diversity of fields reflects OFGS’s versatility, addressing challenges across disciplines such as structural health monitoring, biomedical diagnostics, and environmental sensing. This distribution highlights both the maturity and the expanding frontiers of OFGS research in meeting contemporary technological demands.
Figure 4 presents the top 30 journals that have published research on OFGSs, ranked by the proportion of total publications in this field. Leading journals such as IEEE Sensors Journal (8.92%), Journal of Lightwave Technology (7.44%), and IEEE Photonics Technology Letters (5.66%) collectively account for over 20% of the total publications. These journals emphasize advanced sensing technologies, photonics, and lightwave communication, which are central to OFGS research. Specialized journals like Sensors and Actuators A: Physical (3.80%) and Measurement Science and Technology (2.04%) reflect the practical applications of OFGSs in physical sensing and precise measurement capabilities. Journals like Chinese Optics Letters highlight the growing contributions from regions such as Asia, demonstrating global collaboration in the field.
The concentration of publications in a few core journals underscores the technical focus and maturity of the OFGS field, while the diversity of journal topics and geographic regions illustrates its wide-ranging applications and the global nature of research contributions. These span sensing, measurement, materials science, and photonic innovations, emphasizing the evolving impact of OFGS in both foundational and applied sciences.

3.3. Research Contribution Analysis (Core Authors)

Table 1 presents the top 10 researchers with the highest publication counts in the OFGS field, while Table 2 lists the top 20 references with the strongest citation bursts. From these, we can identify the leading researchers and trace the historical evolution of OFGSs. The foundational theories of fiber grating sensors were established in the 1990s. Kersey et al. (1997) [57] and Rao et al. (1997) [58] are seminal contributors whose works laid the theoretical groundwork for the strain- and temperature-sensing applications of FBGs. Additionally, the work of Vengsarkar (1996) [56] laid the foundation for the application of long-period gratings (LPGs) in chemical sensing. These studies provided a solid understanding of how fiber gratings interact with external perturbations.
In the 2000s, Mihailov (2012) [59] expanded the capabilities of fiber gratings through femtosecond laser inscription, enabling precise control over grating structures and enhancing sensing reliability. Furthermore, Albert et al. (2013) [60] and Caucheteur et al. (2015) [61] significantly contributed to the integration of novel materials and advanced fabrication techniques, making multifunctional sensors a reality.
The 2020s have witnessed the convergence of traditional optical sensing principles with emerging technologies, such as machine learning and photonic crystal fibers. For example, Min et al. (2021) [62] achieved unprecedented sensitivity by integrating intelligent algorithms and advanced fiber architectures. These advancements mark a shift toward adaptive, intelligent sensing systems capable of addressing complex real-world scenarios.
In CiteSpace, several clustering algorithms are available for analyzing research trends and generating cluster labels, including Latent Semantic Indexing (LSI), Log-Likelihood Ratio (LLR), Mutual Information (MI), and User-Specified Ratio (USR). Each algorithm employs a distinct approach to clustering, which can lead to different results. For instance, LSI focuses on semantic relationships within text data, making it suitable for exploring thematic evolution, while MI emphasizes strong statistical dependencies between keywords, particularly for low-frequency terms. USR allows for user-defined rules, offering flexibility but requiring deep domain knowledge. LLR, on the other hand, identifies the most representative keywords based on statistical significance, resulting in highly interpretable cluster labels. In this study, our primary focus was to identify and analyze research themes and generate interpretable cluster labels. Therefore, we selected the LLR algorithm, as it is well suited for highlighting core themes within clusters and producing meaningful labels. Additionally, during the analysis, we experimented with other algorithms (e.g., LSI and MI) and found that the resulting clusters had significantly lower interpretability. Considering these factors, we consistently applied the LLR algorithm throughout this study to ensure the reliability and clarity of the clustering results and to avoid potential biases. On the other hand, in CiteSpace analysis, reducing the time slice length and increasing the g-index can expand the data volume and computational complexity, thereby prolonging the duration of the clustering process. However, through our practical comparative experiments, it was observed that the clustering results showed no significant differences. This reflects the robustness of the clustering results, as variations in parameter settings did not substantially alter the outcome.
Figure 5 presents the author co-citation analysis using “Author” and “Reference” as node types in CiteSpace clustering analysis. The modularity Q = 0.9079 and weighted mean silhouette S = 0.9593 demonstrates that the clustering structure is well defined and exhibits high internal consistency. By filtering out small clusters, twelve key clusters remain, highlighting their primary contributors. Combining information from the tables above provides a comprehensive perspective on the research content and directions in OFGSs. This integrated analysis reveals distinct research focus and their evolution beyond the foundational work on FBGs and LPGs.
Clusters #0, #2, #4, #5, #10, #11, and #14, represent foundational research in OFG technology. In Cluster #2, pioneers such as Albert J. [60] have developed advanced materials that enhance grating performance. His work, with a citation count of 3021 and a burst strength of 56.77, has contributed to innovations like magnetic fluids, which are used in Cluster #0 (magnetic field sensors). Albert J.’s research has been pivotal in understanding how materials influence the performance of optical fiber sensors, laying the groundwork for integrating novel materials with fiber grating technologies to improve sensor sensitivity and robustness in complex environments. Innovations in tilted fiber Bragg gratings (Cluster #14) and polarization-sensitive gratings (Cluster #5) continue to advance detection sensitivity and enable new sensing modalities. Researchers such as Min R. [62] have explored the integration of structural and polarization principles, achieving a burst strength of 25.4. These advances are crucial for high-precision vibration sensing and for improving sensor accuracy under various conditions. These techniques have transformative applications in refractive index sensing and biochemical detection, marking a significant leap in the capabilities of fiber grating sensors [63].
Clusters #3, #1, #9, #12, and #8 focus on real-world sensing applications and performance optimizations. Research in Cluster #1 explores chemical functionalization techniques for detecting specific analytes, driving advances in chemical sensing technologies using optical fiber gratings. Lo Presti D. [64], appearing in Cluster #3, investigated sensor performance under varying conditions. His work, with a burst strength of 32.98, bridges fundamental material research with specific sensor applications, particularly in sensor characterization. His research continues to influence the development of high-performance sensor systems. Researchers like Broadway C. [65], appearing in Cluster #12, and Min R. [62] in Cluster #8, have optimized sensors for simultaneous strain, temperature, and vibration monitoring. Their contributions have improved the reliability and precision of these sensors, with Broadway C. achieving a burst strength of 20.74, and Min R. achieving a burst value of 25.4. These sensors are essential in structural health monitoring, particularly in detecting dynamic changes and mechanical stresses in critical infrastructure. The convergence of their work, particularly in material science and sensor precision, is a significant development in improving the accuracy of thermal and strain measurements. Cluster #7 highlights the increasing role of artificial intelligence (AI) in OFGS technology. AI applications are built on foundational advancements in material research and grating design from other clusters. By applying machine learning techniques, researchers can refine the calibration and optimization of sensors, ultimately enhancing their performance across a wide range of applications. Additionally, AI has the potential to significantly improve real-time data processing and decision-making capabilities in sensor systems.
The cluster analysis in Figure 5 not only highlights key contributors and their groundbreaking work, but also demonstrates how research across various clusters is interconnected. By integrating advanced materials, novel grating designs, and cutting-edge applications like AI, OFGS technology continues to evolve rapidly. This interconnected approach not only enhances sensor capabilities but also ensures that the development of each research area builds upon earlier breakthroughs, forming a strong foundation for future innovations.

3.4. Analysis of the History and the Current Research Hotspots

3.4.1. Keyword Co-Occurrence Analysis

To gain deeper insights into the thematic focus and evolution within the research field of OFGS, a keyword co-occurrence analysis was conducted. This method complements the author co-citation analysis by identifying specific research topics, highlighting evolving trends, and mapping out research clusters based on co-occurring terms. Compared to the foundational research focus observed in the author co-citation clustering, the keywords analysis provides a more application-oriented perspective.
Figure 6 shows the keyword co-occurrence network, where nodes represent keywords and links indicate their co-occurrence within the same body of literature. The network comprises twelve distinct clusters, with modularity Q = 0.5621 and silhouette score S = 0.8433, indicating well-defined and cohesive clusters.
The clustering results in Figure 6 demonstrate significant consistency with the findings from the “author co-citation clustering analysis” in Figure 5, reinforcing the validity of the identified research directions while providing complementary insights. This alignment highlights both foundational themes and emerging trends within the research field.
Foundational studies, such as studies of the optical properties of FBGs and fiber materials, play a central role in both analyses. These themes, emphasized in the author co-citation analysis, align closely with Cluster #1 Fiber Bragg Grating and Cluster #6 Refractive Index in the keyword co-occurrence analysis. Both clusters underscore the intrinsic properties of FBGs, such as their optical characteristics and parameter modulation, which form the basis for their wide-ranging applications.
Sensor applications and performance optimization are another prominent area of overlap between the two analyses. Practical applications, such as structural health monitoring and sensor performance enhancement, highlighted in the author co-citation analysis, correspond to keyword clusters like Cluster #0 Structural Health Monitoring, Cluster #4 Strain Sensors, and Cluster #2 Temperature Measurement. These clusters illustrate how FBG-based sensors have been successfully deployed in engineering projects and environmental monitoring, demonstrating their reliability and effectiveness in real-world scenarios.
Emerging technologies represent a forward-looking dimension of the research landscape. The integration of cutting-edge approaches, such as AI and machine learning, identified in the author co-citation analysis, is reflected in clusters such as Cluster #3 Optical Fiber Polarization, Cluster #5 Surface Plasmon Resonance, and Cluster #8 Robot Sensing Systems in the keyword co-occurrence network. These clusters highlight the growing interdisciplinary nature of research on OFGSs, demonstrating their potential to intersect with diverse technological fields and drive innovation.
While both analyses converge on similar thematic categories, the keyword co-occurrence analysis provides a more granular exploration of specific topics. For instance, emerging clusters such as Cluster #7 Optoelectronic Oscillator and Cluster #10 3D Printing highlight advances in sensor design and manufacturing, areas not explicitly captured in the author co-citation analysis.

3.4.2. Evolutionary Trends and Hot Topics

Figure 7 presents a timeline view of keyword co-occurrence, offering insights into the temporal evolution of research priorities in the field of OFGSs.
During the early phase of research (1990s–2000s), efforts primarily focused on the development of FBGs and their fundamental properties. Studies during this period emphasized their use in temperature and strain sensing, laying the groundwork for establishing FBGs as a pivotal sensing technology. Clusters such as #1 Fiber Bragg Grating and #4 Strain Sensors reflect this foundational focus, highlighting the intrinsic capabilities of FBGs in accurately detecting physical changes.
As the technology matured during the mid-phase expansion (2000s–2010s), applications diversified significantly. This period saw the integration of FBGs into large-scale systems, particularly in structural health monitoring and advanced sensing mechanisms. Keywords such as “structural health monitoring” and “optical fiber polarization” became increasingly prominent, signaling that FBGs were being utilized in comprehensive monitoring frameworks designed to ensure the integrity and safety of critical infrastructure. This shift marked a transition from fundamental research to practical, real-world applications.
In recent years (2010s–present), there has been a surge in interest in emerging technologies, marking a transition towards miniaturization, multifunctionality, and the integration of cutting-edge innovations. Topics such as “mechanical sensors” (Cluster #0), “distribute monitoring” (Cluster #1), “machine learning” (Cluster #3),” surface plasmon resonance” (Cluster #5), “vernier effect” (Cluster #6), “robot sensing systems” (Cluster #8), and “3D printing” (Cluster #10) illustrate the growing interdisciplinary nature of FBG research. Among these, mechanical sensors—particularly those utilizing mechanically induced grating technologies—represent a paradigm shift in sensor fabrication. By replacing traditional UV inscription with controlled stress modulation, these systems enable real-time reconfigurability of grating properties and exceptional thermal stability, surpassing the limitations of traditional FBGs [66]. Furthermore, the integration of OFBGs with distributed monitoring systems enhances their versatility, allowing for spatially resolved sensing across large-scale structures while maintaining high precision and signal-to-noise ratios [67]. This compatibility facilitates simultaneous multipoint detection, making OFBGs ideal for applications in structural health monitoring and smart infrastructure. Collectively, these advancements underscore the convergence of OFGSs with AI, machine learning, and advanced manufacturing techniques, driving innovation in next-generation sensor systems and expanding their potential across diverse industrial and scientific domains.
The consistency between the author co-citation clustering in Figure 5 and the keyword co-occurrence analysis in Figure 6 and Figure 7 underscores the strong thematic foundation of OFGS research. However, the keyword analysis offers additional insights by identifying specific emerging trends and interdisciplinary intersections. For instance, Cluster #7 Optoelectronic Oscillator and Cluster #10 3D Printing represent highly specialized advances in sensor design and manufacturing that were not explicitly highlighted in the author co-citation results. Furthermore, clusters such as “robot sensing systems” and “distributed sensing” illustrate the expansion of OFGS research into fields like robotics and composite materials, demonstrating their cross-disciplinary potential.
By integrating the findings from both the author co-citation and keyword co-occurrence analyses, this study offers a comprehensive overview of the research landscape. This dual-perspective approach not only validates the identified key research directions but also highlights emerging trends and interdisciplinary opportunities that are shaping the future of OFGS research.

4. Challenges and Technological Advancements

OFGSs have attracted considerable attention for their high sensitivity, accuracy, and versatility across diverse applications. However, as sensor technologies continue to advance, OFGSs face increasing competition from alternative sensing methods, some of which demonstrate superior performance in specific operational scenarios. This section presents a comparative analysis of OFGSs and competing sensor types, focusing on their performance and architecture, and the environments in which they can be deployed. Furthermore, we explore the limitations of OFGS, particularly in the context of their use in both enclosed and open spaces. We also examine exposure-related risks and discuss how these factors are driving future OFGS development trajectories.

4.1. Environmental Limitations and Reliability Concerns

Despite their numerous advantages, OFGSs face notable challenges, particularly regarding their adaptability to different environments. In controlled indoor settings such as aircraft cabins and industrial facilities, OFGSs demonstrate exceptional stability [2,3]. The enclosed nature of these environments effectively shields the sensors from external mechanical disturbances and environmental fluctuations, ensuring reliable long-term operation. However, outdoor installations present significant challenges. The absence of protective casings—necessary to maintain high strain sensitivity—makes exposed OFGSs particularly vulnerable to physical damage from environmental factors and wildlife interference [13].
A promising approach to mitigating environmental vulnerabilities is embedding FBGs within construction materials such as concrete or metal structures. This strategy can enhance protection against external factors. However, this approach introduces new challenges related to material compatibility. The thermal expansion mismatch between silica fibers and common construction materials remains a critical issue. Shen et al. [68] quantified this effect, reporting a reduction of approximately 1.5 pm/°C in the temperature sensitivity of concrete-embedded OFGSs. These thermal effects are further compounded by strain transfer variations at the fiber–matrix interface, particularly in metallic structures where the expansion coefficient mismatch can exceed 250 pm/°C at 350–400 °C [69]. The resulting measurement errors and potential long-term fatigue issues have limited the adoption of embedded OFGS solutions in critical infrastructure applications

4.2. Competitive Analysis with Alternative Sensing Technologies

While OFGSs possess unique advantages, they compete with other sensor technologies that offer comparable or alternative functionalities. The selection of appropriate sensing technology depends heavily on specific application requirements.
Traditional electrical sensors, such as strain gauges, piezoelectric sensors, and resistive temperature detectors, remain widely used in industrial applications due to their well-established fabrication processes and cost-effectiveness. However, they suffer from limitations such as susceptibility to electromagnetic interference, limited multiplexing capability, and lower durability in harsh environments.
When comparing OFGSs with distributed fiber optic sensors (DFOSs), it becomes clear that DFOSs offer continuous measurement over long distances, relying on principles like Rayleigh, Raman, or Brillouin scattering. OFGSs, on the other hand, are based on discrete sensing points, which limit their measurement range but enhance their spatial resolution. As detailed in Table 3, key performance metrics including sensing principle, spatial resolution, and measurement range reveal that while DFOSs excel in large-area monitoring, OFGSs maintain superior spatial resolution for point measurements.
Similarly, interferometric optical sensors (such as Fabry–Pérot and Mach–Zehnder interferometers) are known for their ultra-high sensitivity to minute physical changes. These sensors excel in environments that require fine measurement resolution, such as precision metrology and biomedical diagnostics. However, they typically require more complex interrogation systems and are more sensitive to environmental disturbances such as temperature fluctuations and vibrations.

4.3. Emerging Solutions and Future Development Trends

The challenges and competitive pressures discussed in Section 4.1 and Section 4.2 highlight the need for continuous innovation in OFGS technology. Future advancements will likely focus on enhancing environmental resilience, integrating intelligent data processing, and developing hybrid sensing architectures to expand application potential.
One of the most pressing challenges for OFGS deployment is their susceptibility to environmental stressors, particularly in open-space applications. Research efforts are increasingly directed toward developing advanced materials that improve sensor durability and operational stability. For instance, radiation-hardened optical fibers and corrosion-resistant coatings are being explored to enhance sensor longevity in extreme environments such as nuclear facilities, deep-sea explorations, and aerospace applications. Additionally, nanomaterial-based composites, such as graphene-enhanced substrates, offer promising solutions for mitigating thermal expansion mismatches in embedded sensing applications. Studies on chalcogenide glasses and perovskite-based fiber coatings have also demonstrated potential for extending the operational spectral range and improving the nonlinear optical response of OFGS, making them more adaptable to varying environmental conditions.
The integration of artificial intelligence (AI) and quantum technologies is revolutionizing OFGS capabilities, transforming them from passive sensing elements into predictive, adaptive systems. Machine learning algorithms are increasingly employed for real-time pattern recognition and anomaly detection in structural health monitoring and industrial diagnostics. AI-driven signal processing can enhance the accuracy and reliability of OFGS measurements, particularly in noisy environments where traditional signal interpretation methods struggle.
On the frontier of precision sensing, quantum-enhanced optical fiber sensors have shown remarkable potential. The use of quantum entanglement and squeezed light sources could significantly enhance measurement precision, reducing noise interference and extending OFGS applications into domains such as gravitational wave detection and underground resource exploration. Moreover, advancements in single-photon detection and quantum-state engineering are expected to push the performance limits of OFGSs, enabling them to function effectively in ultra-low-light conditions and high-precision metrology scenarios.
With the rise of flexible photonics and microfabrication techniques, miniaturized OFGS solutions are gaining traction, particularly for biomedical and aerospace applications. The development of bio-compatible polymer fiber gratings enables real-time, non-invasive physiological monitoring, offering new possibilities for wearable health sensors. Similarly, lightweight, conformable OFGS patches are being designed for aerospace structural monitoring, where conventional sensors are often impractical due to weight constraints. These innovations address the long-standing trade-off between measurement accuracy and sensor footprint, broadening the range of feasible OFGS applications.
A major trend in OFGS development is the integration of hybrid sensing architectures that combine the strengths of different optical sensing approaches. By strategically merging high-resolution OFGS nodes with DFOS networks, researchers are creating hybrid systems that offer both localized precision and large-area monitoring. These hybrid solutions effectively bridge the gap between point-based OFGS measurements and fully distributed sensing methodologies, enhancing the scalability of fiber optic sensor networks.
Furthermore, the convergence of OFGSs with IoT-enabled sensor networks is unlocking new possibilities for large-scale industrial and environmental monitoring. Edge computing is being leveraged to process sensor data locally, reducing latency and improving response times in real-time applications. Meanwhile, blockchain-based data integrity verification is being explored to enhance the security and reliability of OFGS networks, particularly in critical infrastructure monitoring and environmental sensing applications.
The future of OFGSs will be shaped by interdisciplinary research spanning photonics, materials science, and data analytics. Key research directions include the development of self-healing optical fiber coatings to enhance sensor longevity, MEMS-actuated dynamically tunable fiber gratings for adaptive sensing, and hybrid quantum-classical sensing paradigms to improve resolution and robustness. However, several challenges remain. Standardizing manufacturing processes for next-generation OFGS technologies is essential for ensuring consistency and scalability. Additionally, establishing robust validation protocols for emerging applications will be critical in facilitating widespread adoption. Addressing these challenges will be pivotal in transitioning OFGSs from specialized research tools to ubiquitous sensing solutions across industrial, environmental, and biomedical domains.

5. Conclusions

In conclusion, OFGS technology has evolved significantly from basic strain and temperature sensing to advanced, multifunctional systems. This paper has provided a comprehensive overview of the field’s development, current status, and future potential. While challenges remain, such as the need for improved sensitivity, reliability in extreme environments, and cost-effective manufacturing, the future of OFGSs lies in interdisciplinary collaboration. Future research should focus on exploring new materials, integrating AI and big data for real-time insights, and leveraging emerging technologies like quantum sensing. Addressing these challenges will unlock the full potential of OFGSs, leading to transformative applications in healthcare, infrastructure, and beyond, and enabling intelligent, autonomous systems that redefine technological possibilities.

Author Contributions

Methodology, Y.D. and X.W.; software, Y.D. and W.R.; validation, X.W.; investigation, Y.D. and X.W.; data curation, Y.D. and X.W.; writing—original draft preparation, Y.D.; writing—review and editing, X.W.; visualization, Y.D.; supervision, W.R. and X.W.; project administration, X.W.; funding acquisition, X.W. and W.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Natural Science Foundation of Fujian Province (Project No.: 2023J011032), Industry-University Cooperation Project of Fujian Province (Project No.: 2021H6039) and Sanming Major Science and Technology Project of Industry-University-Research Collaborative Innovation (Project No.: 2022-G-4).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The bibliographic data analyzed in this manuscript were sourced from the Web of Science at https://webofscience.clarivate.cn/wos/woscc/basic-search (5 January 2025).

Acknowledgments

We would like to express our sincere gratitude to Chaomei Chen and his CiteSpace team for their invaluable practical guidance on this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
OFGSOptical Fiber Grating Sensor
FBGFiber Bragg Grating
LPFGLong Period Fiber Grating
PS-FGPhase-Shift Fiber Grating
TFBGTilted Fiber Bragg Grating
SPRPlasmon Resonance
SMFSingle-Mode Fiber
FMFFew-Mode Fiber
MCFMulti-Core Fiber
PCFPhotonic Crystal Fiber
WoSWeb of Science
AIArtificial Intelligence
IoTInternet of Things
DFOSDistributed Fiber Optic Sensor

References

  1. Young, G.O.; Hill, K.O.; Fujii, Y.; Johnson, D.C.; Kawasaki, B.S. Synthetic structure of industrial plastics. Plastics 1978, 32, 647–649. [Google Scholar]
  2. Minakuchi, S.; Takeda, N. Recent advancement in optical fiber sensing for aerospace composite structures. Photonic Sens. 2013, 3, 345–354. [Google Scholar] [CrossRef]
  3. Hegde, G.; Asokan, S.; Hegde, G. Fiber Bragg grating sensors for aerospace applications: A review. ISSS J. Micro Smart Syst. 2022, 11, 257–275. [Google Scholar] [CrossRef]
  4. Wu, T.; Liu, G.; Fu, S.; Xing, F. Recent Progress of Fiber-Optic Sensors for the Structural Health Monitoring of Civil Infrastructure. Sensors 2020, 20, 4517. [Google Scholar] [CrossRef]
  5. Wang, H.; Dai, J.G. Strain transfer analysis of fiber Bragg grating sensor assembled composite structures subjected to thermal loading. Compos. Part. B Eng. 2019, 162, 303–313. [Google Scholar] [CrossRef]
  6. Rajeev, P.; Kodikara, J.; Chiu, W.K.; Kuen, T. Distributed Optical Fibre Sensors and their Applications in Pipeline Monitoring. Key Eng. Mater. 2013, 558, 424–434. [Google Scholar] [CrossRef]
  7. Zhu, Y.; Chen, W.; Fu, Y.; Huang, S. A review of harsh environment fiber optic sensing networks for bridge structural health monitoring. In Proceedings of the SPIE OPTICS + PHOTONICS, San Diego, CA, USA, 13–17 August 2006; Volume 6314, p. 63140. [Google Scholar]
  8. Murayama, H.; Igawa, H.; Omichi, K.; Machijima, Y. Application of distributed sensing with long length FBG to structural health monitoring. In Proceedings of the 9th International Conference on Optical Communications and Networks (ICOCN 2010), Nanjing, China, 24–27 October 2010; Volume 18, pp. 18–24. [Google Scholar]
  9. Afzal, M.H.B.; Kabir, S.; Sidek, O. Fiber optic sensor-based concrete structural health monitoring. In Proceedings of the 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC), Riyadh, Saudi Arabia, 24–26 April 2011; pp. 1–5. [Google Scholar]
  10. Wild, G.; Allwood, G.; Hinckley, S. Distributed sensing, communications, and power in optical Fibre Smart Sensor networks for structural health monitoring. In Proceedings of the 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Brisbane, QLD, Australia, 7–10 December 2010; pp. 139–144. [Google Scholar]
  11. Wang, D.Y.; Wang, Y.; Han, M.; Gong, J.; Wang, A. Fully distributed fiber-optic biological sensing. IEEE Photonics Technol. Lett. 2010, 22, 1553–1555. [Google Scholar] [CrossRef]
  12. You, R.Z.; Ren, L.; Song, G.B. A novel fiber Bragg grating (FBG) soil strain sensor. Measurement 2019, 139, 85–91. [Google Scholar] [CrossRef]
  13. Qin, H.Y.; Tang, P.F.; Lei, J.; Chen, H.B.; Luo, B.G. Investigation of Strain-Temperature Cross-Sensitivity of FBG Strain Sensors Embedded Onto Different Substrates. Photonic Sens. 2022, 13, 230127. [Google Scholar] [CrossRef]
  14. Kang, D.; Kim, H.Y.; Kim, D.H. Thermal characteristics of FBG sensors at cryogenic temperatures for structural health monitoring. Int. J. Precis. Eng. Manuf. 2016, 17, 5–9. [Google Scholar] [CrossRef]
  15. Peng, J.; Jia, S.; Yu, H.; Kang, X.; Yang, S.; Xu, S. Design and Experiment of FBG Sensors for Temperature Monitoring on External Electrode of Lithium-Ion Batteries. IEEE Sens. J. 2021, 21, 4628–4634. [Google Scholar] [CrossRef]
  16. Gowda, R.B.; Sharan, P.; K, S.; Braim, M.; Alodhayb, A.N. An FBG-based optical pressure sensor for the measurement of radial artery pulse pressure. J. Biophotonics 2024, 17, e202400083. [Google Scholar] [CrossRef] [PubMed]
  17. Rosolem, J.B. Electroless Nickel-Plating Sealing in FBG Pressure Sensor for Thermoelectric Power Plant Engines Applications. J. Light. Technol. 2019, 37, 4791–4798. [Google Scholar]
  18. Dey, T.K.; Biswas, P.; Basumallick, N.; Bandyopadhyay, S. Long Period Fiber Grating Near Turn Around Point: Suitable Design for Bio-Sensing. IEEE Sens. J. 2021, 21, 18800–18805. [Google Scholar]
  19. Baliyan, A.; Sital, S.; Tiwari, U.; Gupta, R.; Sharma, E.K. Long period fiber grating based sensor for the detection of triacylglycerides. Biosens. Bioelectron. 2016, 79, 693–700. [Google Scholar]
  20. Wang, R.; Ren, Z.; Kong, X.; Kong, D.; Hu, B.; He, Z. Graphene-assisted high-precision temperature sensing by long-period fiber gratings. J. Phys. D Appl. Phys. 2019, 53, 065104. [Google Scholar]
  21. Wu, C.W.; Chiang, C.C. Application of Notched Long-Period Fiber Grating Based Sensor for CO2 Gas Sensing. Fiber Integr. Opt. 2016, 35, 22–28. [Google Scholar] [CrossRef]
  22. Liu, T. Simultaneous Detection of Temperature, Strain, Refractive Index, and pH Based on a Phase-Shifted Long-Period Fiber Grating. J. Light. Technol. 2023, 41, 5169–5180. [Google Scholar] [CrossRef]
  23. Chen, M.Q.; He, T.Y.; Zhao, Y.; Yang, G. Ultra-short phase-shifted fiber Bragg grating in a microprobe for refractive index sensor with temperature compensation. Opt. Laser Technol. 2023, 156, 108672. [Google Scholar]
  24. Min, R.; Marques, C.; Bang, O.; Ortega, B. Moiré phase-shifted fiber Bragg gratings in polymer optical fibers. Opt. Fiber Technol. 2018, 41, 78–81. [Google Scholar]
  25. Lu, Y.F.; Shen, C.Y.; Chen, D.B.; Chu, J.L.; Wang, Q.; Dong, X.Y. Highly sensitive twist sensor based on tilted fiber Bragg grating of polarization-dependent properties. Opt. Fiber Technol. 2014, 20, 491–494. [Google Scholar] [CrossRef]
  26. Bialiayeu, A.; Ianoul, A.; Albert, J. Polarization-resolved sensing with tilted fiber Bragg gratings: Theory and limits of detection. J. Opt. 2015, 17, 085601. [Google Scholar]
  27. Kisała, P.; Mroczka, J.; Cięszczyk, S.; Skorupski, K.; Panas, P. Twisted tilted fiber Bragg gratings: New structures and polarization properties. Opt. Lett. 2018, 43, 4445–4448. [Google Scholar]
  28. Yan, Y.X.; Gu, Z.T.; Jiang, H.P.; Li, Z.Y.; Wu, J.Y.; Wang, Y. Design and simulation of a hybrid coated LPG-TFBG-FBG three-parameter sensor for an ocean environment. J. Opt. Soc. Am. B 2022, 39, 2109–2119. [Google Scholar]
  29. Jiang, Y.; Hao, Z.; Feng, D.; Zhou, K.; Zhang, L.; Zhao, J. Hybrid Grating in Reduced-Diameter Fiber for Temperature-Calibrated High-Sensitivity Refractive Index Sensing. Appl. Sci. 2019, 9, 1923. [Google Scholar] [CrossRef]
  30. Feng, M.; Liu, Y.; Wang, Z.; Mao, B.; Zhang, H. Ultra-Broadband Mode Converter Using Cascading Chirped Long-Period Fiber Grating. IEEE Photonics J. 2019, 11, 1–10. [Google Scholar]
  31. Zhao, X. Bandwidth Tunable Ultra-Broadband OAM Generators Based on Chirped Long Period Fiber Gratings at Dispersion Turning Point. J. Light. Technol. 2024, 42, 4980–4986. [Google Scholar]
  32. Fang, H.; Wei, C.; Yang, H. D-Shaped Photonic Crystal Fiber Plasmonic Sensor Based on Silver-Titanium Dioxide Composite Micro-grating. Plasmonics 2021, 16, 2049–2059. [Google Scholar] [CrossRef]
  33. Erdogan, İ.; Dogan, Y. Au-TiO2-Graphene Grated Highly Sensitive D-Shaped SPR Refractive Index Sensor. Plasmonics 2023, 18, 1203–1210. [Google Scholar] [CrossRef]
  34. Ying, Y.; Zhang, R.; Si, G.-Y.; Wang, X.; Qi, Y.-W. D-shaped tilted fiber Bragg grating using magnetic fluid for magnetic field sensor. Opt. Commun. 2017, 405, 228–232. [Google Scholar] [CrossRef]
  35. Yan, H.T.; Liu, Q.; Ming, Y.; Luo, W.; Chen, Y.; Lu, Y.Q. Metallic Grating on a D-Shaped Fiber for Refractive Index Sensing. IEEE Photonics J. 2013, 5, 4800706. [Google Scholar] [CrossRef]
  36. Raghuwanshi, S.K.; Kumar, M.; Jindal, S.K.; Kumar, A.; Prakash, O. High-Sensitivity Detection of Hazardous Chemical by Special Featured Grating-Assisted Surface Plasmon Resonance Sensor Based on Bimetallic Layer. IEEE Trans. Instrum. Meas. 2020, 69, 5072–5080. [Google Scholar] [CrossRef]
  37. Huang, C.; Zhou, Y.; Yu, G.; Zeng, J.; Li, Q.; Shen, K.; Wu, X.; Guo, R.; Zhang, C.; Zheng, B. Glutathione-functionalized long-period fiber gratings sensor based on surface plasmon resonance for detection of As3+ ions. Nanotechnology 2023, 32, 485501. [Google Scholar] [CrossRef]
  38. Moreno, Y.; Song, Q.; Xing, Z.; Sun, Y.; Yan, Z. Hybrid tilted fiber gratings-based surface plasmon resonance sensor and its application for hemoglobin detection. Chin. Opt. Lett. 2020, 18, 100601. [Google Scholar] [CrossRef]
  39. Lobry, M.; Loyez, M.; Debliquy, M.; Chah, K.; Goormaghtigh, E.; Caucheteur, C. Electro-plasmonic-assisted biosensing of proteins and cells at the surface of optical fiber. Biosens. Bioelectron. 2023, 220, 114867. [Google Scholar] [CrossRef] [PubMed]
  40. Tian, X.; Shi, J.; Wang, Y.; Li, L.; She, Y. Sensitivity Enhancement for Fiber Bragg Grating Strain Sensing Based on Optoelectronic Oscillator With Vernier Effect. IEEE Photonics J. 2021, 13, 1–6. [Google Scholar]
  41. Wang, X.; Chen, T.; Meng, D.; Wang, F. A Simple FBG Fabry–Perot Sensor System With High Sensitivity Based on Fiber Laser Beat Frequency and Vernier Effect. IEEE Sens. J. 2021, 21, 71–75. [Google Scholar]
  42. Liu, F.; Zhang, Y.; Meng, F.; Dong, M.; Zhu, L.; Luo, F. Complex optical fiber sensor based on the Vernier effect for temperature sensing. Opt. Fiber Technol. 2021, 61, 102424. [Google Scholar] [CrossRef]
  43. Su, H.; Zhao, C.; Song, X.; Kong, F.; Zhang, Z.; Liu, C. High-sensitivity optical fiber temperature sensor with cascaded configuration of MZI and FPI based on Vernier effect. Opt. Fiber Technol. 2021, 67, 102751. [Google Scholar] [CrossRef]
  44. Bell, K.; Mukhangaliyeva, L.; Khalili, L.; Reza, P.H. Hyperspectral absorption microscopy using photoacoustic remote sensing. Opt. Express 2021, 29, 24338–24348. [Google Scholar] [CrossRef]
  45. Leal-Junior, A.; Silveira, M.; Macedo, L.; Frizera, A.; Marques, C. Polarization-Assisted multiparameter sensing using a single fiber Bragg grating. Opt. Fiber Technol. 2024, 84, 103775. [Google Scholar] [CrossRef]
  46. Esposito, F.; Srivastava, A.; Iadicicco, A.; Campopiano, S. Multi-parameter sensor based on single Long Period Grating in Panda fiber for the simultaneous measurement of SRI, temperature and strain. Opt. Laser Technol. 2019, 113, 198–203. [Google Scholar] [CrossRef]
  47. Zhao, Y.; Song, T.-T. Fiber bragg grating current sensor based on birefringence effect. Microw. Opt. Technol. Lett. 2012, 54, 822–826. [Google Scholar] [CrossRef]
  48. Tang, J. Long Period Fiber Grating Inscribed in Hollow-Core Photonic Bandgap Fiber for Gas Pressure Sensing. IEEE Photonics J. 2017, 9, 1–7. [Google Scholar] [CrossRef]
  49. Chen, Y.; Han, Q.; Yan, W.; Yao, Y.; Liu, T. Magnetic-Fluid-Coated Photonic Crystal Fiber and FBG for Magnetic Field and Temperature Sensing. IEEE Photonics Technol. Lett. 2016, 28, 2665–2668. [Google Scholar] [CrossRef]
  50. Chen, C.; Ibekwe-SanJuan, F.; Hou, J. The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. J. Am. Soc. Inf. Sci. Technol. 2010, 61, 1386–1409. [Google Scholar]
  51. Chen, C. Predictive effects of structural variation on citation counts. J. Am. Soc. Inf. Sci. Technol. 2012, 63, 431–449. [Google Scholar]
  52. Chen, C. Science Mapping: A Systematic Review of the Literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar]
  53. Chen, C.; Song, M. Visualizing a Field of Research: A Methodology of Systematic Scientometric Reviews. PLoS ONE 2019, 14, 0223994. [Google Scholar]
  54. Chen, C. Hindsight, insight, and foresight: A multi-level structural variation approach to the study of a scientific field. Technol. Anal. Strateg. Manag. 2013, 25, 619–640. [Google Scholar]
  55. Morey, W.W.; Meltz, G.; Glenn, W.H. Fiber optic Bragg grating sensors. Fiber Opt. Laser Sens. 1990, 1169, 98–107. [Google Scholar]
  56. Vengsarkar, A.M. Long-period fiber gratings as band-rejection filters. J. Light. Technol. 1996, 14, 58–65. [Google Scholar] [CrossRef]
  57. Kersey, A.D. Fiber grating sensors. J. Light. Technol. 1997, 15, 1442–1463. [Google Scholar] [CrossRef]
  58. Rao, Y. “In-fibre Bragg grating sensors” Meas. Sci. Technol. 1997, 8, 355–375. [Google Scholar] [CrossRef]
  59. Mihailov, S.J. Fiber Bragg Grating Sensors for Harsh Environments. Sensors 2012, 12, 1898–1918. [Google Scholar] [CrossRef]
  60. Albert, J.; Shao, L.Y.; Caucheteur, C. Tilted fiber Bragg grating sensors. Laser Photonics Rev. 2012, 7, 83–108. [Google Scholar] [CrossRef]
  61. Caucheteur, C.; Guo, T.; Albert, J. Review of plasmonic fiber optic biochemical sensors: Improving the limit of detection. Anal. Bioanal. Chem. 2015, 407, 3883–3897. [Google Scholar] [CrossRef]
  62. Min, R.; Liu, Z.; Pereira, L.; Yang, C.; Sui, Q.; Marques, C. Optical fiber sensing for marine environment and marine structural health monitoring: A review. Opt. Laser Technol. 2021, 140, 107082. [Google Scholar] [CrossRef]
  63. Chiavaioli, F.; Baldini, F.; Tombelli, S.; Trono, C.; Giannetti, A. Biosensing with optical fiber gratings. Nanophotonics 2017, 6, 663–679. [Google Scholar] [CrossRef]
  64. Presti, D.L. Fiber Bragg Gratings for Medical Applications and Future Challenges: A Review. IEEE Access 2020, 8, 156863–156888. [Google Scholar] [CrossRef]
  65. Broadway, C.; Min, R.; Leal-Junior, A.G.; Marques, C.; Caucheteur, C. Toward Commercial Polymer Fiber Bragg Grating Sensors: Review and Applications. J. Light. Technol. 2019, 37, 2605–2615. [Google Scholar]
  66. Ran, J.; Chen, Y.; Wang, G.; Zhong, Z.; Zhang, J.; Xu, O.; Huang, Q.; Lei, X. Mechanically Induced Long-Period Fiber Gratings and Applications. Photonics 2024, 11, 223. [Google Scholar] [CrossRef]
  67. Li, C.; Tang, J.; Cheng, C. FBG Arrays for Quasi-Distributed Sensing: A Review. Photonic Sens. 2021, 11, 91–108. [Google Scholar] [CrossRef]
  68. Shen, S.; Xiong, L.; Pan, H.; Ge, D.; Zhou, W.; Guo, Y. Investigation of the Thermal-Force Coupling and Temperature Compensation of Embedded FBG Strain Sensor. IEEE Sens. J. 2024, 24, 20645–20654. [Google Scholar] [CrossRef]
  69. Prashar, S.; Engles, D.; Malik, S.S. Effect of thermal expansion mismatch in grating material and host specimen on thermal sensitivity of FBG sensor. Photon Netw. Commun. 2017, 34, 266–270. [Google Scholar] [CrossRef]
  70. Ferdinand, P. The Evolution of Optical Fiber Sensors Technologies During the 35 Last Years and Their Applications in Structure Health Monitoring. In Proceedings of the EWSHM—7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Nantes, France, 8–11 July 2014; pp. 914–929. [Google Scholar]
  71. Ma, J.; Pei, H.; Zhu, H.; Shi, B.; Yin, J. A review of previous studies on the applications of fiber optic sensing technologies in geotechnical monitoring. Rock. Mech. Bull. 2023, 2, 100021. [Google Scholar] [CrossRef]
  72. Udd, E.; Spillman, W.B. (Eds.) Fiber Optic Sensors: An Introduction for Engineers and Scientists, 3rd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2024. [Google Scholar]
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Figure 1. Flow diagram of study selection process based on PRISMA 2020 Guidelines.
Figure 1. Flow diagram of study selection process based on PRISMA 2020 Guidelines.
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Figure 2. Temporal evolution of publication documents on OFGSs from 1992 to 2024.
Figure 2. Temporal evolution of publication documents on OFGSs from 1992 to 2024.
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Figure 3. Top 30 main research areas in WOS on OFGS research studies from 1992 to 2024.
Figure 3. Top 30 main research areas in WOS on OFGS research studies from 1992 to 2024.
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Figure 4. Top 30 journals in which papers related to OFGS were published from 1992 to 2024.
Figure 4. Top 30 journals in which papers related to OFGS were published from 1992 to 2024.
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Figure 5. Author co-citation clustering analysis (#: cluster numbers, ()). g-index: a measure of the citation impact of the articles. LRF (Link Retaining Factor): a parameter that refers to the proportion of links that are maintained when filtering out weak connections during the clustering process. L/N (Link-to-Node Ratio): the ratio of the total number of links (edges) to the total number of nodes. LBY (Look-Back Years): refers to the number of years considered in the analysis when looking back. N: number of total nodes. E: number of edges. Modularity Q: generally considered significant when Q > 0.3, indicates the prominence of the clustering structure. Weighted mean silhouette S: represents the average silhouette value of the clusters (S > 0.5 suggests reasonable clustering, while S > 0.7 indicates that the clustering is highly convincing) [50,51,52,53,54].
Figure 5. Author co-citation clustering analysis (#: cluster numbers, ()). g-index: a measure of the citation impact of the articles. LRF (Link Retaining Factor): a parameter that refers to the proportion of links that are maintained when filtering out weak connections during the clustering process. L/N (Link-to-Node Ratio): the ratio of the total number of links (edges) to the total number of nodes. LBY (Look-Back Years): refers to the number of years considered in the analysis when looking back. N: number of total nodes. E: number of edges. Modularity Q: generally considered significant when Q > 0.3, indicates the prominence of the clustering structure. Weighted mean silhouette S: represents the average silhouette value of the clusters (S > 0.5 suggests reasonable clustering, while S > 0.7 indicates that the clustering is highly convincing) [50,51,52,53,54].
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Figure 6. Keyword co-occurrence network (#: cluster numbers, ()). g-index: a measure of the citation impact of the articles. LRF (Link Retaining Factor): a parameter that refers to the proportion of links that are maintained when filtering out weak connections during the clustering process. L/N (Link-to-Node Ratio): the ratio of the total number of links (edges) to the total number of nodes. LBY (Look-Back Years): refers to the number of years considered in the analysis when looking back. N: number of total nodes. E: number of edges. Modularity Q: generally considered significant when Q > 0.3, indicates the prominence of the clustering structure. Weighted mean silhouette S: represents the average silhouette value of the clusters (S > 0.5 suggests reasonable clustering, while S > 0.7 indicates that the clustering is highly convincing) [50,51,52,53,54].
Figure 6. Keyword co-occurrence network (#: cluster numbers, ()). g-index: a measure of the citation impact of the articles. LRF (Link Retaining Factor): a parameter that refers to the proportion of links that are maintained when filtering out weak connections during the clustering process. L/N (Link-to-Node Ratio): the ratio of the total number of links (edges) to the total number of nodes. LBY (Look-Back Years): refers to the number of years considered in the analysis when looking back. N: number of total nodes. E: number of edges. Modularity Q: generally considered significant when Q > 0.3, indicates the prominence of the clustering structure. Weighted mean silhouette S: represents the average silhouette value of the clusters (S > 0.5 suggests reasonable clustering, while S > 0.7 indicates that the clustering is highly convincing) [50,51,52,53,54].
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Figure 7. Visualization of clusters in terms of timeline view of the keyword’s co-citation analysis. The horizontal axis represents the year, each node represents a popular term, and the size of each node is proportional to its cited frequency; red tree rings mean the burst. The line between each node represents the temporal evolution of the terms, and the line thickness represents the co-citation strength; these lines reflect the relationship between transfer and inheritance among keywords [50,51,52,53,54].
Figure 7. Visualization of clusters in terms of timeline view of the keyword’s co-citation analysis. The horizontal axis represents the year, each node represents a popular term, and the size of each node is proportional to its cited frequency; red tree rings mean the burst. The line between each node represents the temporal evolution of the terms, and the line thickness represents the co-citation strength; these lines reflect the relationship between transfer and inheritance among keywords [50,51,52,53,54].
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Table 1. Top 10 researchers with the highest publication record count on OFGS.
Table 1. Top 10 researchers with the highest publication record count on OFGS.
AuthorRC%CATCH-iInstitutionCountry
Caucheteur C1091.1963442 (3341)5388 (4867)41Univ. of MonsBelgium/China
Marques C1081.1852381 (2287)4078 (3648)41Tech. Univ. of OstravaCzech/Portugal
Qiao X1061.1631378 (1300)2023 (1767)26 Northwest Univ. China
Yuan L1011.108967 (897)1327 (1179)21Guilin Univ. of Elec. Tech.China
Guan B840.9213254 (3197)4463 (4289)37Jinan Univ.UK/USA/China
TAM H830.912891 (2842)3480 (3382)33Hong Kong Polytechnic Univ.China
Albert J820.93021 (2947)5464 (5006)41Univ. of Ottawa Canada
Cusano A750.8232385 (2331)3122 (2992)35Univ. of SannioItaly/USA
Dong X700.7682032 (1984)2560 (2461)34Guangdong Univ. of Tech.China/Singapore
Zhu L690.757496 (479)571 (542)13Beijing Info. Sci. & Tech.Univ.China
RC = record counts, CA = citing articles, TC = times cited, H-i = H-index. Number inside of ( ) is the count without self-citations.
Table 2. Top 20 references with the strongest citation bursts.
Table 2. Top 20 references with the strongest citation bursts.
ReferencesYearStrengthBeginEnd1992–2024
KERSEY AD, 1992, ELECTRON LETT., V28, P236, DOI 10.1049/el:19920146z199219.3519921997Photonics 12 00349 i001
Kersey AD, 1997, J.LIGHTWAVE TECHNOL, V15, P1442, DOI 10.1109/50.618377199743.5119982003Photonics 12 00349 i002
Rao YJ, 1997, MEAS SCI TECHNOL, V8, P355, DOI 10.1088/0957-0233/8/4/002199721.9819982003Photonics 12 00349 i003
James SW, 2003, MEAS SCI TECHNOL, V14, PR49, DOI 10.1088/0957-0233/14/5/201200329.0320042009Photonics 12 00349 i004
Lee B, 2003, OPT FIBER TECHNOL, V9, P57, DOI 10.1016/S1068-5200(02)00527-8200319.7220042009Photonics 12 00349 i005
Majumder M, 2008, SENSOR ACTUAT A-PHYS, V147, P150, DOI 10.1016/j.sna.2008.04.008200830.9320102013Photonics 12 00349 i006
Albert J, 2013, LASER PHOTONICS REV, V7, P83, DOI 10.1002/lpor.20110039201356.7720132019Photonics 12 00349 i007
Mihalov SJ, 2012, SENSORS-BASEL, V12, P1898, DOI 10.3390/s120201898201235.3420122017Photonics 12 00349 i008
Kinet D, 2014, SENSORS-BASEL, V14, P7394, DOI 10.3390/s140407394201428.4820142019Photonics 12 00349 i009
Guo T, 2016, OPT LASER TECHNOL, V78, P19, DOI 10.1016/j.optlastec.2015.10.007201625.3920162021Photonics 12 00349 i010
Caucheteux C, 2015, ANAL BIOANAL CHEM, V407, P3883, DOI 10.1007/s00216-014-8411-6201519.8120162019Photonics 12 00349 i011
Tosi D, 2017, SENSORS-BASEL, V17, P0, DOI 10.3390/s17102368201725.2820182023Photonics 12 00349 i012
Hong CY, 2016, SENSOR ACTUAT A-PHYS, V244, P184, DOI 10.1016/j.sna.2016.04.033201622.8320182021Photonics 12 00349 i013
Chiavaloni F, 2017, NANO PHOTONICS-BERLIN, V6, P663, DOI 10.1515/nanoph-2016-0178201721.6220182021Photonics 12 00349 i014
Sahota JK, 2020, OPT ENG, V59, P0, DOI 10.1117/1.OE.59.6.06090120204320202024Photonics 12 00349 i015
Campanella CE, 2018, SENSORS-BASEL, V18, P0, DOI 10.3390/s18093115201834.6720202024Photonics 12 00349 i016
Lo Presti D, 2020, IEEE ACCESS, V8, P156863, DOI 10.1109/ACCESS.2020.3019138202032.9820202024Photonics 12 00349 i017
Broadway C, 2019, J.LIGHTWAVE TECHNOL, V37, P2605, DOI 10.1109/JLT.2018.2885957201920.7420202024Photonics 12 00349 i018
Li TL, 2020, IEEE SENS J, V20, P12074, DOI 10.1109/JSEN.2020.3000257202026.2620222024Photonics 12 00349 i019
Min R, 2021, OPT LASER TECHNOL, V140, P0, DOI 10.1016/j.optlastec.2021.107082202125.420222024Photonics 12 00349 i020
(The light-blue line segments represent periods with no related articles published, the dark-blue line segments represent periods when articles began to be published, and the red line segments represent the periods of citation bursts.).
Table 3. Comparison Between OFGSs, DFOSs, and interferometric optical sensors [70,71,72].
Table 3. Comparison Between OFGSs, DFOSs, and interferometric optical sensors [70,71,72].
FeatureOFGSDFOSInterferometric Optical Sensors
Sensing PrincipleBragg wavelength shiftScattering
(Rayleigh, Raman, Brillouin)		Interference pattern
(Fabry–Pérot, Mach–Zehnder)
Spatial Resolutioncm
(discrete points)		m/variable
(continuous)		mm
(requires precise calibration)
Measurement RangeLimited to sensor locations (km)Long-range
(>50 km)		Limited
(typically, m)
Multiplexing CapabilityExcellent with WDM/FDM techniquesGood but with lower resolutionLimited
Environmental robustnessModerate
(exposed fiber risk)		Good
(embedded sensing possible)		Low
(sensitive to temperature, vibration)
Cost per sensing pointHighLowVery high
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Deng, Y.; Ren, W.; Wang, X. Scientometric Analysis and Research Trends in Optical Fiber Grating Sensors: A Review. Photonics 2025, 12, 349. https://doi.org/10.3390/photonics12040349

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Deng Y, Ren W, Wang X. Scientometric Analysis and Research Trends in Optical Fiber Grating Sensors: A Review. Photonics. 2025; 12(4):349. https://doi.org/10.3390/photonics12040349

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Deng, Yiqiang, Wen Ren, and Xiaoyan Wang. 2025. "Scientometric Analysis and Research Trends in Optical Fiber Grating Sensors: A Review" Photonics 12, no. 4: 349. https://doi.org/10.3390/photonics12040349

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Deng, Y., Ren, W., & Wang, X. (2025). Scientometric Analysis and Research Trends in Optical Fiber Grating Sensors: A Review. Photonics, 12(4), 349. https://doi.org/10.3390/photonics12040349

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