Publicación: Emerging trends in IoT for aquatic systems: a systematic literature review
| dc.contributor.author | Cohen Manrique, Carlos | |
| dc.contributor.author | Camacho-Leon, Sergio | |
| dc.contributor.author | Villa Ramírez, José Luis | |
| dc.contributor.researchgroup | Grupo de Investigación Automatización Industrial y Control (GAICO) | |
| dc.contributor.seedbeds | Semillero de Investigación en Automatización y Control | |
| dc.coverage.country | Colombia | |
| dc.date.accessioned | 2026-01-16T21:19:54Z | |
| dc.date.available | 2025-12-04 | |
| dc.date.issued | 2025-12-04 | |
| dc.description.abstract | 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. | |
| dc.description.researcharea | Automatización y control de procesos industriales | |
| dc.description.tableofcontents | 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 Conclusion | |
| dc.format.extent | 18 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | 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 | doi: 10.3389/frwa.2025.1699240 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12585/14293 | |
| dc.language.iso | eng | |
| dc.publisher | Front. Water | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | Anker, C. (2023). Digital Twin Paradigms Towards Monitoring Insights for Deep Aquifer Pumps [PhD thesis]. Stellenbosch University, Stellenbosch. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | :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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | Mastan Vali, S. (2024). Enhancing coverage and efficiency in wireless sensor networks: a review of optimization techniques. Adv. Eng. Intell. Syst. 3, 39–52. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | Ortega, L. R. M. (2023). A Digital Twin for Ground Water Table Monitoring [Master’s thesis]. University of Twente, Enschede. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | Pointet, T. (2022). The united nations world water development report 2022 on groundwater, a synthesis. LHB 108:2090867. doi: 10.1080/27678490.2022.2090867 | |
| dc.relation.references | 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 | |
| dc.relation.references | Primeau, R. B. (2024). Framework for a Digital Twin of Brusdalsvatnet Water Quality [Master’s thesis]. Norwegian University of Science and Technology, Trondheim. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | Shemer, H., Wald, S., and Semiat, R. (2023). Challenges and solutions for global water scarcity. Membranes 13:612. doi: 10.3390/membranes13060612 | |
| dc.relation.references | 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. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | © 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. | |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
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| dc.rights.license | Atribución 4.0 Internacional (CC BY 4.0) | |
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| dc.subject.ddc | 330 - Economía::333 - Economía de la tierra y de la energía | |
| dc.subject.lemb | Recursos hídricos -- Gestión | |
| dc.subject.lemb | Escasez de agua | |
| dc.subject.lemb | Cambio climático | |
| dc.subject.lemb | Internet de las cosas | |
| dc.subject.lemb | Monitoreo ambiental | |
| dc.subject.lemb | Water Resources -- Management | |
| dc.subject.lemb | Water Scarcity | |
| dc.subject.lemb | Climate Change | |
| dc.subject.lemb | Internet of Things | |
| dc.subject.lemb | Environmental Monitoring | |
| dc.subject.ocde | 2. Ingeniería y Tecnología | |
| dc.subject.ods | ODS 6: Agua limpia y saneamiento. Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos | |
| dc.subject.proposal | Internet of Things | |
| dc.subject.proposal | sensor fusion | |
| dc.subject.proposal | systematic literature review | |
| dc.subject.proposal | water quality | |
| dc.subject.proposal | water resources | |
| dc.title | Emerging trends in IoT for aquatic systems: a systematic literature review | |
| dc.type | Artículo de revista | |
| dc.type.coar | http://purl.org/coar/resource_type/c_18cf | |
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| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dcterms.audience | Comunidad Académica | |
| dspace.entity.type | Publication | |
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