A Smart Approach to Aquatic Ecosystem Protection: IoT-Based Water Quality Monitoring System (WQMAS)
DOI:
https://doi.org/10.56532/mjsat.v5i3.529Keywords:
IoT, Sustainable Development, Real-Time , Smart Water ManagementAbstract
In an era increasingly threatened by pollution and climate change, water quality is under serious risk. Spatial water quality monitoring and traditional laboratory testing are inadequate to protect aquatic ecosystems. An innovative solution emerges from the Internet of Things (IoT), which enables the Water Quality Monitoring System (WQMAS) to transform traditional water resource monitoring practices. Through advanced sensor technologies, this system obtains real-time critical measurements including ambient temperature (25°C–30°C), water temperature (25°C–30°C), turbidity, pH values, electrical conductivity (500–1000 µS/cm), and dissolved oxygen levels. The system automatically pushes data into an easy-to-use IoT dashboard that allows users to immediately understand and assess data for smart and knowledgeable choices. The system utilizes Polyethylene Terephthalate Glycol-modified (PETG) material for durable 3D-printed enclosures that make it affordable and suitable for different fields due to its scalable characteristics and environmental resiliency. The automated system for water quality checks removes manual errors and performs fast and precise checks that manual operations cannot match. The technical capabilities of this solution become part of a sustainability showcase which enables communities to protect aquatic ecosystems while enhancing water resource optimization for creating a more environmentally friendly world. Within the water management industry, the Water Quality Monitoring System operates beyond its status as a tool to serve as a fundamental step towards sustainable development.
References
I. Essamlali, H. Nhaila, and M. El Khaili, “Advances in machine learning and IoT for water quality monitoring: A comprehensive review,” Heliyon, vol. 10, no. 6, p. e27920, Mar. 2024, doi: 10.1016/j.heliyon.2024.e27920.
C. Z. Zulkifli et al., “IoT-Based Water Monitoring Systems: A Systematic Review,” Water, vol. 14, no. 22, p. 3621, Nov. 2022, doi: 10.3390/w14223621.
G. N. Satya Sai, R. Sudheer, K. S. Manikanta, S. G. Arjula, B. N. Rao, and D. V. Sai Maneeswar Mutyala, “IoT based Water Quality Monitoring System,” in 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India: IEEE, Sep. 2021, pp. 01–06. doi: 10.1109/R10-HTC53172.2021.9641630.
M. Rahman, C. Bapery, M. J. Hossain, Z. Hassan, G. M. J. Hossain, and M. Islam, “Internet of Things (IoT) Based Water Quality Monitoring System,” Internet Things.
K. Lal, S. Menon, F. Noble, and K. M. Arif, “Low-cost IoT based system for lake water quality monitoring,” PLOS ONE, vol. 19, no. 3, p. e0299089, Mar. 2024, doi: 10.1371/journal.pone.0299089.
P. Di Felice and G. Paolone, “Papers Mentioning Things Board: A Systematic Mapping Study,” J. Comput. Sci., vol. 20, no. 5, pp. 574–584, May 2024, doi: 10.3844/jcssp.2024.574.584.
S. Zhuiykov, D. O’Brien, and M. Best, “Water quality assessment by an integrated multi-sensor based on semiconductor RuO2 nanostructures,” Meas. Sci. Technol., vol. 20, no. 9, p. 095201, Sep. 2009, doi: 10.1088/0957-0233/20/9/095201.
B. Bach-Gia, L. Luu-Trinh, M. Nguyen-Dinh, T. Pham-Dinh, and C. Pham-Quoc, “An IoT Solution for Multiple Sensors Control and Management,” in 2022 9th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, Vietnam: IEEE, Oct. 2022, pp. 117–122. doi: 10.1109/NICS56915.2022.10013474.
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, Sep. 2013, doi: 10.1016/j.future.2013.01.010.
Y. Ahmed Alkhamis et al., “The Impact of Biofloc Technology on Water Quality in Aquaculture: A Systematic Meta-Analysis,” Aquac. Nutr., vol. 2023, pp. 1–20, Oct. 2023, doi: 10.1155/2023/9915874.
G. Holcomb, E. B. Caldona, X. Cheng, and R. C. Advincula, “On the optimized 3D printing and post-processing of PETG materials,” MRS Commun., vol. 12, no. 3, pp. 381–387, Jun. 2022, doi: 10.1557/s43579-022-00188-3.
R. K. Upadhyay, A. K. Mishra, and A. Kumar, “Mechanical Degradation of 3D Printed PLA in Simulated Marine Environment,” Surf. Interfaces, vol. 21, p. 100778, Dec. 2020, doi: 10.1016/j.surfin.2020.100778.
J. R. Rocca-Smith et al., “Effect of the state of water and relative humidity on ageing of PLA films,” Food Chem., vol. 236, pp. 109–119, Dec. 2017, doi: 10.1016/j.foodchem.2017.02.113.
S. Chopra et al., “Explication of mechanism governing atmospheric degradation of 3D-printed poly(lactic acid) (PLA) with different in-fill pattern and varying in-fill density,” RSC Adv., vol. 13, no. 11, pp. 7135–7152, 2023, doi: 10.1039/D2RA07061H.
J. Z. Gul, M. Khan, M. M. Rehman, Z. Mohy Ud Din, and W. Y. Kim, “Preparation and Performance Analysis of 3D Thermoformed Fluidic Polymer Temperature Sensors for Aquatic and Terrestrial Applications,” Sensors, vol. 23, no. 20, p. 8506, Oct. 2023, doi: 10.3390/s23208506.
A. Delgado et al., “Assessment of biofouling on typical marine sensors materials,” in OCEANS 2023 - Limerick, Limerick, Ireland: IEEE, Jun. 2023, pp. 1–8. doi: 10.1109/OCEANSLimerick52467.2023.10244559.
Leonila, T., Senthil, G., Geerthik, S., Sowmiya, R., & Nithish, J. (2024). Dynamic Water Quality Monitoring via IoT Sensor Networks and Machine Learning Technique. 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT), 1-6. https://doi.org/10.1109/IC3IoT60841.2024.10550224.
Hussein, E., Derdour, A., Zerouali, B., Almaliki, A., Wong, Y., Santos, M., Ngoc, P., Hashim, M., & Elbeltagi, A. (2024). Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms. Water. https://doi.org/10.3390/w16020264.
Wong, Y., Shimizu, Y., He, K., & Sulaiman, N. (2020). Comparison among different ASEAN water quality indices for the assessment of the spatial variation of surface water quality in the Selangor river basin, Malaysia. Environmental Monitoring and Assessment, 192. https://doi.org/10.1007/s10661-020-08543-4.
Naloufi, M., Abreu, T., Souihi, S., Thérial, C., De Ponte Rodrigues, N., Goff, A., Saad, M., Vinçon‐Leite, B., Dubois, P., Delarbre, M., Kennouche, P., & Lucas, F. (2024). Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers. Water. https://doi.org/10.3390/w16121708.
Nalakurthi, N., Abimbola, I., Ahmed, T., Anton, I., Riaz, K., Ibrahim, Q., Banerjee, A., Tiwari, A., & Gharbia, S. (2024). Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors. Sensors (Basel, Switzerland), 24. https://doi.org/10.3390/s24113650.
Sharma, H., & Sharma, S. (2014). A review of sensor networks: Technologies and applications. 2014 Recent Advances in Engineering and Computational Sciences (RAECS), 1-4. https://doi.org/10.1109/RAECS.2014.6799579.
Kedia, N. (2015). Water quality monitoring for rural areas- a Sensor Cloud based economical project. 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 50-54. https://doi.org/10.1109/NGCT.2015.7375081.
Chandalwar, K., Barde, N., Pureddi, S., Uike, T., Yadgiri, N., & Dumbere, P. (2024). Water Quality Monitoring System Based on IoT. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-22300.
Aldabagh, H., & Talal, R. (2025). Hybrid Intelligent Technique between Supervised and Unsupervised Machine Learning to Predict Water Quality. International Journal of Computing and Digital Systems. https://doi.org/10.12785/ijcds/1571031447.
Aher, S., Kalamb, S., Sawase, A., & Jidge, S. (2022). Smart Water Quality Based on IoT. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-2958.
Cloete, N., Malekian, R., & Nair, L. (2016). Design of Smart Sensors for Real-Time Water Quality Monitoring. IEEE Access, 4, 3975-3990. https://doi.org/10.1109/ACCESS.2016.2592958.
Candra, H., Noor, S., Bahit, M., & Mulyani, D. (2024). Prediction of Freshwater Fish Pond Water Quality Levels Using The Backpropagation Method Based On The Internet of Things (IoT). International Journal of Science, Technology & Management. https://doi.org/10.46729/ijstm.v4i5.857.
Adamo, F., Attivissimo, F., Carducci, G., & Lanzolla, A. (2015). A Smart Sensor Network for Sea Water Quality Monitoring. IEEE Sensors Journal, 15, 2514-2522. https://doi.org/10.1109/JSEN.2014.2360816.
Surasak, T., Kitchat, K., & Jiteurtragool, N. (2024). IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment. 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), 18-23. https://doi.org/10.1109/CSPA60979.2024.10525617.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ahmad Anwar Zainuddin, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Amir ‘Aatieff Amir Hussin, Muhammad Irfan Zaki Mohd Razali, Normawaty Mohammad-Noor, Roziawati Mohd Razali, Muhd Farouk Harman
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
