Research Progress in Marine Environmental Monitoring Technology

Authors

  • Botao Xie

    China National Offshore Oil Corporation Research Institute, Beijing 100024, China

  • Bigui Huang

    China National Offshore Oil Corporation Research Institute, Beijing 100024, China

  • Jiwen Song

    China National Offshore Oil Corporation Information Technology, Beijing 100010, China

  • Feida Zhao

    China National Offshore Oil Corporation Information Technology, Beijing 100010, China

DOI:

https://doi.org/10.30564/jees.v7i8.10742
Received: 30 June 2025 | Revised: 17 July 2025 | Accepted: 21 July 2025 | Published Online: 29 August 2025

Abstract

Marine environmental monitoring and data platform technology plays a pivotal role in advancing marine scientific research, sustainable resource development, ecological conservation, and the effective utilization of ocean resources. Despite its growing importance in addressing global environmental and economic challenges, a comprehensive and systematic review of recent advancements in this field remains lacking. To address this gap, this paper synthesizes and analyzes academic literature published between 2021 and 2025, sourced from reputable databases including Scopus and Web of Science, while adhering to the PRISMA systematic review standards. It delineates core technologies employed in marine environmental monitoring, such as advanced sensor systems, robust data acquisition and transmission methods, and innovative data processing and analysis techniques. Furthermore, the study examines the architectural functionalities, data sharing mechanisms, and interoperability standards that underpin modern marine data platforms. The paper also addresses critical technical challenges encountered in deep-water monitoring operations, including equipment durability under extreme conditions, significant economic constraints, data management complexities, and emerging privacy and security concerns. Finally, future development trajectories are outlined, emphasizing the transformative potential of novel materials and artificial intelligence (AI) in enhancing deep-water monitoring capabilities, alongside the urgent need for strengthened global collaboration to improve data sharing protocols and management frameworks. Collectively, the continuous evolution of marine monitoring technologies promises to provide increasingly intelligent, integrated, and systematic support for global marine protection efforts and sustainable resource stewardship.

Keywords:

Marine Environmental; Marine Monitoring; Sensor Technology; Underwater Platforms; Ocean Data; Underwater Communication

References

[1] Korpinen, S., Kahlert, M., Kuosa, H., et al., 2022. Marine monitoring in transition: On the verge of technological revolution? Frontiers in Marine Science. 9, 1066769. DOI: https://doi.org/10.3389/fmars.2022.1066769

[2] Sea-Bird Scientific. SBE CTDs Profiling. Available from: https://www.seabird.com/ (cited 30 April 2025).

[3] Xu, G., Shi, Y., Sun, X., et al., 2019. Internet of Things in Marine Environment Monitoring: A Review. Sensors. 19(7), 1711. DOI: https://doi.org/10.3390/s19071711

[4] Briciu-Burghina, C., Power, S., Delgado, A., et al., 2023. Sensors for Coastal and Ocean Monitoring. Annual Review of Analytical Chemistry. 16(1), 451–469. DOI: https://doi.org/10.1146/annurev-anchem-091922-085746

[5] Yuan, S., Li, Y., Bao, F., et al., 2023. Marine environmental monitoring with unmanned vehicle platforms: Present applications and future prospects. Science of The Total Environment. 858, 159741. DOI: https://doi.org/10.1016/j.scitotenv.2022.159741

[6] Su, R., Zhang, D., Li, C., et al., 2019. Localization and Data Collection in AUV-Aided Underwater Sensor Networks: Challenges and Opportunities. IEEE Network. 33(6), 86–93. DOI: https://doi.org/10.1109/MNET.2019.1800425

[7] Sameer Babu, T.P., Ameer, P.M., David Koilpillai, R., 2023. Synchronization techniques for underwater acoustic communications. International Journal of Communication Systems. 36(15), e5563. DOI: https://doi.org/10.1002/dac.5563

[8] Zong, L., Wang, H., Luo, G., 2022. Transmission Control Over Satellite Network for Marine Environmental Monitoring System. IEEE Transactions on Intelligent Transportation Systems. 23(10), 19668–19675. DOI: https://doi.org/10.1109/TITS.2022.3145881

[9] Chen, Y., Ma, Q., Liu, C., et al., 2021. Research on marine environment monitoring based on Internet of things. DESALINATION AND WATER TREATMENT. 219, 71–76. DOI: https://doi.org/10.5004/dwt.2021.26874

[10] Diviacco, P., Nadali, A., Iurcev, M., et al., 2021. Underwater Noise Monitoring with Real-Time and Low-Cost Systems, (The CORMA Experience). Journal of Marine Science and Engineering. 9(4), 390. DOI: https://doi.org/10.3390/jmse9040390

[11] Alin, S.R., Newton, J.A., Feely, R.A., et al., 2024. A decade-long cruise time series (2008–2018) of physical and biogeochemical conditions in the southern Salish Sea, North America. Earth System Science Data. 16(2), 837–865. DOI: https://doi.org/10.5194/essd-16-837-2024

[12] Dehm, J., Le Gendre, R., Lal, M., et al., 2025. Water quality within the greater Suva urban marine environment through spatial analysis of nutrients and water properties. Marine Pollution Bulletin. 213, 117601. DOI: https://doi.org/10.1016/j.marpolbul.2025.117601

[13] Saucan, A.A., Win, M.Z., 2020. Information-Seeking Sensor Selection for Ocean-of-Things. IEEE Internet of Things Journal. 7(10), 10072–10088. DOI: https://doi.org/10.1109/JIOT.2020.2992509

[14] Zhang, C., Yu, C., Yuan, L., et al., 2022. Assessment of Conductivity-Temperature-Depth via multi-criteria approach: Regret theory based model on the pythagorean fuzzy environment. Ocean Engineering. 266, 112740. DOI: https://doi.org/10.1016/j.oceaneng.2022.112740

[15] Eick, D., Geyer, M., 2013. The RNA Polymerase II Carboxy-Terminal Domain (CTD) Code. Chemical Reviews. 113(11), 8456–8490. DOI: https://doi.org/10.1021/cr400071f

[16] Lu, S.-H., Li, Y., Wang, X., 2023. Soft, flexible conductivity sensors for ocean salinity monitoring. Journal of Materials Chemistry B. 11(31), 7334–7343. DOI: https://doi.org/10.1039/D3TB01167D

[17] Ji, H., Kim, S.-R., Kim, Y.-H., et al., 2010. Inactivation of the CTD phosphatase-like gene OsCPL1 enhances the development of the abscission layer and seed shattering in rice. The Plant Journal. 61(1), 96–106. DOI: https://doi.org/10.1111/j.1365-313X.2009.04039.x

[18] Han, J., Cheng, P., Wang, H., et al., 2014. MEMS-based Pt film temperature sensor on an alumina substrate. Materials Letters. 125, 224–226. DOI: https://doi.org/10.1016/j.matlet.2014.03.170

[19] Zhou, L., Yu,Y., Meng, Z., 2021. Review of fiber optic ocean conductivity-temperature-depth sensor. Laser & Optoelectronics Progress. 58(13), 1306019. DOI: https://doi.org/10.3788/LOP202158.1306019 (in Chinese).

[20] Wong, K.H., Jin, Y., Struhl, K., 2014. TFIIH Phosphorylation of the Pol II CTD Stimulates Mediator Dissociation from the Preinitiation Complex and Promoter Escape. Molecular Cell. 54(4), 601–612. DOI: https://doi.org/10.1016/j.molcel.2014.03.024

[21] Van Haren, H., Uchida, H., Yanagimoto, D., 2021. Further correcting pressure effects on SBE911 CTD-conductivity data from hadal depths. Journal of Oceanography. 77(1), 137–144. DOI: https://doi.org/10.1007/s10872-020-00565-3

[22] IDRONAUT. Ocean Seven 320 Plus WOCE-CTD. Available from: https://www.idronaut.it/multiparameter-ctds/oceanographic-ctds/os320plus-oceanographic-ctd(cited 30 April 2025).

[23] Rubio, A., Gomis, D., Jordà, G., et al., 2009. Estimating geostrophic and total velocities from CTD and ADCP data: Intercomparison of different methods. Journal of Marine Systems. 77(1–2), 61–76. DOI: https://doi.org/10.1016/j.jmarsys.2008.11.009

[24] Crescentini, M., Bennati, M., Tartagni, M., 2012. Design of integrated and autonomous conductivity–temperature–depth (CTD) sensors. AEU - International Journal of Electronics and Communications. 66(8), 630–635. DOI: https://doi.org/10.1016/j.aeue.2012.03.013

[25] Sea & Sun Technology. CTD Probes. Available from: https://www.sea-sun-tech.com/multiparameter-ctds/ (cited 30 April 2025).

[26] Tsurumi Seiki Co. eXpendable Conductivity, Temperature and Depth system (XCTD). Available from: https://tsurumi-seiki.co.jp/en/product/e-sku-2/ (cited 30 April 2025).

[27] Lauer, J.W., Klinger, P., O’Shea, S., et al., 2023. Development and validation of an open-source four-pole electrical conductivity, temperature, depth sensor for in situ water quality monitoring in an estuary. Environmental Monitoring and Assessment. 195(1), 221. DOI: https://doi.org/10.1007/s10661-022-10493-y

[28] Schwer, B., Sanchez, A.M., Shuman, S., 2012. Punctuation and syntax of the RNA polymerase II CTD code in fission yeast. Proceedings of the National Academy of Sciences. 109(44), 18024–18029. DOI: https://doi.org/10.1073/pnas.1208995109

[29] Qian, Y., Zhao, Y., Wu, Q., et al., 2018. Review of salinity measurement technology based on optical fiber sensor. Sensors and Actuators B: Chemical. 260, 86–105. DOI: https://doi.org/10.1016/j.snb.2017.12.077

[30] Fadeev, K.M., Larionov, D.D., Zhikina, L.A., et al., 2020. A Fiber-Optic Sensor for Simultaneous Temperature and Pressure Measurements Based on a Fabry–Perot Interferometer and a Fiber Bragg Grating. Instruments and Experimental Techniques. 63(4), 543–546. DOI: https://doi.org/10.1134/S0020441220050024

[31] Bian, C., Wang, J., Bai, X., et al., 2020. Optical fiber based on humidity sensor with improved sensitivity for monitoring applications. Optics & Laser Technology. 130, 106342. DOI: https://doi.org/10.1016/j.optlastec.2020.106342

[32] Li, C., Ning, T., Wen, X., et al., 2015. Magnetic field and temperature sensor based on a no-core fiber combined with a fiber Bragg grating. Optics & Laser Technology. 72, 104–107. DOI: https://doi.org/10.1016/j.optlastec.2015.03.014

[33] Li, X., Zhou, X., Zhao, Y., et al., 2018. Multi-modes interferometer for magnetic field and temperature measurement using Photonic crystal fiber filled with magnetic fluid. Optical Fiber Technology. 41, 1–6. DOI: https://doi.org/10.1016/j.yofte.2017.12.002

[34] Minato, H., Kakui, Y., Nishimoto, A., et al., 1989. Remote refractive index difference meter for salinity sensor. IEEE Transactions on Instrumentation and Measurement. 38(2), 608–612. DOI: https://doi.org/10.1109/19.192359

[35] Bergh, Ø., Danre, J.-B., Stensland, K., et al., 2024. A Modular Smart Ocean Observatory for Development of Sensors, Underwater Communication and Surveillance of Environmental Parameters. Sensors. 24(20), 6530. DOI: https://doi.org/10.3390/s24206530

[36] Ertekin, R.C., Rodenbusch, G., 2016. Wave, Current and Wind Loads, in: Dhanak, M.R., Xiros, N.I. (Eds.). Springer Handbook of Ocean Engineering. Springer International Publishing: Cham, Switzerland. pp. 787–818. DOI: https://doi.org/10.1007/978-3-319-16649-0_35

[37] Abdullah, M.A., Chuah, L.F., Zakariya, R., et al., 2024. Evaluating climate change impacts on reef environments via multibeam echosounder and Acoustic Doppler Current profiler technology. Environmental Research. 252, 118858. DOI: https://doi.org/10.1016/j.envres.2024.118858

[38] Creane, S., O’Shea, M., Coughlan, M., et al., 2025. The Estimation of Suspended Solids Concentration from an Acoustic Doppler Current Profiler in a Tidally Dominated Continental Shelf Sea Setting and Its Use as a Numerical Modelling Validation Technique. Water. 17(12), 1788. DOI: https://doi.org/10.3390/w17121788

[39] Xia, Q., Chen, B., Sun, X., et al., 2022. Research on the Depth Control Strategy of an Underwater Profiler Driven by a Mixture of Ocean Thermal Energy and Electric Energy. Journal of Marine Science and Engineering. 10(5), 640. DOI: https://doi.org/10.3390/jmse10050640

[40] Lyle, J.H., Pitt, C.W., 1981. Vortex shedding fluid flowmeter using optical fibre sensor. Electronics Letters. 17(6), 244–245. DOI: https://doi.org/10.1049/el:19810173

[41] Ardhuin, F., Aksenov, Y., Benetazzo, A., et al., 2018. Measuring currents, ice drift, and waves from space: the Sea surface KInematics Multiscale monitoring (SKIM) concept. Ocean Science. 14(3), 337–354. DOI: https://doi.org/10.5194/os-14-337-2018

[42] Yang, C.-T., Wu, M.-C., Chuang, H.-S., 2002. Adjustment and evaluation of an LDA probe for accurate flow measurement. Optics and Lasers in Engineering. 38(5), 291–304. DOI: https://doi.org/10.1016/S0143-8166(01)00152-X

[43] Li, H.-N., Li, D.-S., Song, G.-B., 2004. Recent applications of fiber optic sensors to health monitoring in civil engineering. Engineering Structures. 26(11), 1647–1657. DOI: https://doi.org/10.1016/j.engstruct.2004.05.018

[44] Wang, S.M., Wang, Z., He, Q.Y., et al., 2014. Structure Design of the ANSYS-Based SLC9-2-Type Direct Reading Current Meter. Advanced Materials Research. 926–930, 1412–1416. DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.1412

[45] Chen, S., Wu, Y., Liu, S., et al., 2023. Development of Electromagnetic Current Meter for Marine Environment. Journal of Marine Science and Engineering. 11(1), 206. DOI: https://doi.org/10.3390/jmse11010206

[46] Linnert, M.A., Mariager, S.O., Rupitsch, S.J., et al., 2019. Dynamic Offset Correction of Electromagnetic Flowmeters. IEEE Transactions on Instrumentation and Measurement. 68(5), 1284–1293. DOI: https://doi.org/10.1109/TIM.2018.2880942

[47] Li, B., Fan, X., Chen, J., et al., 2021. Study on the Mechanism of Excitation Switching Process in Electromagnetic Flowmeter to Overcome Slurry Noise. IEEE Sensors Journal. 21(7), 9023–9037. DOI: https://doi.org/10.1109/JSEN.2021.3053988

[48] Thierry, N.N.B., Tang, H., Xu, L., et al., 2021. Identifying the turbulent flow developing inside and around the bottom trawl by Electromagnetic Current Velocity Meter approach in the flume tank. Journal of Hydrodynamics. 33(3), 636–656. DOI: https://doi.org/10.1007/s42241-021-0058-0

[49] Hu, C., Li, X., Ji, C., et al., 2023. In-situ observation of seabed vertical deformation in Yellow River Delta under storm surges. Marine and Petroleum Geology. 152, 106250. DOI: https://doi.org/10.1016/j.marpetgeo.2023.106250

[50] Watral, Z., Jakubowski, J., Michalski, A., 2015. Electromagnetic flow meters for open channels: Current state and development prospects. Flow Measurement and Instrumentation. 42, 16–25. DOI: https://doi.org/10.1016/j.flowmeasinst.2015.01.003

[51] Dunn, M., Zedel, L., 2022. Evaluation of discrete target detection with an acoustic Doppler current profiler. Limnology and Oceanography: Methods. 20(5), 249–259. DOI: https://doi.org/10.1002/lom3.10484

[52] Muste, M., Yu, K., Spasojevic, M., 2004. Practical aspects of ADCP data use for quantification of mean river flow characteristics; Part I: moving-vessel measurements. Flow Measurement and Instrumentation. 15(1), 1–16. DOI: https://doi.org/10.1016/j.flowmeasinst.2003.09.001

[53] Bogdanov, S., Zdorovennov, R., Palshin, N., et al., 2021. Deriving Six Components of Reynolds Stress Tensor from Single-ADCP Data. Water. 13(17), 2389. DOI: https://doi.org/10.3390/w13172389

[54] Voulgaris, G., Trowbridge, J.H., 1998. Evaluation of the Acoustic Doppler Velocimeter (ADV) for Turbulence Measurements*. Journal of Atmospheric and Oceanic Technology. 15(1), 272–289. DOI: https://doi.org/10.1175/1520-0426(1998)015%253C0272:EOTADV%253E2.0.CO;2

[55] Vagle, S., Burnham, R.E., O’Neill, C., et al., 2021. Variability in Anthropogenic Underwater Noise Due to Bathymetry and Sound Speed Characteristics. Journal of Marine Science and Engineering. 9(10), 1047. DOI: https://doi.org/10.3390/jmse9101047

[56] Yazdanshenasshad, B., Safizadeh, M.S., 2019. Reducing the additional error caused by the time‐difference method in transit‐time UFMs. IET Science, Measurement & Technology. 13(6), 895–902. DOI: https://doi.org/10.1049/iet-smt.2018.5106

[57] Teledyne Marine. Workhorse II Monitor ADCP. Available from: https://www.teledynemarine.com/products/workhorse-monitor-adcp (cited 7 May 2025).

[58] Nobsk a Development Corporation. MAVS Series. Available from: https://nobska.net/page4/page18/index.html (cited 7 May 2025).

[59] Kumari, C.R.U., Samiappan, D., R., K., et al., 2019. Fiber optic sensors in ocean observation: A comprehensive review. Optik. 179, 351–360. DOI: https://doi.org/10.1016/j.ijleo.2018.10.186

[60] Klishina, V.A., Varzhel, S.V., Loseva, E.A., 2023. Method for simultaneous measurement of velocity and direction of fluid flow using fiber Bragg gratings. Optical Fiber Technology. 75, 103215. DOI: https://doi.org/10.1016/j.yofte.2022.103215

[61] Zhao, J., Zhao, Y., Peng, Y., et al., 2022. Review of femtosecond laser direct writing fiber-optic structures based on refractive index modification and their applications. Optics & Laser Technology. 146, 107473. DOI: https://doi.org/10.1016/j.optlastec.2021.107473

[62] Liu, Z., Tse, M.-L.V., Zhang, A.P., et al., 2014. Integrated microfluidic flowmeter based on a micro-FBG inscribed in Co^2+-doped optical fiber. Optics Letters. 39(20), 5877. DOI: https://doi.org/10.1364/OL.39.005877

[63] Gao, R., Lu, D., 2019. Temperature compensated fiber optic anemometer based on graphene-coated elliptical core micro-fiber Bragg grating. Optics Express. 27(23), 34011. DOI: https://doi.org/10.1364/OE.27.034011

[64] Gupta, H., Arumuru, V., Jha, R., 2021. Industrial Fluid Flow Measurement Using Optical Fiber Sensors: A Review. IEEE Sensors Journal. 21(6), 7130–7144. DOI: https://doi.org/10.1109/JSEN.2020.3045506

[65] Novikova, V.A., Varzhel, S.V., Tokareva, I.D., et al., 2020. Liquid flow motion rate measuring method, based on the fiber Bragg gratings. Optical and Quantum Electronics. 52(3), 132. DOI: https://doi.org/10.1007/s11082-020-2257-2

[66] Lv, R., Zheng, H., Zhao, Y., et al., 2018. An optical fiber sensor for simultaneous measurement of flow rate and temperature in the pipeline. Optical Fiber Technology. 45, 313–318. DOI: https://doi.org/10.1016/j.yofte.2018.08.003

[67] Ding, M., Zhang, T., Wang, R., et al., 2023. A Low-Flow Fiber-Optic Flowmeter Based on Bending Measuring Using a Cladding Fiber Bragg Grating. IEEE Sensors Journal. 23(4), 3609–3614. DOI: https://doi.org/10.1109/JSEN.2023.3233959

[68] Liu, C., Zhang, Z., Li, H., et al., 2016. Research on one-piece structure target flow sensing technology based on fiber Bragg grating. Photonic Sensors. 6(4), 303–311. DOI: https://doi.org/10.1007/s13320-016-0352-6

[69] Zhang, H., Zhong, Z., Duan, J., et al., 2021. Design of Flow Velocity and Direction Monitoring Sensor Based on Fiber Bragg Grating. Sensors. 21(14), 4925. DOI: https://doi.org/10.3390/s21144925

[70] Hou, B., Yin, B., Wang, M., et al., 2022. Differential Fiber Grating Vector Flow Velocity Sensor Based on Strain Amplifying Cantilever Beam Structure. IEEE Sensors Journal. 22(23), 22678–22690. DOI: https://doi.org/10.1109/JSEN.2022.3216727

[71] Tsabaris, C., Androulakaki, E.G., Ballas, D., et al., 2021. Radioactivity Monitoring at North Aegean Sea Integrating In-Situ Sensor in an Ocean Observing Platform. Journal of Marine Science and Engineering. 9(1), 77. DOI: https://doi.org/10.3390/jmse9010077

[72] Martinez-Manuel, R., Esquivel-Hernandez, J., LaRochelle, S., 2022. Nonlinearity Reduction in a Fiber Fabry-Perot Interferometer Interrogated by a Wavelength Scanning Optical Source. IEEE Sensors Journal. 22(10), 9433–9439. DOI: https://doi.org/10.1109/JSEN.2022.3164808

[73] Ma, C., Peng, D., Bai, X., et al., 2023. A Review of Optical Fiber Sensing Technology Based on Thin Film and Fabry–Perot Cavity. Coatings. 13(7), 1277. DOI: https://doi.org/10.3390/coatings13071277

[74] Li, J., Tong, Z., Jing, L., et al., 2020. Fiber temperature and humidity sensor based on photonic crystal fiber coated with graphene oxide. Optics Communications. 467, 125707. DOI: https://doi.org/10.1016/j.optcom.2020.125707

[75] Xu, B., Zhao, Q., Duan, X., et al., 2023. Versatile Optofluidic Fabry-Perot Sensor for Multiple Physical Parameters in Microfluidic Chips. Journal of Lightwave Technology. 41(17), 5788–5795. DOI: https://doi.org/10.1109/JLT.2023.3268117

[76] Xie, Z., He, X., Xiao, Y., et al., 2020. Ultrasensitive all-fiber inline Fabry–Perot strain sensors for aerodynamic measurements in hypersonic flows. ISA Transactions. 102, 388–396. DOI: https://doi.org/10.1016/j.isatra.2020.02.020

[77] Islam, Md., Ali, M., Lai, M.-H., et al., 2014. Chronology of Fabry-Perot Interferometer Fiber-Optic Sensors and Their Applications: A Review. Sensors. 14(4), 7451–7488. DOI: https://doi.org/10.3390/s140407451

[78] Zhou, B., Jiang, H., Lu, C., et al., 2016. Hot Cavity Optical Fiber Fabry–Perot Interferometer as a Flow Sensor With Temperature Self-Calibrated. Journal of Lightwave Technology. 34(21), 5044–5048. DOI: https://doi.org/10.1109/JLT.2016.2612657

[79] Costa, J.W., Franco, M.A.R., Serrão, V.A., et al., 2019. Macrobending SMS fiber-optic anemometer and flow sensor. Optical Fiber Technology. 52, 101981. DOI: https://doi.org/10.1016/j.yofte.2019.101981

[80] Liu, G., Sheng, Q., Hou, W., et al., 2016. Optical fiber vector flow sensor based on a silicon Fabry–Perot interferometer array. Optics Letters. 41(20), 4629. DOI: https://doi.org/10.1364/OL.41.004629

[81] Zhang, T., Guo, T., Wang, R., et al., 2021. A hot-wire flowmeter based on fiber Extrinsic Fabry–Pérot Interferometer with assistance of fiber Bragg grating. Optics Communications. 497, 126952. DOI: https://doi.org/10.1016/j.optcom.2021.126952

[82] Moreira, D.L., Dalto, A.G., Figueiredo Jr., A.G., et al., 2023. Multidisciplinary Scientific Cruises for Environmental Characterization in the Santos Basin – Methods and Sampling Design. Ocean and Coastal Research. 71(suppl 3), e23022. DOI: https://doi.org/10.1590/2675-2824071.22072dlm

[83] Zhao, Y., Zhao, H., Lv, R., et al., 2019. Review of optical fiber Mach–Zehnder interferometers with micro-cavity fabricated by femtosecond laser and sensing applications. Optics and Lasers in Engineering. 117, 7–20. DOI: https://doi.org/10.1016/j.optlaseng.2018.12.013

[84] Yuan, L., Yang, J., Liu, Z., 2008. A Compact Fiber-Optic Flow Velocity Sensor Based on a Twin-Core Fiber Michelson Interferometer. IEEE Sensors Journal. 8(7), 1114–1117. DOI: https://doi.org/10.1109/JSEN.2008.926873

[85] Hou, L., Li, Y., Liu, Y., et al., 2021. High Sensitivity Flow Velocity Sensor Based on All-Fiber Target-Type Structure. Journal of Lightwave Technology. 39(12), 4174–4178. DOI: https://doi.org/10.1109/JLT.2020.3034252

[86] Sun, Z., Wu, S., Shuai, S., et al., 2019. Cascaded bowknot-type taper based Mach–Zehnder interferometer for microfluidic flow rate sensing. Optical Fiber Technology. 48, 12–14. DOI: https://doi.org/10.1016/j.yofte.2018.12.006

[87] Chen, S., Zhu, K., Han, J., et al., 2022. Photonic Integrated Sensing and Communication System Harnessing Submarine Fiber Optic Cables for Coastal Event Monitoring. IEEE Communications Magazine. 60(12), 110–116. DOI: https://doi.org/10.1109/MCOM.002.2200191

[88] Sun, Y., Cao, S., Xu, H., et al., 2020. Application of Distributed Fiber Optic Sensing Technique to Monitor Stability of a Geogrid-Reinforced Model Slope. International Journal of Geosynthetics and Ground Engineering. 6(2), 29. DOI: https://doi.org/10.1007/s40891-020-00209-y

[89] Jia, S., Yang, X., Feng, W., et al., 2025. Self‐Adaptive Gyroscope‐Structured Hybrid Triboelectric‐Electromagnetic Buoy System for Real‐Time Ocean Currents Monitoring. Small. 21(20), 2501073. DOI: https://doi.org/10.1002/smll.202501073

[90] Zhao, L., Meng, W., Zheng, Z., et al., 2020. Nonlinear Dynamics Behavior of Tethered Submerged Buoy under Wave Loadings. International Journal of Nonlinear Sciences and Numerical Simulation. 21(1), 11–21. DOI: https://doi.org/10.1515/ijnsns-2018-0009

[91] Sonardyne. Pressure Inverted Echo Sounder (PIES). Available from: https://www.sonardyne.com/products/pressure-inverted-echo-sounder/ (cited 7 May 2025).

[92] Kongsberg Maritime. cNODE Transponders. Available from: https://www.kongsberg.com/discovery/navigation-positioning/cnode-transponder/(cited 7 May 2025).

[93] Ding, B., Cazzolato, B.S., Arjomandi, M., et al., 2016. Sea-state based maximum power point tracking damping control of a fully submerged oscillating buoy. Ocean Engineering. 126, 299–312. DOI: https://doi.org/10.1016/j.oceaneng.2016.09.020

[94] Hu, Y., Yang, S., He, H., et al., 2019. Influence of Central Platform on Hydrodynamic Performance of Semi-Submerged Multi-Buoy Wave Energy Converter. Journal of Marine Science and Engineering. 8(1), 12. DOI: https://doi.org/10.3390/jmse8010012

[95] Li, C.-Y., Weng, W.-K., Shih, R.-S., et al., 2019. Enhancing Wave Energy Harvesting with a Submerged Crescent-Shaped Plate. Journal of Coastal Research. 35(5), 985. DOI: https://doi.org/10.2112/JCOASTRES-D-18-00093.1

[96] Sathyendranath, S. (Eds.), 2000. Remote sensing of ocean colour in coastal, and other optically-complex, waters. DOI: https://doi.org/10.25607/OBP-95

[97] TECHNICAP. TBM 1.76 (Structures for ADCP). Available from: https://www.technicap.com/products/structures-for-adcp (cited 7 May 2025).

[98] Mooring Systems, Inc. Bottom Mount Systems for ADCPs. Available from: https://www.environmental-expert.com/products/bottom-mount-systems-for-adcps-954589 (cited 7 May 2025).

[99] DeepWater Buoyancy Inc. DeepWater Buoyancy buoys. Available from: https://www.deepwaterbuoyancy.com/ (cited 7 May 2025).

[100] Bao, L., Zeng, Q., Zhu, Z., et al., 2019. AUV Docking Recovery Based on USBL Integrated Navigation Method. In Proceeding of the 2019 Chinese Automation Congress (CAC), Hangzhou, China, Novemver, 2019; pp. 5804–5809. DOI: https://doi.org/10.1109/CAC48633.2019.8996455

[101] Mazzeo, A., Aguzzi, J., Calisti, M., et al., 2022. Marine Robotics for Deep-Sea Specimen Collection: A Systematic Review of Underwater Grippers. Sensors. 22(2), 648. DOI: https://doi.org/10.3390/s22020648

[102] Dalhatu, A. A., de Azevedo, R. C., Udebhulu, O. D., et al., 2021. Recent developments of remotely operated vehicle in the oil and gas industry. Holos. 3, 1-18. Available from: https://www.researchgate.net/publication/370865268_RECENT_DEVELOPMENTS_OF_REMOTELY_OPERATED_VEHICLE_IN_THE_OIL_AND_GAS_INDUSTRY (cited 7 May 2025).

[103] Chellapurath, M., Walker, K.L., Donato, E., et al., 2022. Analysis of Station Keeping Performance of an Underwater Legged Robot. IEEE/ASME Transactions on Mechatronics. 27(5), 3730–3741. DOI: https://doi.org/10.1109/TMECH.2021.3132779

[104] Eldesouky, E., Bekhit, M., Fathalla, A., et al., 2021. A Robust UWSN Handover Prediction System Using Ensemble Learning. Sensors. 21(17), 5777. DOI: https://doi.org/10.3390/s21175777

[105] Hao, K., Ding, Y., Li, C., et al., 2021. An Energy-Efficient Routing Void Repair Method Based on an Autonomous Underwater Vehicle for UWSNs. IEEE Sensors Journal. 21(4), 5502–5511. DOI: https://doi.org/10.1109/JSEN.2020.3030019

[106] NEWS, M. T. Pioneer Work Class ROVs (CURV-I). Marine Technology News. Available from: https://www.marinetechnologynews.com/blogs/pioneer-work-class-rovs-%28curv-i-iii%29-e28093-part-1-700495(cited 30 May 2025).

[107] SMD. Quantum® Work Class ROV. Available from: https://www.smd.co.uk/our-products/work-class-rovs/quantum-work-class-rov/ (cited 30 May 2025).

[108] Soil Machine Dynamics Ltd. Atom® Compact Work Class ROV. Available from: https://www.smd.co.uk/our-products/work-class-rovs/atom-work-class-rov/ (cited 30 May 2025).

[109] Odetti, A., Bibuli, M., Bruzzone, Giorgio, et al., 2017. e-URoPe: a reconfgurable AUV/ROV for man-robot underwater cooperation. IFAC-PapersOnLine. 50(1), 11203–11208. DOI: https://doi.org/10.1016/j.ifacol.2017.08.2089

[110] Talkington, H., 1983. History And Accomplishments In Ocean Engineering At The Naval Ocean Systems Center, 1966-1983. In Proceedings of the OCEANS ’83, San Francisco, CA, USA, 1983; pp. 384–387. DOI: https://doi.org/10.1109/OCEANS.1983.1152047

[111] Ratmeyer, V., Rigaud, V., 2009. Europe’s growing fleet of scientific deepwater ROVs: Emerging demands for interchange, workflow enhancement and training. In Proceeding of the OCEANS 2009-EUROPE, Bremen, Germany, May 2009; pp. 1–6. DOI: https://doi.org/10.1109/OCEANSE.2009.5278157

[112] Brantner, G., Khatib, O., 2021. Controlling Ocean One: Human–robot collaboration for deep‐sea manipulation. Journal of Field Robotics. 38(1), 28–51. DOI: https://doi.org/10.1002/rob.21960

[113] Khatib, O., Yeh, X., Brantner, G., et al., 2016. Ocean One: A Robotic Avatar for Oceanic Discovery. IEEE Robotics & Automation Magazine. 23(4), 20–29. DOI: https://doi.org/10.1109/MRA.2016.2613281

[114] Saeed, N., Alouini, M.-S., Al-Naffouri, T.Y., 2020. Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things. IEEE Internet of Things Journal. 7(2), 937–947. DOI: https://doi.org/10.1109/JIOT.2019.2946270

[115] Khan, M.T.R., Ahmed, S.H., Kim, D., 2019. AUV-Aided Energy-Efficient Clustering in the Internet of Underwater Things. IEEE Transactions on Green Communications and Networking. 3(4), 1132–1141. DOI: https://doi.org/10.1109/TGCN.2019.2922278

[116] Jahanbakht, M., Xiang, W., Hanzo, L., et al., 2021. Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey. IEEE Communications Surveys & Tutorials. 23(2), 904–956. DOI: https://doi.org/10.1109/COMST.2021.3053118

[117] Jiang, B., Feng, J., Cui, X., et al., 2025. Security and Reliability of Internet of Underwater Things: Architecture, Challenges, and Opportunities. ACM Computing Surveys. 57(3), 1–37. DOI: https://doi.org/10.1145/3700640

[118] Liu, S., Zhu, L., Huang, F., et al., 2023. A Survey on Air-to-Sea Integrated Maritime Internet of Things: Enabling Technologies, Applications, and Future Challenges. Journal of Marine Science and Engineering. 12(1), 11. DOI: https://doi.org/10.3390/jmse12010011

[119] Wang, Y., Liu, Y., Guo, Z., 2012. Three-dimensional ocean sensor networks: A survey. Journal of Ocean University of China. 11(4), 436–450. DOI: https://doi.org/10.1007/s11802-012-2111-7

[120] Al-Dharrab, S., Uysal, M., Duman, T.M., 2013. Cooperative underwater acoustic communications [Accepted From Open Call]. IEEE Communications Magazine. 51(7), 146–153. DOI: https://doi.org/10.1109/MCOM.2013.6553691

[121] Domingo, M.C., 2012. An overview of the internet of underwater things. Journal of Network and Computer Applications. 35(6), 1879–1890. DOI: https://doi.org/10.1016/j.jnca.2012.07.012

[122] Amaechi, C.V., Wang, F., Ye, J., 2022. Experimental Study on Motion Characterisation of CALM Buoy Hose System under Water Waves. Journal of Marine Science and Engineering. 10(2), 204. DOI: https://doi.org/10.3390/jmse10020204

[123] Xu, Y., Wang, J., Guan, L., 2021. Application research of narrow band Internet of things buoy and surface hydrodynamics monitoring. Acta Oceanologica Sinica. 40(8), 176–181. DOI: https://doi.org/10.1007/s13131-021-1884-1

[124] Glaviano, F., Esposito, R., Cosmo, A.D., et al., 2022. Management and Sustainable Exploitation of Marine Environments through Smart Monitoring and Automation. Journal of Marine Science and Engineering. 10(2), 297. DOI: https://doi.org/10.3390/jmse10020297

[125] Ntoumas, M., Perivoliotis, L., Petihakis, G., et al., 2022. The POSEIDON Ocean Observing System: Technological Development and Challenges. Journal of Marine Science and Engineering. 10(12), 1932. DOI: https://doi.org/10.3390/jmse10121932

[126] Ying, F., Zhao, S., Wang, J., 2024. A Security Information Transmission Method Based on DHR for Seafloor Observation Network. Sensors. 24(4), 1147. DOI: https://doi.org/10.3390/s24041147

[127] Gopi, S., Govindan, K., Chander, D., et al., 2010. E-PULRP: Energy Optimized Path Unaware Layered Routing Protocol for Underwater Sensor Networks. IEEE Transactions on Wireless Communications. 9(11), 3391–3401. DOI: https://doi.org/10.1109/TWC.2010.091510.090452

[128] Wahid, A., Lee, S., Jeong, H.-J., et al., 2011. EEDBR: Energy-Efficient Depth-Based Routing Protocol for Underwater Wireless Sensor Networks, in: Kim, T., Adeli, H., Robles, R.J., et al. (Eds.), Advanced Computer Science and Information Technology, Communications in Computer and Information Science. Springer: Berlin, Germany. pp. 223–234. DOI: https://doi.org/10.1007/978-3-642-24267-0_27

[129] Wang, K., Gao, H., Xu, X., et al., 2016. An Energy-Efficient Reliable Data Transmission Scheme for Complex Environmental Monitoring in Underwater Acoustic Sensor Networks. IEEE Sensors Journal. 16(11), 4051–4062. DOI: https://doi.org/10.1109/JSEN.2015.2428712

[130] Han, G., Shen, S., Song, H., et al., 2018. A Stratification-Based Data Collection Scheme in Underwater Acoustic Sensor Networks. IEEE Transactions on Vehicular Technology. 67(11), 10671–10682. DOI: https://doi.org/10.1109/TVT.2018.2867021

[131] Fang, Z., Wang, J., Jiang, C., et al., 2021. AoI-Inspired Collaborative Information Collection for AUV-Assisted Internet of Underwater Things. IEEE Internet of Things Journal. 8(19), 14559–14571. DOI: https://doi.org/10.1109/JIOT.2021.3049239

[132] Gjanci, P., Petrioli, C., Basagni, S., et al., 2018. Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks. IEEE Transactions on Mobile Computing. 17(2), 404–418. DOI: https://doi.org/10.1109/TMC.2017.2706689

[133] Duan, R., Du, J., Jiang, C., et al., 2020. Value-Based Hierarchical Information Collection for AUV-Enabled Internet of Underwater Things. IEEE Internet of Things Journal. 7(10), 9870–9883. DOI: https://doi.org/10.1109/JIOT.2020.2994909

[134] Duan, R., Wang, J., Jiang, C., et al., 2019. Resource Allocation for Multi-UAV Aided IoT NOMA Uplink Transmission Systems. IEEE Internet of Things Journal. 6(4), 7025–7037. DOI: https://doi.org/10.1109/JIOT.2019.2913473

[135] Yang, G., Dai, R., Liang, Y.-C., 2021. Energy-Efficient UAV Backscatter Communication With Joint Trajectory Design and Resource Optimization. IEEE Transactions on Wireless Communications. 20(2), 926–941. DOI: https://doi.org/10.1109/TWC.2020.3029225

[136] Wang, Qubeijian, Dai, H.-N., Wang, Qiu, et al., 2020. On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things. IEEE Internet of Things Journal. 7(6), 5371–5385. DOI: https://doi.org/10.1109/JIOT.2020.2979691

[137] Ma, R., Wang, R., Liu, G., et al., 2021. UAV-Aided Cooperative Data Collection Scheme for Ocean Monitoring Networks. IEEE Internet of Things Journal. 8(17), 13222–13236. DOI: https://doi.org/10.1109/JIOT.2021.3065740

[138] Ju, H., Zhang, R., 2014. Throughput Maximization in Wireless Powered Communication Networks. IEEE Transactions on Wireless Communications. 13(1), 418–428. DOI: https://doi.org/10.1109/TWC.2013.112513.130760

[139] Gautam, S., Lagunas, E., Chatzinotas, S., et al., 2019. Relay Selection and Resource Allocation for SWIPT in Multi-User OFDMA Systems. IEEE Transactions on Wireless Communications. 18(5), 2493–2508. DOI: https://doi.org/10.1109/TWC.2019.2904273

[140] Pensieri, S., Viti, F., Moser, G., et al., 2021. Evaluating LoRaWAN Connectivity in a Marine Scenario. Journal of Marine Science and Engineering. 9(11), 1218. DOI: https://doi.org/10.3390/jmse9111218

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How to Cite

Xie, B., Huang, B., Song, J., & Zhao, F. (2025). Research Progress in Marine Environmental Monitoring Technology. Journal of Environmental & Earth Sciences, 7(8), 376–407. https://doi.org/10.30564/jees.v7i8.10742