Emerging trends in IoT for aquatic systems: a systematic literature review

Climate change, pollution, and the overexploitation of water resources have intensified global water scarcity, particularly in arid and semi-arid regions. This systematic literature review analyzes 458 peer-reviewed articles published between 2015 and 2025 to identify the main IoT-based technologica...

Full description

Autores:: Cohen Manrique, Carlos
Camacho-Leon, Sergio
Villa Ramírez, José Luis

Tipo de recurso:

Fecha de publicación:: 2025

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

id	UTB2_5a890728d3f23ffea09bb936a78596a6
oai_identifier_str	oai:repositorio.utb.edu.co:20.500.12585/14293
network_acronym_str	UTB2
network_name_str	Repositorio Institucional UTB
repository_id_str
dc.title.none.fl_str_mv	Emerging trends in IoT for aquatic systems: a systematic literature review
title	Emerging trends in IoT for aquatic systems: a systematic literature review
spellingShingle	Emerging trends in IoT for aquatic systems: a systematic literature review 330 - Economía::333 - Economía de la tierra y de la energía Internet of Things sensor fusion systematic literature review water quality water resources Recursos hídricos -- Gestión Escasez de agua Cambio climático Internet de las cosas Monitoreo ambiental Water Resources -- Management Water Scarcity Climate Change Internet of Things Environmental Monitoring 2. Ingeniería y Tecnología ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
title_short	Emerging trends in IoT for aquatic systems: a systematic literature review
title_full	Emerging trends in IoT for aquatic systems: a systematic literature review
title_fullStr	Emerging trends in IoT for aquatic systems: a systematic literature review
title_full_unstemmed	Emerging trends in IoT for aquatic systems: a systematic literature review
title_sort	Emerging trends in IoT for aquatic systems: a systematic literature review
dc.creator.fl_str_mv	Cohen Manrique, Carlos Camacho-Leon, Sergio Villa Ramírez, José Luis
dc.contributor.author.none.fl_str_mv	Cohen Manrique, Carlos Camacho-Leon, Sergio Villa Ramírez, José Luis
dc.contributor.researchgroup.none.fl_str_mv	Grupo de Investigación Automatización Industrial y Control (GAICO)
dc.contributor.seedbeds.none.fl_str_mv	Semillero de Investigación en Automatización y Control
dc.subject.ddc.none.fl_str_mv	330 - Economía::333 - Economía de la tierra y de la energía
topic	330 - Economía::333 - Economía de la tierra y de la energía Internet of Things sensor fusion systematic literature review water quality water resources Recursos hídricos -- Gestión Escasez de agua Cambio climático Internet de las cosas Monitoreo ambiental Water Resources -- Management Water Scarcity Climate Change Internet of Things Environmental Monitoring 2. Ingeniería y Tecnología ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
dc.subject.proposal.none.fl_str_mv	Internet of Things sensor fusion systematic literature review water quality water resources
dc.subject.lemb.none.fl_str_mv	Recursos hídricos -- Gestión Escasez de agua Cambio climático Internet de las cosas Monitoreo ambiental Water Resources -- Management Water Scarcity Climate Change Internet of Things Environmental Monitoring
dc.subject.ocde.none.fl_str_mv	2. Ingeniería y Tecnología
dc.subject.ods.none.fl_str_mv	ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
description	Climate change, pollution, and the overexploitation of water resources have intensified global water scarcity, particularly in arid and semi-arid regions. This systematic literature review analyzes 458 peer-reviewed articles published between 2015 and 2025 to identify the main IoT-based technological strategies applied to the monitoring and management of surface and groundwater systems. Following PRISMA guidelines, the studies were categorized into four thematic areas: IoT applications in aquatic environments, data transmission technologies, algorithms for process optimization and data analysis, and sensor fusion techniques. The results show that LoRa is the most widely adopted transmission technology due to its long-range coverage, scalability, and low energy consumption. Emerging innovations such as remote IoT, satellite-assisted sensing, and digital twins are also gaining relevance as transformative tools for real-time hydrological monitoring. Overall, the findings reveal a shift toward more integrated and intelligent IoT frameworks and include a recommended architecture for aquatic systems. Despite these advancements, the review highlights the need for more accessible, affordable, and interoperable IoT solutions to enable broader adoption, particularly in resource-constrained regions, and to support sustainable water resource management.
publishDate	2025
dc.date.available.none.fl_str_mv	2025-12-04
dc.date.issued.none.fl_str_mv	2025-12-04
dc.date.accessioned.none.fl_str_mv	2026-01-16T21:19:54Z
dc.type.none.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/ART
dc.type.content.none.fl_str_mv	Text
dc.type.coarversion.none.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.identifier.citation.none.fl_str_mv	Cohen-Manrique C, Camacho-Leon S and Villa JL (2025) Emerging trends in IoT for aquatic systems: a systematic literature review. Front. Water 7:1699240. doi: 10.3389/frwa.2025.1699240
dc.identifier.other.none.fl_str_mv	doi: 10.3389/frwa.2025.1699240
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/14293
identifier_str_mv	Cohen-Manrique C, Camacho-Leon S and Villa JL (2025) Emerging trends in IoT for aquatic systems: a systematic literature review. Front. Water 7:1699240. doi: 10.3389/frwa.2025.1699240 doi: 10.3389/frwa.2025.1699240
url	https://hdl.handle.net/20.500.12585/14293
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.none.fl_str_mv	Abdulbaqi, A. G., and Hashim, Y. (2024). Design and implementation of Iot based rivers monitoring system. IEEJ Trans. Electr. Electron. Eng. 19, 469–474. doi: 10.1002/tee.23992 Addow, M., and Jimale, A. (2024). Integrating Iot and water quality: a bibliometric analysis. Int. J. Electron. Commun. Eng. 11, 149–160. doi: 10.14445/23488549/IJECE-V11I10P112 Adeleke, I. A., Nwulu, N. I., and Ogbolumani, O. A. (2023). A hybrid machine learning and embedded Iot-based water quality monitoring system. Internet Things 22:100774. doi: 10.1016/j.iot.2023.100774 Adriansyah, A., Budiutomo, M. H., Hermawan, H., Andriani, R. I., Sulistyawan, R., Shamsudin, A. U., et al. (2024). Design of water level detection monitoring system using fusion sensor based on internet of things (Iot). SINERGI 28, 191–198. doi: 10.22441/sinergi.2024.1.019 Ahmad, S., Uyanık, H., Ovatman, T., Sandıkkaya, M. T., Maio, V. D., Brandic, I., et al. (2023). Sustainable environmental monitoring via energy and ´ information efficient multi-node placement. IEEE Internet Things J. 10, 22065–22079. doi: 10.1109/JIot.2023.3303124 Aiche, A., Tardif, P. M., and Erritali, M. (2024). Modeling trust in Iot systems for drinking-water management. Future Internet 16:273. doi: 10.3390/fi16080273 Alghamdi, W., Alshamrani, R., Aloufi, R., and Lhamar, B. (2025). Exploring the synergy between digital twin technology and artificial intelligence: a comprehensive survey. Int. J. Adv. Comput. Sci. Appl. 16, 1–36. doi: 10.14569/IJACSA.2025.01 60399 Alhamam, N., Rahman, H., and Aljughaiman, A. (2025). A comprehensive review on cybersecurity of digital twins issues, challenges, and future research directions. IEEE Access 13, 45106–45124. doi: 10.1109/ACCESS.2025.3545004 Aliagas, C., Pérez-Foguet, A., Meseguer, R., Millán, P., and Molina, C. (2022). A lowcost and do-it-yourself device for pumping monitoring in deep aquifers. Electronics 11:3788. doi: 10.3390/electronics11223788 Alobaidy, I. A., Abdullah, N. F., Nordin, R., Behjati, M., Abu-Samah, A., Maizan, H., et al. (2024). Empowering extreme communication: propagation characterization of a lora-based internet of things network using hybrid machine learning. IEEE Open J. Commun. Soc. 5, 3997–4023. doi: 10.1109/OJCOMS.2024.3420229 Anker, C. (2023). Digital Twin Paradigms Towards Monitoring Insights for Deep Aquifer Pumps [PhD thesis]. Stellenbosch University, Stellenbosch. Ansari, V., and Kumar, V. (2025). Use of internet of things in water resources applications: challenges and future directions: a critical review. Discov. Internet Things 5, 1–58. doi: 10.1007/s43926-025-00193-7 Arias-Rodriguez, L. F., Duan, Z., Sepúlveda, R., Martinez-Martinez, S. I., and Disse, M. (2020). Monitoring water quality of Valle de Bravo reservoir, Mexico, using entire lifespan of Meris data and machine learning approaches. Remote Sens. 12:1586. doi: 10.3390/rs12101586 Baena-Miret, S., Puig, M. A., Rodes, R. B., Farran, L. B., Durán, S., Martí, M. G., et al. (2024). Enhancing efficiency and quality control: the impact of digital drinking water networks. Water Environ. Res. 96:e11139. doi: 10.1002/wer.11139 Bamini, A., Agarwal, S., Kim, H., Stephan, P., and Stephan, T. (2024). Iot-based automatic water quality monitoring system with optimized neural network. KSII Trans. Internet Inf. Syst. 18, 46–63. doi: 10.3837/tiis.2024.01.004 Barrios-Ulloa, A., Ariza-Colpas, P. P., Sánchez-Moreno, H., Quintero, A. P., and la Hoz-Franco, E. D. (2022). Modeling radio wave propagation for wireless sensor networks in vegetated environments: a systematic literature review. Sensors 22:5285. doi: 10.3390/s22145285 Barsha, D., Syed, H., Sabuj, S., Hossain, A., and Ray, S. (2025). A comprehensive survey on emerging AI technologies for 6g communications: research direction, trends, challenges, and opportunities. Int. J. Intell. Netw. 6, 113–150. doi: 10.1016/j.ijin.2025.06.001 Bhati, A., Hiran, K. K., Vyas, A. K., Mijwil, M. M., Aljanabi, M., Metwally, A. S. M., et al. (2024). Low-cost artificial intelligence internet of things based water quality monitoring for rural areas. Internet Things 27:101255. doi: 10.1016/j.iot.2024. 101255 Bogdan, R., Paliuc, C., Crisan-Vida, M., Nimara, S., and Barmayoun, D. (2023). Low-cost internet-of-things water-quality monitoring system for rural areas. Sensors 23:3919. doi: 10.3390/s23083919 Boonsong, W., Inthasuth, T., and Zulkifli, C. Z. (2023). Proposed precision analysis of water quality monitoring embedded Iot network. Prz. Elektrotech. 1, 177–180. doi: 10.15199/48.2023.09.33 Bouchaou, L., Alfy, M. E., Shanafield, M., Siffeddine, A., and Sharp, J. (2024). Groundwater in arid and semi-arid areas. Geosciences 14:332. doi: 10.3390/geosciences14120332 Butler, A. R., Hartmann-Boyce, J., Livingstone-Banks, J., Turner, T., and Lindson, N. (2024). Optimizing process and methods for a living systematic review: 30 search updates and three review updates later. J. Clin. Epidemiol. 166:111231. doi: 10.1016/j.jclinepi.2023.111231 Chen, S. L., Chou, H. S., Huang, C. H., Chen, C. Y., Li, L. Y., Huang, C. H., et al. (2023). An intelligent water monitoring Iot system for ecological environment and smart cities. Sensors 23:8540. doi: 10.3390/s23208540 Chen, W., Hao, X., Lu, J., Yan, K., Liu, J., He, C., et al. (2021). Research and design of distributed Iot water environment monitoring system based on lora. Wirel. Commun. Mobile Comput. 2021:9403963. doi: 10.1155/2021/9403963 Choudhary, A. (2025). Internet of things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions. Discov. Internet Things 4, 1–41. doi: 10.1007/s43926-024-00084-3 Chowdury, M. S. U., Emran, T. B., Ghosh, S., Pathak, A., Alam, M. M., Absar, N., et al. (2019). Iot based real-time river water quality monitoring system. Procedia Comput. Sci. 155, 161–168. doi: 10.1016/j.procs.2019.08.025 Cohen, C., Villa, J., Camacho, S., Solano, Y., Alvarez, A., Coronado, O., et al. (2025). Simulation and optimisation using a digital twin for resilience-based management of confined aquifers. Water 17, 19–45. doi: 10.3390/w17131973 Dahane, A., Benameur, R., and Kechar, B. (2022). An Iot low-cost smart farming for enhancing irrigation efficiency of smallholders farmers. Wirel. Pers. Commun. 127, 3173–3210. doi: 10.1007/s11277-022-09915-4 Dhanda, S., Singh, B., and Jindal, P. (2020). Lightweight cryptography: a solution to secure Iot. Wirel. Pers. Commun. 112, 1947–1980. doi: 10.1007/s11277-020-07134-3 Dharmarathne, G., Abekoon, A., Bogahawaththa, M., and Alawatugoda, J. (2025). A review of machine learning and internet-of-things on the water quality assessment: methods, applications and future trends. Results Eng. 26, 1–30. doi: 10.1016/j.rineng.2025.105182 Döring, J., Scholtyssek, J., Beering, A., and Krieger, K. L. (2022). Optimization of road surface wetness classification using feature selection algorithms and sensor fusion. IEEE Access 10, 106248–106257. doi: 10.1109/ACCESS.2022.3211648 Drogkoula, M., Samaras, N., Iatrellis, O., Nathanail, E., and Kokkinos, K. (2025). Systematic review and topic classification of soft computing and machine learning in water resources management. Discov. Sustain. 6, 1–44. doi: 10.1007/s43621-025-01832-3 Du, R., Gkatzikis, L., Fischione, C., and Xiao, M. (2015). Energy efficient sensor activation for water distribution networks based on compressive sensing. IEEE J. Sel. Areas Commun. 33, 2997–3010. doi: 10.1109/JSAC.2015.2481199 El-Shafeiy, E., Alsabaan, M., Ibrahem, M. I., and Elwahsh, H. (2023). Real-time anomaly detection for water quality sensor monitoring based on multivariate deep learning technique. Sensors 23:8613. doi: 10.3390/s23208613 Friedmann, E., Gleason, C., Feng, D., and Langhorst, T. (2024). Estimating riverine total suspended solids from spatiotemporal satellite sensor fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 17, 15443–15462. doi: 10.1109/JSTARS.2024.3443756 Ganguly, S., and Sengupta, J. (2025). Graphene-based nanotechnology in the internet of things: a mini review. Discov. Nano 19, 1–27. doi: 10.1186/s11671-024-04054-0 Garcia-Martin, J., Torralba, A., Hidalgo-Fort, E., Daza, D., and Gonzalez-Carvajal, R. (2023). Iot solution for smart water distribution networks based on a low-power wireless network, combined at the device-level: a case study. Internet Things 22:100746. doi: 10.1016/j.iot.2023.100746 Gennaro, P. D., Lofú, D., Vitanio, D., Tedeschi, P., and Boccadoro, P. (2019). Waters: a sigfox-compliant prototype for water monitoring. Internet Technol. Lett. 2:e74. doi: 10.1002/itl2.74 :e74. doi: 10.1002/itl2.74 Georgantas, I., Mitropoulos, S., Katsoulis, S., Chronis, I., and Christakism, I. (2025). Integrated low-cost water quality monitoring system based on lora network. Electronics 14, 857–880. doi: 10.3390/electronics14050857 Ghazali, M. A. B., Rahman, F. Y. A., Mohamad, R., Shahbudin, S., Suliman, S. I., Yusof, Y. W. M., et al. (2024). “Development of water quality monitoring and communication system using raspberry pi and lora,” in Proceedings of the 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)(Shah Alam: IEEE), 35–40. doi: 10.1109/I2CACIS61270.2024.10649865 Gueye, A., Drame, M., and Niang, S. A. A. (2025). A low-cost Iot-based realtime pollution monitoring system using esp8266 nodemcu. Meas. Control 1, 1–10. doi: 10.1177/00202940241306690 Hagh, S. F., Amngostar, P., Zylka, A., Zimmerman, M., Cresanti, L., Karins, S., et al. (2024). Autonomous uav-mounted lorawan system for real-time monitoring of harmful algal blooms (HABs) and water quality. IEEE Sens. J. 7, 11414–11424. doi: 10.1109/JSEN.2024.3364142 Hakiri, A., Gokhale, A., Yahia, S. B., and Mellouli, N. (2024). A comprehensive survey on digital twin for future networks and emerging internet of things industry. Comput. Netw. 244:110350. doi: 10.1016/j.comnet.2024.110350 Hamou-Ali, Y., Karmouda, N., Mohsine, I., and Bouramtane, T. (2025). Downscaling grace total water storage data using random forest: a threeround validation approach under drought conditions. Front. Water 7:1545821. doi: 10.3389/frwa.2025.1545821 Homaei, M., Gonzalez, V., Mogollon, O., Molano, R., and Caro, A. (2025). Smart water security with AI and blockchain-enhanced digital twins. arXiv [preprint]. arXiv:2504.20275. doi: 10.48550/arXiv.2504.20275 Hussain, K., Rahmatyar, A. R., Riskhan, B., Sheikh, M. A. U., and Sindiramutty, S. R. (2024). “Threats and vulnerabilities of wireless networks in the internet of things (IoT),” in 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC) (Tandojam: IEEE), 1–8. doi: 10.1109/KHI-HTC60760.2024. 10482197 Jabbar, W. A., Ting, T. M., Hamidun, M. F. I., Kamarudin, A. H. C., Wu, W., Sultan, J., et al. (2024). Development of lorawan-based Iot system for water quality monitoring in rural areas. Expert Syst. Appl. 242:122862. doi: 10.1016/j.eswa.2023.122862 Jamroen, C., Komkum, P., Fongkerd, C., and Krongpha, W. (2020). An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture. IEEE Access 8, 172756–172769. doi: 10.1109/ACCESS.2020.3025590 Jan, F., Min-Allah, N., and Düstegör, D. (2021). Iot based smart water quality monitoring: recent techniques, trends and challenges for domestic applications. Water 13:1729. doi: 10.3390/w13131729 Jiazhe, W., Xinrui, D., Yancheng, S., Xiangtian, Z., Ghaffar, B., Nasir, R., et al. (2025). Multisensor data fusion and gis-drastic integration for groundwater vulnerability assessment with rainfall consideration. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 18, 3556–3568. doi: 10.1109/JSTARS.2024. 3524376 Kage, L., Milic, V., Andersson, M., and Wallen, M. (2025). Reinforcement learning applications in water resource management: a systematic literature review. Front. Water 7:1537868. doi: 10.3389/frwa.2025.1537868 Kameswari, Y. L., Jagan, B. O. L., Mohammed, T. K., and Aleem, S. H. A. (2025). “Future trends and research challenges in digital twins,” in Digital Twins for Smart Cities and Villages (Cham: Springer), 81–101. doi: 10.1016/B978-0-443-28884-5.00004-X Karki, J., Hu, J., Zhu, Y., Afzal, M. M., Xie, F., Liu, S., et al. (2025). Advances in grace satellite studies on terrestrial water storage: a comprehensive review. Geocarto Int. 40, 1–25. doi: 10.1080/10106049.2025.2482706 Kinman, G., Žilic, Ž., and Purnell, D. (2023). Scheduling sparse Leo ´ satellite transmissions for remote water level monitoring. Sensors 23:5581. doi: 10.3390/s23125581 Kombo, O. H., Kumaran, S., and Bovim, A. (2021). Design and application of a low-cost, low-power, lora-Gsm, Iot enabled system for monitoring of groundwater resources with energy harvesting integration. IEEE Access 9, 128417–128433. doi: 10.1109/ACCESS.2021.3112519 Koronides, M., Stylianidis, P., Michailides, C., and Onoufriou, T. (2024). Realtime monitoring of seawater quality parameters in ayia napa, cyprus. J. Mar. Sci. Eng. 12:1731. doi: 10.3390/jmse12101731 Krishnamurthy, R., and Milani, A. (2025). Graphene–pla printed sensor combined with xr and the Iot for enhanced temperature monitoring: a case study. J. Sens. Actuator Netw. 14, 1–28. doi: 10.3390/jsan14040068 Kumar, J., Gupta, R., Sharma, S., Chakrabarti, T., Chakrabarti, P., Margala, M., et al. (2024). Iot-enabled advanced water quality monitoring system for pond management and environmental conservation. IEEE Access 12, 58156–58167. doi: 10.1109/ACCESS.2024.3391807 Kumar, M., Singh, T., Maurya, M. K., Shivhare, A., Raut, A., Singh, P. K., et al. (2023). Quality assessment and monitoring of river water using Iot infrastructure. IEEE Internet Things J. 10, 10280–10290. doi: 10.1109/JIot.2023.3238123 Kumar, P., and Choudhury, D. (2024). “Innovative technologies for effective water resources management,” in Water Crises and Sustainable Management in the Global South (Singapore: Springer), 555–594. doi: 10.1007/978-981-97-4966-9_18 Lal, K., Menon, S., Noble, F., and Mahmood, K. (2025). Low-cost Iot based system for lake water quality monitoring. PLoS ONE 19, 1–21. doi: 10.1371/journal.pone.0299089 Lee, M. H., Won, J., Chung, S., Kim, S., and Park, S. (2022). Rapid detection of ionic contents in water through sensor fusion and convolutional neural network. Chemosphere 294:133746. doi: 10.1016/j.chemosphere.2022.133746 Li, J., Li, L., Song, Y., Chen, J., Wang, Z., Bao, Y., et al. (2023). A robust largescale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data. Int. J. Appl. Earth Observ. Geoinform. 118:103288. doi: 10.1016/j.jag.2023.103288 Li, L., Mitropoulos, S., Liew, J. T., Saleh, N. L., and Ali, A. M. (2025). Machine learning for peatland ground water level (GWL) prediction via Iot system. IEEE Access 12, 89585–89598. doi: 10.1109/ACCESS.2024.3419237 Liu, Q. (2021). Intelligent water quality monitoring system based on multisensor data fusion technology. Int. J. Ambient Comput. Intell. 12, 43–63. doi: 10.4018/IJACI.2021100103 Lloret, J., Tomas, J., Canovas, A., and Parra, L. (2016). An integrated Iot architecture for smart metering. IEEE Commun. Mag. 54, 50–57. doi: 10.1109/MCOM.2016.1600647CM López-Muñoz, M. A., Torrealba-Meléndez, R., Arriaga-Arriaga, C. A., TamarizFlores, E. I., López-López, M., Quirino-Morales, F., et al. (2024). Wireless dynamic sensor network for water quality monitoring based on the Iot. Technologies 12:211. doi: 10.3390/technologies12110211 Mahomed, A., and Saha, A. (2025). Unleashing the potential of 5g for smart cities: a focus on real-time digital twin integration. Smart Cities 8, 70–91. doi: 10.3390/smartcities8020070 Maia, R., Lurbe, C., and Hornbuckle, J. (2022). Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data. Front. Plant Sci. 13, 19–35. doi: 10.3389/fpls.2022.931491 Manjakkal, L., Mitra, S., Petillot, Y. R., Shutler, J., Scott, E. M., Willander, M., et al. (2021). Connected sensors, innovative sensor deployment, and intelligent data analysis for online water quality monitoring. IEEE Internet Things J. 8, 13805–13824. doi: 10.1109/JIot.2021.3081772 Manocha, A., Sood, S. K., and Bhatia, M. (2024). Iot-digital twin-inspired smart irrigation approach for optimal water utilization. Sustain. Comput. Inform. Syst. 41:100947. doi: 10.1016/j.suscom.2023.100947 Manzione, R. L., and Castrignanò, A. (2019). A geostatistical approach for multisource data fusion to predict water table depth. Sci. Total Environ. 696:133763. doi: 10.1016/j.scitotenv.2019.133763 Marzi, G., Balzano, M., Caputo, A., and Pellegrini, M. M. (2025). Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. Int. J. Manag. Rev. 27, 81–103. doi: 10.1111/ijmr.12381 Masood, F., Khan, W. U., Jan, S. U., and Ahmad, J. (2023). Ai-enabled traffic control prioritization in software-defined Iot networks for smart agriculture. Sensors 23:8218. doi: 10.3390/s23198218 Mastan Vali, S. (2024). Enhancing coverage and efficiency in wireless sensor networks: a review of optimization techniques. Adv. Eng. Intell. Syst. 3, 39–52. Miao, H. Y., Yang, C. T., Kristiani, E., Fathoni, H., Lin, Y. S., Chen, C. Y., et al. (2022). On construction of a campus outdoor air and water quality monitoring system using lorawan. Appl. Sci. 12:5018. doi: 10.3390/app12105018 Miller, M., Kisiel, A., Cembrowska-Lech, D., Durlik, I., and Miller, T. (2023). Iot in water quality monitoring—are we really here? Sensors 23:960. doi: 10.3390/s23020960 Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzinska, A., Sokołowska, S., ´ et al. (2025). Integrating artificial intelligence agents with the internet of things for enhanced environmental monitoring: applications in water quality and climate data. Electronics 14:696. doi: 10.3390/electronics14040696 Mirzavand, R., Honari, M. M., and Mousavi, P. (2017). Direct-conversion sensor for wireless sensing networks. IEEE Trans. Ind. Electron. 64, 9675–9682. doi: 10.1109/TIE.2017.2716863 Mohaimenuzzaman, M., Rahman, S. M. M., Alhussein, M., Muhammad, G., and Mamun, K. A. A. (2016). Enhancing safety in water transport system based on internet of things for developing countries. Int. J. Distrib. Sens. Netw. 12:2834616. doi: 10.1155/2016/2834616 Mohammadi, M., Assaf, G., Assaad, R. H., and Chang, A. J. (2024). An intelligent cloud-based Iot-enabled multimodal edge sensing device for automated, real-time, comprehensive, and standardized water quality monitoring and assessment process using multisensor data fusion technologies. J. Comput. Civil Eng. 38:04024029. doi: 10.1061/JCCEE5.CPENG-5989 Moiroux-Arvis, L., Royer, L., Sarramia, D., Sousa, G. D., Claude, A., Latour, D., et al. (2023). Connecsens, a versatile Iot platform for environment monitoring: bring water to cloud. Sensors 23:2896. doi: 10.3390/s23062896 Morchid, A., Said, Z., Abdelaziz, A. Y., Siano, P., and Qjidaa, H. (2025). Fuzzy logic-based Iot system for optimizing irrigation with cloud computing: enhancing water sustainability in smart agriculture. Smart Agric. Technol. 11:231. doi: 10.1016/j.atech.2025.100979 Moreno, C., Aquino, R., Ibarreche, J., Pérez, I., Castellanos, E., Álvarez, E., et al. (2019). Rivercore: Iot device for river water level monitoring over cellular communications. Sensors 19:127. doi: 10.3390/s19010127 Mukta, M., Islam, S., Barman, S. D., Reza, A. W., and Khan, M. S. H. (2019). “Iot based smart water quality monitoring system,” in Proceedings of the 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (Singapore: IEEE), 669–673. doi: 10.1109/CCOMS.2019.8821742 Mutunga, T., Sinanovic, S., and Harrison, C. (2024). A wireless network for monitoring pesticides in groundwater: an inclusive approach for a vulnerable kenyan population. Sensors 24:4665. doi: 10.3390/s24144665 Ogenyi, F. Ugwu1, C., Ugwu, O. (2025). Securing the future: AI-driven cybersecurity in the age of autonomous Iot. Front. Internet Things 4:1658273. doi: 10.3389/friot.2025.1658273 Olatinwo, S. O., and Joubert, T. H. (2020). Energy efficiency maximization in a wireless powered Iot sensor network for water quality monitoring. Comput. Netw. 176:107237. doi: 10.1016/j.comnet.2020.107237 Omrany, H., Al-Obaidi, K. M., Husain, A., and Ghaffarianhoseini, A. (2023). Digital twins in the construction industry: a comprehensive review of current implementations, enabling technologies, and future directions. Sustainability 15:10908. doi: 10.3390/su151410908 Ortega, L. R. M. (2023). A Digital Twin for Ground Water Table Monitoring [Master’s thesis]. University of Twente, Enschede. Ortiz, M. E., Molina, J. P. A., Jiménez, S. I. B., Barrientos, J. H., Guevara, H. J. P., Miranda, A. S., et al. (2023). Development of low-cost Iot system for monitoring piezometric level and temperature of groundwater. Sensors 23:9364. doi: 10.3390/s23239364 Pasika, S., and Gandla, S. T. (2020). Smart water quality monitoring system with cost-effective using Iot. Heliyon 6:e04096. doi: 10.1016/j.heliyon.2020.e04096 Pointet, T. (2022). The united nations world water development report 2022 on groundwater, a synthesis. LHB 108:2090867. doi: 10.1080/27678490.2022.2090867 Prabu, T., Sarkar, M., Chaudhary, D., Obaid, S. A., Khalid, T. A., and Kalam, M. A. (2025). Iot-enabled groundwater monitoring with k-nn-svm algorithm for sustainable water management. Acta Geophys. 72, 2715–2728. doi: 10.1007/s11600-023-01178-2 Primeau, R. B. (2024). Framework for a Digital Twin of Brusdalsvatnet Water Quality [Master’s thesis]. Norwegian University of Science and Technology, Trondheim. Priyanka, E. B., Thangavel, S., Mohanasundaram, R., and Anand, R. (2024). Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with kalman filter. Sci. Rep. 14:25202. doi: 10.1038/s41598-024-76068-8 Promput, S., Maithomklang, S., and Panya-isara, C. (2023). Design and analysis performance of Iot-based water quality monitoring system using lora technology. TEM J. 12, 29–35. doi: 10.18421/TEM121-04 Qiao, J., Lin, Y., Bi, J., and Yuan, H. (2024). Attention-based spatiotemporal graph fusion convolution networks for water quality prediction. Sens. Int. 22, 1–22. doi: 10.1109/TASE.2023.3285253 Rahman, A., Yap, B. K., Murad, W., and Islam, Z. (2025). Low-cost Iot solution for real-time monitoring of aquaculture water parameters. Interdiscip. J. Papua New Guinea Univ. Technol. 2, 1–11. doi: 10.63900/dc24fx27 Rahu, M. A., Shaikh, M. M., Karim, S., Soomro, S. A., Hussain, D., Ali, S. M., et al. (2024). Water quality monitoring and assessment for efficient water resource management through internet of things and machine learning approaches for agricultural irrigation. Water Resour. Manag. 38, 4987–5028. doi: 10.1007/s11269-024-03899-5 Rana, M. S., Nobi, M. N., Murali, B., and Sung, A. H. (2022). Deepfake detection: a systematic literature review. IEEE Access 10, 25494–25513. doi: 10.1109/ACCESS.2022.3154404 Raphael, R., Sarukkalige, R., Sarukkalige, R., and Agrawal, H. (2025). Performance evaluation of chaosfortress lightweight cryptographic algorithm for data security in water and other utility management. Sensors 25, 1–21. doi: 10.3390/s251 65103 Razaque, A., Hariri, S., Alajlan, A. M., and Yoo, J. (2025). A comprehensive review of cybersecurity vulnerabilities, threats, and solutions for the internet of things at the network-cum-application layer. Comput. Sci. Rev. 58, 10–41. doi: 10.1016/j.cosrev.2025.100789 Rueda, J. S., and Talavera, J. M. (2017). Similarities and differences between wireless sensor networks and the internet of things: towards a clarifying position. Rev. Colomb. Computación 18, 58–74. doi: 10.29375/25392115.3218 Saghir, A., Akbar, A., Hasan, A., and Zafar, A. (2025). Deep learning for multimodal data fusion in Iot applications. Mehran Univ. Res. J. Eng. Technol. 44, 75–81. doi: 10.22581/muet1982.3171 Saheed, Y., Omole, A., and Sabit, M. (2025). Ga-madam-IIoT: a new lightweight threats detection in the industrial Iot via genetic algorithm with attention mechanism and lstm on multivariate time series sensor data. Sens. Int. 6, 1–22. doi: 10.1016/j.sintl.2024.100297 Saranya, K., and Valarmathi, A. (2025). A multilayer deep autoencoder approach for cross layer Iot attack detection using deep learning algorithms. Sci. Rep. 15, 1–30. doi: 10.1038/s41598-025-93473-9 Shemer, H., Wald, S., and Semiat, R. (2023). Challenges and solutions for global water scarcity. Membranes 13:612. doi: 10.3390/membranes13060612 Shen, B. (2023). Study on Data Fusion Method Based on AWDF and LSTM for Water Environment Monitoring in WSNs [PhD thesis]. Tokyo University of Marine Science and Technology, Tokyo. Shukla, S. (2023). Improving latency in internet-of-things and cloud computing for real-time data transmission: a systematic literature review (SLR). Cluster Comput. 26, 2657–2680. doi: 10.1007/s10586-021-03279-3 Singh, M., Sahoo, K. S., and Gandomi, A. H. (2023). An intelligent-IoT-based data analytics for freshwater recirculating aquaculture system. IEEE Internet Things J. 11, 4206–4217. doi: 10.1109/JIot.2023.3298844 Slany, V., Krcalova, E., Balej, J., Zach, M., and Kucova, T. (2025). Smart water-IoT: harnessing IoT and AI for efficient water management. ACM Comput. Surv. 57, 1–36. doi: 10.1145/3744338 Slaný, V., Lucanský, A., Koudelka, P., Mare ˇ cek, J., Kr ˇ cálová, E., Martínek, R., et al. ˇ (2020). An integrated Iot architecture for smart metering using next generation sensor for water management based on lorawan technology: a pilot study. Sensors 20:4712. doi: 10.3390/s20174712 Sonbul, O. S., and Rashid, M. (2023). Algorithms and techniques for the structural health monitoring of bridges: systematic literature review. Sensors 23:4230. doi: 10.3390/s23094230 Syed, T. A., Muhammad, M. M. AlShahrani, A. A., Hammad, M., and Naqash, M. T. (2024). Smart water management with digital twins and multimodal transformers: a predictive approach to usage and leakage detection. Water 16:3410. doi: 10.3390/w16233410 Truong, V. T., Nayyar, A., and Lone, S. A. (2021). System performance of wireless sensor network using Lora-Zigbee hybrid communication. Comput. Mater. Contin. 68, 1615–1635. doi: 10.32604/cmc.2021.016922 Tu, L. T., Bradai, A., Pousset, Y., and Aravanis, A. I. (2022). On the spectral efficiency of lora networks: performance analysis, trends and optimal points of operation. IEEE Trans. Commun. 70, 2788–2804. doi: 10.1109/TCOMM.2022. 3148784 Vadone, S., Shaji, S., and Sundaram, M. (2025). Water management prediction using deep convolutional spiking neural network optimized with red fox optimization algorithm based on Iot. Sens. Imaging 26, 3556–3568. doi: 10.1007/s11220-024- 00533-x Venkatesh, J., Partheeban, P., Baskaran, A., Krishnan, D., and Sridhar, M. (2025). Wireless sensor network technology and geospatial technology for groundwater quality monitoring. J. Ind. Inf. Integ. 38, 1–38. doi: 10.1016/j.jii.2024.100569 Wang, A. J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., et al. (2024). Digital twins for wastewater treatment: a technical review. Engineering 36, 21–35. doi: 10.1016/j.eng.2024.04.012 Wang, M., Shi, B., Catsamas, S., Kolotelo, P., and McCarthy, D. (2024). A compact, low-cost, and low-power turbidity sensor for continuous in situ stormwater monitoring. Sensors 24:3926. doi: 10.3390/s24123926 Wang, S., Wu, M., Wei, X., and Song, A. (2025). An advanced multi-source data fusion method utilizing deep learning techniques for fire detection. Eng. Appl. Artif. Intell. 142, 1–21. doi: 10.1016/j.engappai.2024.109902 Wang, S., and Xu, O. (2024). Interoperability structure of smart water conservancy based on internet of things. Int. J. Distrib. Sens. Netw. 2024:7724783. doi: 10.1155/2024/7724783 Wang, Y., Xie, C., Liu, Y., Zhu, J., and Qin, J. (2024). A multi-sensor fusion underwater localization method based on unscented kalman filter on manifolds. Sensors 24:6299. doi: 10.3390/s24196299 Wong, Y. J., Nakayama, R., Shimizu, Y., Kamiya, A., Shen, S., Rashid, I. Z. M., et al. (2021). Toward industrial revolution 4.0: development, validation, and application of 3d-printed Iot-based water quality monitoring system. J. Clean. Prod. 324:129230. doi: 10.1016/j.jclepro.2021.129230 Yahya, H., Al-Dweik, A., Iraqi, Y., Alsusa, E., and Ahmed, A. (2022). A power and spectrum efficient uplink transmission scheme for QOS-constrained Iot networks. IEEE Internet Things J. 9, 17425–17439. doi: 10.1109/JIot.2022.3156209 Yalli, J. S., Hasan, M. H., and Badawi, A. (2024). Internet of things (Iot): origin, embedded technologies, smart applications and its growth in the last decade. IEEE Access. 12, 91357–91382. doi: 10.1109/ACCESS.2024.341899 Yin, W., Hu, Q., Liu, W., Liu, J., He, P., Zhu, D., et al. (2024). Harnessing game engines and digital twins: advancing flood education, data visualization, and interactive monitoring for enhanced hydrological understanding. Water 16:2528. doi: 10.3390/w161 72528 Zhang, H., Leng, L., Adeleke Qiao, Z., Zhang, Z., and Shi, Z. (2023). “Design and implementation of a multi-sensor online water quality monitoring system,” in Proceedings of the 2023 4th International Conference on Computer Science and Management Technology (New York, NY: ACM), 207–212 doi: 10.1145/3644523.3644562 Zhang, L., Banihashemi, S., Zhu, L., Molavi, H., Odacioglu, E., Shan, M., et al. (2024). A scientometric analysis of knowledge transfer partnerships in digital transformation. J. Open Innov. Technol. Mark. Complex. 10, 1–15. doi: 10.1016/j.joitmc.2024.100325 Zhang, R., Zhu, H., Chang, Q., and Mao, Q. (2025). A comprehensive review of digital twins technology in agriculture. Agriculture 15, 1–21. doi: 10.3390/agriculture15090903
dc.rights.uri.none.fl_str_mv	https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.none.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.license.none.fl_str_mv	Atribución 4.0 Internacional (CC BY 4.0)
dc.rights.coar.none.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	https://creativecommons.org/licenses/by-nc-sa/4.0/ Atribución 4.0 Internacional (CC BY 4.0) http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.none.fl_str_mv	18
dc.format.mimetype.none.fl_str_mv	application/pdf
dc.coverage.country.none.fl_str_mv	Colombia
dc.publisher.none.fl_str_mv	Front. Water
publisher.none.fl_str_mv	Front. Water
institution	Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv	https://repositorio.utb.edu.co/bitstreams/44b3b41c-1081-4aa8-93b4-297d16f431f8/download https://repositorio.utb.edu.co/bitstreams/788114f8-9f13-4672-a349-7582bae5dff7/download https://repositorio.utb.edu.co/bitstreams/4071218e-e7f2-4ed7-a1a0-9a97c630cb6e/download https://repositorio.utb.edu.co/bitstreams/f5fce718-f99d-4c3d-8d5a-8705bb51465b/download
bitstream.checksum.fl_str_mv	9f5c144fb58d870caa5af089267b1b62 b76e7a76e24cf2f94b3ce0ae5ed275d0 b143ad33c22e3e04276dd0007f025809 4bd0d26f3a9d4ac32ab0c43b372ca8b1
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
_version_	1858228392720072704
spelling	Cohen Manrique, Carlosvirtual::6706-1Camacho-Leon, Sergiovirtual::6707-1Villa Ramírez, José Luisvirtual::6708-1Grupo de Investigación Automatización Industrial y Control (GAICO)Semillero de Investigación en Automatización y Control2026-01-16T21:19:54Z2025-12-042025-12-04Cohen-Manrique C, Camacho-Leon S and Villa JL (2025) Emerging trends in IoT for aquatic systems: a systematic literature review. Front. Water 7:1699240. doi: 10.3389/frwa.2025.1699240doi: 10.3389/frwa.2025.1699240https://hdl.handle.net/20.500.12585/14293Climate change, pollution, and the overexploitation of water resources have intensified global water scarcity, particularly in arid and semi-arid regions. This systematic literature review analyzes 458 peer-reviewed articles published between 2015 and 2025 to identify the main IoT-based technological strategies applied to the monitoring and management of surface and groundwater systems. Following PRISMA guidelines, the studies were categorized into four thematic areas: IoT applications in aquatic environments, data transmission technologies, algorithms for process optimization and data analysis, and sensor fusion techniques. The results show that LoRa is the most widely adopted transmission technology due to its long-range coverage, scalability, and low energy consumption. Emerging innovations such as remote IoT, satellite-assisted sensing, and digital twins are also gaining relevance as transformative tools for real-time hydrological monitoring. Overall, the findings reveal a shift toward more integrated and intelligent IoT frameworks and include a recommended architecture for aquatic systems. Despite these advancements, the review highlights the need for more accessible, affordable, and interoperable IoT solutions to enable broader adoption, particularly in resource-constrained regions, and to support sustainable water resource management.Introduction Research Methodology Cluster Analysis Thematic Trend Analysis Answer to questions set forth in the RSL Suggested IoT architecture for aquatic systems based on the contributions of the authors ConclusionAutomatización y control de procesos industriales18application/pdfengFront. Water© 2025 Cohen-Manrique, Camacho-Leon and Villa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2330 - Economía::333 - Economía de la tierra y de la energíaInternet of Thingssensor fusionsystematic literature reviewwater qualitywater resourcesRecursos hídricos -- GestiónEscasez de aguaCambio climáticoInternet de las cosasMonitoreo ambientalWater Resources -- ManagementWater ScarcityClimate ChangeInternet of ThingsEnvironmental Monitoring2. Ingeniería y TecnologíaODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todosEmerging trends in IoT for aquatic systems: a systematic literature reviewArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARTTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Abdulbaqi, A. G., and Hashim, Y. (2024). Design and implementation of Iot based rivers monitoring system. IEEJ Trans. Electr. Electron. Eng. 19, 469–474. doi: 10.1002/tee.23992Addow, M., and Jimale, A. (2024). Integrating Iot and water quality: a bibliometric analysis. Int. J. Electron. Commun. Eng. 11, 149–160. doi: 10.14445/23488549/IJECE-V11I10P112Adeleke, I. A., Nwulu, N. I., and Ogbolumani, O. A. (2023). A hybrid machine learning and embedded Iot-based water quality monitoring system. Internet Things 22:100774. doi: 10.1016/j.iot.2023.100774Adriansyah, A., Budiutomo, M. H., Hermawan, H., Andriani, R. I., Sulistyawan, R., Shamsudin, A. U., et al. (2024). Design of water level detection monitoring system using fusion sensor based on internet of things (Iot). SINERGI 28, 191–198. doi: 10.22441/sinergi.2024.1.019Ahmad, S., Uyanık, H., Ovatman, T., Sandıkkaya, M. T., Maio, V. D., Brandic, I., et al. (2023). Sustainable environmental monitoring via energy and ´ information efficient multi-node placement. IEEE Internet Things J. 10, 22065–22079. doi: 10.1109/JIot.2023.3303124Aiche, A., Tardif, P. M., and Erritali, M. (2024). Modeling trust in Iot systems for drinking-water management. Future Internet 16:273. doi: 10.3390/fi16080273Alghamdi, W., Alshamrani, R., Aloufi, R., and Lhamar, B. (2025). Exploring the synergy between digital twin technology and artificial intelligence: a comprehensive survey. Int. J. Adv. Comput. Sci. Appl. 16, 1–36. doi: 10.14569/IJACSA.2025.01 60399Alhamam, N., Rahman, H., and Aljughaiman, A. (2025). A comprehensive review on cybersecurity of digital twins issues, challenges, and future research directions. IEEE Access 13, 45106–45124. doi: 10.1109/ACCESS.2025.3545004Aliagas, C., Pérez-Foguet, A., Meseguer, R., Millán, P., and Molina, C. (2022). A lowcost and do-it-yourself device for pumping monitoring in deep aquifers. Electronics 11:3788. doi: 10.3390/electronics11223788Alobaidy, I. A., Abdullah, N. F., Nordin, R., Behjati, M., Abu-Samah, A., Maizan, H., et al. (2024). Empowering extreme communication: propagation characterization of a lora-based internet of things network using hybrid machine learning. IEEE Open J. Commun. Soc. 5, 3997–4023. doi: 10.1109/OJCOMS.2024.3420229Anker, C. (2023). Digital Twin Paradigms Towards Monitoring Insights for Deep Aquifer Pumps [PhD thesis]. Stellenbosch University, Stellenbosch.Ansari, V., and Kumar, V. (2025). Use of internet of things in water resources applications: challenges and future directions: a critical review. Discov. Internet Things 5, 1–58. doi: 10.1007/s43926-025-00193-7Arias-Rodriguez, L. F., Duan, Z., Sepúlveda, R., Martinez-Martinez, S. I., and Disse, M. (2020). Monitoring water quality of Valle de Bravo reservoir, Mexico, using entire lifespan of Meris data and machine learning approaches. Remote Sens. 12:1586. doi: 10.3390/rs12101586Baena-Miret, S., Puig, M. A., Rodes, R. B., Farran, L. B., Durán, S., Martí, M. G., et al. (2024). Enhancing efficiency and quality control: the impact of digital drinking water networks. Water Environ. Res. 96:e11139. doi: 10.1002/wer.11139Bamini, A., Agarwal, S., Kim, H., Stephan, P., and Stephan, T. (2024). Iot-based automatic water quality monitoring system with optimized neural network. KSII Trans. Internet Inf. Syst. 18, 46–63. doi: 10.3837/tiis.2024.01.004Barrios-Ulloa, A., Ariza-Colpas, P. P., Sánchez-Moreno, H., Quintero, A. P., and la Hoz-Franco, E. D. (2022). Modeling radio wave propagation for wireless sensor networks in vegetated environments: a systematic literature review. Sensors 22:5285. doi: 10.3390/s22145285Barsha, D., Syed, H., Sabuj, S., Hossain, A., and Ray, S. (2025). A comprehensive survey on emerging AI technologies for 6g communications: research direction, trends, challenges, and opportunities. Int. J. Intell. Netw. 6, 113–150. doi: 10.1016/j.ijin.2025.06.001Bhati, A., Hiran, K. K., Vyas, A. K., Mijwil, M. M., Aljanabi, M., Metwally, A. S. M., et al. (2024). Low-cost artificial intelligence internet of things based water quality monitoring for rural areas. Internet Things 27:101255. doi: 10.1016/j.iot.2024. 101255Bogdan, R., Paliuc, C., Crisan-Vida, M., Nimara, S., and Barmayoun, D. (2023). Low-cost internet-of-things water-quality monitoring system for rural areas. Sensors 23:3919. doi: 10.3390/s23083919Boonsong, W., Inthasuth, T., and Zulkifli, C. Z. (2023). Proposed precision analysis of water quality monitoring embedded Iot network. Prz. Elektrotech. 1, 177–180. doi: 10.15199/48.2023.09.33Bouchaou, L., Alfy, M. E., Shanafield, M., Siffeddine, A., and Sharp, J. (2024). Groundwater in arid and semi-arid areas. Geosciences 14:332. doi: 10.3390/geosciences14120332Butler, A. R., Hartmann-Boyce, J., Livingstone-Banks, J., Turner, T., and Lindson, N. (2024). Optimizing process and methods for a living systematic review: 30 search updates and three review updates later. J. Clin. Epidemiol. 166:111231. doi: 10.1016/j.jclinepi.2023.111231Chen, S. L., Chou, H. S., Huang, C. H., Chen, C. Y., Li, L. Y., Huang, C. H., et al. (2023). An intelligent water monitoring Iot system for ecological environment and smart cities. Sensors 23:8540. doi: 10.3390/s23208540Chen, W., Hao, X., Lu, J., Yan, K., Liu, J., He, C., et al. (2021). Research and design of distributed Iot water environment monitoring system based on lora. Wirel. Commun. Mobile Comput. 2021:9403963. doi: 10.1155/2021/9403963Choudhary, A. (2025). Internet of things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions. Discov. Internet Things 4, 1–41. doi: 10.1007/s43926-024-00084-3Chowdury, M. S. U., Emran, T. B., Ghosh, S., Pathak, A., Alam, M. M., Absar, N., et al. (2019). Iot based real-time river water quality monitoring system. Procedia Comput. Sci. 155, 161–168. doi: 10.1016/j.procs.2019.08.025Cohen, C., Villa, J., Camacho, S., Solano, Y., Alvarez, A., Coronado, O., et al. (2025). Simulation and optimisation using a digital twin for resilience-based management of confined aquifers. Water 17, 19–45. doi: 10.3390/w17131973Dahane, A., Benameur, R., and Kechar, B. (2022). An Iot low-cost smart farming for enhancing irrigation efficiency of smallholders farmers. Wirel. Pers. Commun. 127, 3173–3210. doi: 10.1007/s11277-022-09915-4Dhanda, S., Singh, B., and Jindal, P. (2020). Lightweight cryptography: a solution to secure Iot. Wirel. Pers. Commun. 112, 1947–1980. doi: 10.1007/s11277-020-07134-3Dharmarathne, G., Abekoon, A., Bogahawaththa, M., and Alawatugoda, J. (2025). A review of machine learning and internet-of-things on the water quality assessment: methods, applications and future trends. Results Eng. 26, 1–30. doi: 10.1016/j.rineng.2025.105182Döring, J., Scholtyssek, J., Beering, A., and Krieger, K. L. (2022). Optimization of road surface wetness classification using feature selection algorithms and sensor fusion. IEEE Access 10, 106248–106257. doi: 10.1109/ACCESS.2022.3211648Drogkoula, M., Samaras, N., Iatrellis, O., Nathanail, E., and Kokkinos, K. (2025). Systematic review and topic classification of soft computing and machine learning in water resources management. Discov. Sustain. 6, 1–44. doi: 10.1007/s43621-025-01832-3Du, R., Gkatzikis, L., Fischione, C., and Xiao, M. (2015). Energy efficient sensor activation for water distribution networks based on compressive sensing. IEEE J. Sel. Areas Commun. 33, 2997–3010. doi: 10.1109/JSAC.2015.2481199El-Shafeiy, E., Alsabaan, M., Ibrahem, M. I., and Elwahsh, H. (2023). Real-time anomaly detection for water quality sensor monitoring based on multivariate deep learning technique. Sensors 23:8613. doi: 10.3390/s23208613Friedmann, E., Gleason, C., Feng, D., and Langhorst, T. (2024). Estimating riverine total suspended solids from spatiotemporal satellite sensor fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 17, 15443–15462. doi: 10.1109/JSTARS.2024.3443756Ganguly, S., and Sengupta, J. (2025). Graphene-based nanotechnology in the internet of things: a mini review. Discov. Nano 19, 1–27. doi: 10.1186/s11671-024-04054-0Garcia-Martin, J., Torralba, A., Hidalgo-Fort, E., Daza, D., and Gonzalez-Carvajal, R. (2023). Iot solution for smart water distribution networks based on a low-power wireless network, combined at the device-level: a case study. Internet Things 22:100746. doi: 10.1016/j.iot.2023.100746Gennaro, P. D., Lofú, D., Vitanio, D., Tedeschi, P., and Boccadoro, P. (2019). Waters: a sigfox-compliant prototype for water monitoring. Internet Technol. Lett. 2:e74. doi: 10.1002/itl2.74:e74. doi: 10.1002/itl2.74 Georgantas, I., Mitropoulos, S., Katsoulis, S., Chronis, I., and Christakism, I. (2025). Integrated low-cost water quality monitoring system based on lora network. Electronics 14, 857–880. doi: 10.3390/electronics14050857Ghazali, M. A. B., Rahman, F. Y. A., Mohamad, R., Shahbudin, S., Suliman, S. I., Yusof, Y. W. M., et al. (2024). “Development of water quality monitoring and communication system using raspberry pi and lora,” in Proceedings of the 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)(Shah Alam: IEEE), 35–40. doi: 10.1109/I2CACIS61270.2024.10649865Gueye, A., Drame, M., and Niang, S. A. A. (2025). A low-cost Iot-based realtime pollution monitoring system using esp8266 nodemcu. Meas. Control 1, 1–10. doi: 10.1177/00202940241306690Hagh, S. F., Amngostar, P., Zylka, A., Zimmerman, M., Cresanti, L., Karins, S., et al. (2024). Autonomous uav-mounted lorawan system for real-time monitoring of harmful algal blooms (HABs) and water quality. IEEE Sens. J. 7, 11414–11424. doi: 10.1109/JSEN.2024.3364142Hakiri, A., Gokhale, A., Yahia, S. B., and Mellouli, N. (2024). A comprehensive survey on digital twin for future networks and emerging internet of things industry. Comput. Netw. 244:110350. doi: 10.1016/j.comnet.2024.110350Hamou-Ali, Y., Karmouda, N., Mohsine, I., and Bouramtane, T. (2025). Downscaling grace total water storage data using random forest: a threeround validation approach under drought conditions. Front. Water 7:1545821. doi: 10.3389/frwa.2025.1545821Homaei, M., Gonzalez, V., Mogollon, O., Molano, R., and Caro, A. (2025). Smart water security with AI and blockchain-enhanced digital twins. arXiv [preprint]. arXiv:2504.20275. doi: 10.48550/arXiv.2504.20275Hussain, K., Rahmatyar, A. R., Riskhan, B., Sheikh, M. A. U., and Sindiramutty, S. R. (2024). “Threats and vulnerabilities of wireless networks in the internet of things (IoT),” in 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC) (Tandojam: IEEE), 1–8. doi: 10.1109/KHI-HTC60760.2024. 10482197Jabbar, W. A., Ting, T. M., Hamidun, M. F. I., Kamarudin, A. H. C., Wu, W., Sultan, J., et al. (2024). Development of lorawan-based Iot system for water quality monitoring in rural areas. Expert Syst. Appl. 242:122862. doi: 10.1016/j.eswa.2023.122862Jamroen, C., Komkum, P., Fongkerd, C., and Krongpha, W. (2020). An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture. IEEE Access 8, 172756–172769. doi: 10.1109/ACCESS.2020.3025590Jan, F., Min-Allah, N., and Düstegör, D. (2021). Iot based smart water quality monitoring: recent techniques, trends and challenges for domestic applications. Water 13:1729. doi: 10.3390/w13131729Jiazhe, W., Xinrui, D., Yancheng, S., Xiangtian, Z., Ghaffar, B., Nasir, R., et al. (2025). Multisensor data fusion and gis-drastic integration for groundwater vulnerability assessment with rainfall consideration. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 18, 3556–3568. doi: 10.1109/JSTARS.2024. 3524376Kage, L., Milic, V., Andersson, M., and Wallen, M. (2025). Reinforcement learning applications in water resource management: a systematic literature review. Front. Water 7:1537868. doi: 10.3389/frwa.2025.1537868Kameswari, Y. L., Jagan, B. O. L., Mohammed, T. K., and Aleem, S. H. A. (2025). “Future trends and research challenges in digital twins,” in Digital Twins for Smart Cities and Villages (Cham: Springer), 81–101. doi: 10.1016/B978-0-443-28884-5.00004-XKarki, J., Hu, J., Zhu, Y., Afzal, M. M., Xie, F., Liu, S., et al. (2025). Advances in grace satellite studies on terrestrial water storage: a comprehensive review. Geocarto Int. 40, 1–25. doi: 10.1080/10106049.2025.2482706Kinman, G., Žilic, Ž., and Purnell, D. (2023). Scheduling sparse Leo ´ satellite transmissions for remote water level monitoring. Sensors 23:5581. doi: 10.3390/s23125581Kombo, O. H., Kumaran, S., and Bovim, A. (2021). Design and application of a low-cost, low-power, lora-Gsm, Iot enabled system for monitoring of groundwater resources with energy harvesting integration. IEEE Access 9, 128417–128433. doi: 10.1109/ACCESS.2021.3112519Koronides, M., Stylianidis, P., Michailides, C., and Onoufriou, T. (2024). Realtime monitoring of seawater quality parameters in ayia napa, cyprus. J. Mar. Sci. Eng. 12:1731. doi: 10.3390/jmse12101731Krishnamurthy, R., and Milani, A. (2025). Graphene–pla printed sensor combined with xr and the Iot for enhanced temperature monitoring: a case study. J. Sens. Actuator Netw. 14, 1–28. doi: 10.3390/jsan14040068Kumar, J., Gupta, R., Sharma, S., Chakrabarti, T., Chakrabarti, P., Margala, M., et al. (2024). Iot-enabled advanced water quality monitoring system for pond management and environmental conservation. IEEE Access 12, 58156–58167. doi: 10.1109/ACCESS.2024.3391807Kumar, M., Singh, T., Maurya, M. K., Shivhare, A., Raut, A., Singh, P. K., et al. (2023). Quality assessment and monitoring of river water using Iot infrastructure. IEEE Internet Things J. 10, 10280–10290. doi: 10.1109/JIot.2023.3238123Kumar, P., and Choudhury, D. (2024). “Innovative technologies for effective water resources management,” in Water Crises and Sustainable Management in the Global South (Singapore: Springer), 555–594. doi: 10.1007/978-981-97-4966-9_18Lal, K., Menon, S., Noble, F., and Mahmood, K. (2025). Low-cost Iot based system for lake water quality monitoring. PLoS ONE 19, 1–21. doi: 10.1371/journal.pone.0299089Lee, M. H., Won, J., Chung, S., Kim, S., and Park, S. (2022). Rapid detection of ionic contents in water through sensor fusion and convolutional neural network. Chemosphere 294:133746. doi: 10.1016/j.chemosphere.2022.133746Li, J., Li, L., Song, Y., Chen, J., Wang, Z., Bao, Y., et al. (2023). A robust largescale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data. Int. J. Appl. Earth Observ. Geoinform. 118:103288. doi: 10.1016/j.jag.2023.103288Li, L., Mitropoulos, S., Liew, J. T., Saleh, N. L., and Ali, A. M. (2025). Machine learning for peatland ground water level (GWL) prediction via Iot system. IEEE Access 12, 89585–89598. doi: 10.1109/ACCESS.2024.3419237Liu, Q. (2021). Intelligent water quality monitoring system based on multisensor data fusion technology. Int. J. Ambient Comput. Intell. 12, 43–63. doi: 10.4018/IJACI.2021100103Lloret, J., Tomas, J., Canovas, A., and Parra, L. (2016). An integrated Iot architecture for smart metering. IEEE Commun. Mag. 54, 50–57. doi: 10.1109/MCOM.2016.1600647CMLópez-Muñoz, M. A., Torrealba-Meléndez, R., Arriaga-Arriaga, C. A., TamarizFlores, E. I., López-López, M., Quirino-Morales, F., et al. (2024). Wireless dynamic sensor network for water quality monitoring based on the Iot. Technologies 12:211. doi: 10.3390/technologies12110211Mahomed, A., and Saha, A. (2025). Unleashing the potential of 5g for smart cities: a focus on real-time digital twin integration. Smart Cities 8, 70–91. doi: 10.3390/smartcities8020070Maia, R., Lurbe, C., and Hornbuckle, J. (2022). Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data. Front. Plant Sci. 13, 19–35. doi: 10.3389/fpls.2022.931491Manjakkal, L., Mitra, S., Petillot, Y. R., Shutler, J., Scott, E. M., Willander, M., et al. (2021). Connected sensors, innovative sensor deployment, and intelligent data analysis for online water quality monitoring. IEEE Internet Things J. 8, 13805–13824. doi: 10.1109/JIot.2021.3081772Manocha, A., Sood, S. K., and Bhatia, M. (2024). Iot-digital twin-inspired smart irrigation approach for optimal water utilization. Sustain. Comput. Inform. Syst. 41:100947. doi: 10.1016/j.suscom.2023.100947Manzione, R. L., and Castrignanò, A. (2019). A geostatistical approach for multisource data fusion to predict water table depth. Sci. Total Environ. 696:133763. doi: 10.1016/j.scitotenv.2019.133763Marzi, G., Balzano, M., Caputo, A., and Pellegrini, M. M. (2025). Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. Int. J. Manag. Rev. 27, 81–103. doi: 10.1111/ijmr.12381Masood, F., Khan, W. U., Jan, S. U., and Ahmad, J. (2023). Ai-enabled traffic control prioritization in software-defined Iot networks for smart agriculture. Sensors 23:8218. doi: 10.3390/s23198218Mastan Vali, S. (2024). Enhancing coverage and efficiency in wireless sensor networks: a review of optimization techniques. Adv. Eng. Intell. Syst. 3, 39–52.Miao, H. Y., Yang, C. T., Kristiani, E., Fathoni, H., Lin, Y. S., Chen, C. Y., et al. (2022). On construction of a campus outdoor air and water quality monitoring system using lorawan. Appl. Sci. 12:5018. doi: 10.3390/app12105018Miller, M., Kisiel, A., Cembrowska-Lech, D., Durlik, I., and Miller, T. (2023). Iot in water quality monitoring—are we really here? Sensors 23:960. doi: 10.3390/s23020960Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzinska, A., Sokołowska, S., ´ et al. (2025). Integrating artificial intelligence agents with the internet of things for enhanced environmental monitoring: applications in water quality and climate data. Electronics 14:696. doi: 10.3390/electronics14040696Mirzavand, R., Honari, M. M., and Mousavi, P. (2017). Direct-conversion sensor for wireless sensing networks. IEEE Trans. Ind. Electron. 64, 9675–9682. doi: 10.1109/TIE.2017.2716863Mohaimenuzzaman, M., Rahman, S. M. M., Alhussein, M., Muhammad, G., and Mamun, K. A. A. (2016). Enhancing safety in water transport system based on internet of things for developing countries. Int. J. Distrib. Sens. Netw. 12:2834616. doi: 10.1155/2016/2834616Mohammadi, M., Assaf, G., Assaad, R. H., and Chang, A. J. (2024). An intelligent cloud-based Iot-enabled multimodal edge sensing device for automated, real-time, comprehensive, and standardized water quality monitoring and assessment process using multisensor data fusion technologies. J. Comput. Civil Eng. 38:04024029. doi: 10.1061/JCCEE5.CPENG-5989Moiroux-Arvis, L., Royer, L., Sarramia, D., Sousa, G. D., Claude, A., Latour, D., et al. (2023). Connecsens, a versatile Iot platform for environment monitoring: bring water to cloud. Sensors 23:2896. doi: 10.3390/s23062896Morchid, A., Said, Z., Abdelaziz, A. Y., Siano, P., and Qjidaa, H. (2025). Fuzzy logic-based Iot system for optimizing irrigation with cloud computing: enhancing water sustainability in smart agriculture. Smart Agric. Technol. 11:231. doi: 10.1016/j.atech.2025.100979Moreno, C., Aquino, R., Ibarreche, J., Pérez, I., Castellanos, E., Álvarez, E., et al. (2019). Rivercore: Iot device for river water level monitoring over cellular communications. Sensors 19:127. doi: 10.3390/s19010127Mukta, M., Islam, S., Barman, S. D., Reza, A. W., and Khan, M. S. H. (2019). “Iot based smart water quality monitoring system,” in Proceedings of the 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (Singapore: IEEE), 669–673. doi: 10.1109/CCOMS.2019.8821742Mutunga, T., Sinanovic, S., and Harrison, C. (2024). A wireless network for monitoring pesticides in groundwater: an inclusive approach for a vulnerable kenyan population. Sensors 24:4665. doi: 10.3390/s24144665Ogenyi, F. Ugwu1, C., Ugwu, O. (2025). Securing the future: AI-driven cybersecurity in the age of autonomous Iot. Front. Internet Things 4:1658273. doi: 10.3389/friot.2025.1658273Olatinwo, S. O., and Joubert, T. H. (2020). Energy efficiency maximization in a wireless powered Iot sensor network for water quality monitoring. Comput. Netw. 176:107237. doi: 10.1016/j.comnet.2020.107237Omrany, H., Al-Obaidi, K. M., Husain, A., and Ghaffarianhoseini, A. (2023). Digital twins in the construction industry: a comprehensive review of current implementations, enabling technologies, and future directions. Sustainability 15:10908. doi: 10.3390/su151410908Ortega, L. R. M. (2023). A Digital Twin for Ground Water Table Monitoring [Master’s thesis]. University of Twente, Enschede.Ortiz, M. E., Molina, J. P. A., Jiménez, S. I. B., Barrientos, J. H., Guevara, H. J. P., Miranda, A. S., et al. (2023). Development of low-cost Iot system for monitoring piezometric level and temperature of groundwater. Sensors 23:9364. doi: 10.3390/s23239364Pasika, S., and Gandla, S. T. (2020). Smart water quality monitoring system with cost-effective using Iot. Heliyon 6:e04096. doi: 10.1016/j.heliyon.2020.e04096Pointet, T. (2022). The united nations world water development report 2022 on groundwater, a synthesis. LHB 108:2090867. doi: 10.1080/27678490.2022.2090867Prabu, T., Sarkar, M., Chaudhary, D., Obaid, S. A., Khalid, T. A., and Kalam, M. A. (2025). Iot-enabled groundwater monitoring with k-nn-svm algorithm for sustainable water management. Acta Geophys. 72, 2715–2728. doi: 10.1007/s11600-023-01178-2Primeau, R. B. (2024). Framework for a Digital Twin of Brusdalsvatnet Water Quality [Master’s thesis]. Norwegian University of Science and Technology, Trondheim.Priyanka, E. B., Thangavel, S., Mohanasundaram, R., and Anand, R. (2024). Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with kalman filter. Sci. Rep. 14:25202. doi: 10.1038/s41598-024-76068-8Promput, S., Maithomklang, S., and Panya-isara, C. (2023). Design and analysis performance of Iot-based water quality monitoring system using lora technology. TEM J. 12, 29–35. doi: 10.18421/TEM121-04Qiao, J., Lin, Y., Bi, J., and Yuan, H. (2024). Attention-based spatiotemporal graph fusion convolution networks for water quality prediction. Sens. Int. 22, 1–22. doi: 10.1109/TASE.2023.3285253Rahman, A., Yap, B. K., Murad, W., and Islam, Z. (2025). Low-cost Iot solution for real-time monitoring of aquaculture water parameters. Interdiscip. J. Papua New Guinea Univ. Technol. 2, 1–11. doi: 10.63900/dc24fx27Rahu, M. A., Shaikh, M. M., Karim, S., Soomro, S. A., Hussain, D., Ali, S. M., et al. (2024). Water quality monitoring and assessment for efficient water resource management through internet of things and machine learning approaches for agricultural irrigation. Water Resour. Manag. 38, 4987–5028. doi: 10.1007/s11269-024-03899-5Rana, M. S., Nobi, M. N., Murali, B., and Sung, A. H. (2022). Deepfake detection: a systematic literature review. IEEE Access 10, 25494–25513. doi: 10.1109/ACCESS.2022.3154404Raphael, R., Sarukkalige, R., Sarukkalige, R., and Agrawal, H. (2025). Performance evaluation of chaosfortress lightweight cryptographic algorithm for data security in water and other utility management. Sensors 25, 1–21. doi: 10.3390/s251 65103Razaque, A., Hariri, S., Alajlan, A. M., and Yoo, J. (2025). A comprehensive review of cybersecurity vulnerabilities, threats, and solutions for the internet of things at the network-cum-application layer. Comput. Sci. Rev. 58, 10–41. doi: 10.1016/j.cosrev.2025.100789Rueda, J. S., and Talavera, J. M. (2017). Similarities and differences between wireless sensor networks and the internet of things: towards a clarifying position. Rev. Colomb. Computación 18, 58–74. doi: 10.29375/25392115.3218Saghir, A., Akbar, A., Hasan, A., and Zafar, A. (2025). Deep learning for multimodal data fusion in Iot applications. Mehran Univ. Res. J. Eng. Technol. 44, 75–81. doi: 10.22581/muet1982.3171Saheed, Y., Omole, A., and Sabit, M. (2025). Ga-madam-IIoT: a new lightweight threats detection in the industrial Iot via genetic algorithm with attention mechanism and lstm on multivariate time series sensor data. Sens. Int. 6, 1–22. doi: 10.1016/j.sintl.2024.100297Saranya, K., and Valarmathi, A. (2025). A multilayer deep autoencoder approach for cross layer Iot attack detection using deep learning algorithms. Sci. Rep. 15, 1–30. doi: 10.1038/s41598-025-93473-9Shemer, H., Wald, S., and Semiat, R. (2023). Challenges and solutions for global water scarcity. Membranes 13:612. doi: 10.3390/membranes13060612Shen, B. (2023). Study on Data Fusion Method Based on AWDF and LSTM for Water Environment Monitoring in WSNs [PhD thesis]. Tokyo University of Marine Science and Technology, Tokyo.Shukla, S. (2023). Improving latency in internet-of-things and cloud computing for real-time data transmission: a systematic literature review (SLR). Cluster Comput. 26, 2657–2680. doi: 10.1007/s10586-021-03279-3Singh, M., Sahoo, K. S., and Gandomi, A. H. (2023). An intelligent-IoT-based data analytics for freshwater recirculating aquaculture system. IEEE Internet Things J. 11, 4206–4217. doi: 10.1109/JIot.2023.3298844Slany, V., Krcalova, E., Balej, J., Zach, M., and Kucova, T. (2025). Smart water-IoT: harnessing IoT and AI for efficient water management. ACM Comput. Surv. 57, 1–36. doi: 10.1145/3744338Slaný, V., Lucanský, A., Koudelka, P., Mare ˇ cek, J., Kr ˇ cálová, E., Martínek, R., et al. ˇ (2020). An integrated Iot architecture for smart metering using next generation sensor for water management based on lorawan technology: a pilot study. Sensors 20:4712. doi: 10.3390/s20174712Sonbul, O. S., and Rashid, M. (2023). Algorithms and techniques for the structural health monitoring of bridges: systematic literature review. Sensors 23:4230. doi: 10.3390/s23094230Syed, T. A., Muhammad, M. M. AlShahrani, A. A., Hammad, M., and Naqash, M. T. (2024). Smart water management with digital twins and multimodal transformers: a predictive approach to usage and leakage detection. Water 16:3410. doi: 10.3390/w16233410Truong, V. T., Nayyar, A., and Lone, S. A. (2021). System performance of wireless sensor network using Lora-Zigbee hybrid communication. Comput. Mater. Contin. 68, 1615–1635. doi: 10.32604/cmc.2021.016922Tu, L. T., Bradai, A., Pousset, Y., and Aravanis, A. I. (2022). On the spectral efficiency of lora networks: performance analysis, trends and optimal points of operation. IEEE Trans. Commun. 70, 2788–2804. doi: 10.1109/TCOMM.2022. 3148784Vadone, S., Shaji, S., and Sundaram, M. (2025). Water management prediction using deep convolutional spiking neural network optimized with red fox optimization algorithm based on Iot. Sens. Imaging 26, 3556–3568. doi: 10.1007/s11220-024- 00533-xVenkatesh, J., Partheeban, P., Baskaran, A., Krishnan, D., and Sridhar, M. (2025). Wireless sensor network technology and geospatial technology for groundwater quality monitoring. J. Ind. Inf. Integ. 38, 1–38. doi: 10.1016/j.jii.2024.100569Wang, A. J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., et al. (2024). Digital twins for wastewater treatment: a technical review. Engineering 36, 21–35. doi: 10.1016/j.eng.2024.04.012Wang, M., Shi, B., Catsamas, S., Kolotelo, P., and McCarthy, D. (2024). A compact, low-cost, and low-power turbidity sensor for continuous in situ stormwater monitoring. Sensors 24:3926. doi: 10.3390/s24123926Wang, S., Wu, M., Wei, X., and Song, A. (2025). An advanced multi-source data fusion method utilizing deep learning techniques for fire detection. Eng. Appl. Artif. Intell. 142, 1–21. doi: 10.1016/j.engappai.2024.109902Wang, S., and Xu, O. (2024). Interoperability structure of smart water conservancy based on internet of things. Int. J. Distrib. Sens. Netw. 2024:7724783. doi: 10.1155/2024/7724783Wang, Y., Xie, C., Liu, Y., Zhu, J., and Qin, J. (2024). A multi-sensor fusion underwater localization method based on unscented kalman filter on manifolds. Sensors 24:6299. doi: 10.3390/s24196299Wong, Y. J., Nakayama, R., Shimizu, Y., Kamiya, A., Shen, S., Rashid, I. Z. M., et al. (2021). Toward industrial revolution 4.0: development, validation, and application of 3d-printed Iot-based water quality monitoring system. J. Clean. Prod. 324:129230. doi: 10.1016/j.jclepro.2021.129230Yahya, H., Al-Dweik, A., Iraqi, Y., Alsusa, E., and Ahmed, A. (2022). A power and spectrum efficient uplink transmission scheme for QOS-constrained Iot networks. IEEE Internet Things J. 9, 17425–17439. doi: 10.1109/JIot.2022.3156209Yalli, J. S., Hasan, M. H., and Badawi, A. (2024). Internet of things (Iot): origin, embedded technologies, smart applications and its growth in the last decade. IEEE Access. 12, 91357–91382. doi: 10.1109/ACCESS.2024.341899Yin, W., Hu, Q., Liu, W., Liu, J., He, P., Zhu, D., et al. (2024). Harnessing game engines and digital twins: advancing flood education, data visualization, and interactive monitoring for enhanced hydrological understanding. Water 16:2528. doi: 10.3390/w161 72528Zhang, H., Leng, L., Adeleke Qiao, Z., Zhang, Z., and Shi, Z. (2023). “Design and implementation of a multi-sensor online water quality monitoring system,” in Proceedings of the 2023 4th International Conference on Computer Science and Management Technology (New York, NY: ACM), 207–212 doi: 10.1145/3644523.3644562Zhang, L., Banihashemi, S., Zhu, L., Molavi, H., Odacioglu, E., Shan, M., et al. (2024). A scientometric analysis of knowledge transfer partnerships in digital transformation. J. Open Innov. Technol. Mark. Complex. 10, 1–15. doi: 10.1016/j.joitmc.2024.100325Zhang, R., Zhu, H., Chang, Q., and Mao, Q. (2025). A comprehensive review of digital twins technology in agriculture. Agriculture 15, 1–21. doi: 10.3390/agriculture15090903ColombiaComunidad AcadémicaPublicationecd4ff40-dc83-492b-89ac-7900e78d9877virtual::6706-1df241bbc-948c-4c12-9eef-31ffcc5aa338virtual::6707-162d4fdb2-3ee9-4664-9a51-e2913dfb115evirtual::6708-1ecd4ff40-dc83-492b-89ac-7900e78d9877virtual::6706-1df241bbc-948c-4c12-9eef-31ffcc5aa338virtual::6707-162d4fdb2-3ee9-4664-9a51-e2913dfb115evirtual::6708-1ORIGINALfrwa-7-1699240.pdffrwa-7-1699240.pdfapplication/pdf242007https://repositorio.utb.edu.co/bitstreams/44b3b41c-1081-4aa8-93b4-297d16f431f8/download9f5c144fb58d870caa5af089267b1b62MD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814837https://repositorio.utb.edu.co/bitstreams/788114f8-9f13-4672-a349-7582bae5dff7/downloadb76e7a76e24cf2f94b3ce0ae5ed275d0MD52falseAnonymousREADTEXTfrwa-7-1699240.pdf.txtfrwa-7-1699240.pdf.txtExtracted texttext/plain100164https://repositorio.utb.edu.co/bitstreams/4071218e-e7f2-4ed7-a1a0-9a97c630cb6e/downloadb143ad33c22e3e04276dd0007f025809MD53falseAnonymousREADTHUMBNAILfrwa-7-1699240.pdf.jpgfrwa-7-1699240.pdf.jpgGenerated Thumbnailimage/jpeg11824https://repositorio.utb.edu.co/bitstreams/f5fce718-f99d-4c3d-8d5a-8705bb51465b/download4bd0d26f3a9d4ac32ab0c43b372ca8b1MD54falseAnonymousREAD20.500.12585/14293oai:repositorio.utb.edu.co:20.500.12585/142932026-01-17 03:00:23.531https://creativecommons.org/licenses/by-nc-sa/4.0/© 2025 Cohen-Manrique, Camacho-Leon and Villa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.open.accesshttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.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

Emerging trends in IoT for aquatic systems: a systematic literature review

Publicaciones similares