IOT-BASED LOW-COST SENSOR INSTRUMENTATION FOR REAL-TIME WATER QUALITY MONITORING: DESIGN, CALIBRATION, AND FIELD VALIDATION

Muhammad Firdaus Abduh (1), Zain Nizam (2), Rashid Rahman (3)
(1) Universitas Sains dan Teknologi JayapuraID Indonesia,
(2) Universiti Malaysia SarawakMY Malaysia,
(3) Universiti PutraMY Malaysia

Abstract

Water-quality deterioration requires timely and reliable monitoring, yet conventional laboratory-based approaches often face limitations in cost, sampling frequency, spatial coverage, and response time, particularly in resource-constrained environments. This study aimed to design, calibrate, and validate a low-cost Internet of Things sensor instrumentation system for continuous real-time water-quality monitoring. An experimental engineering design integrated multi-parameter sensing, embedded processing, wireless communication, laboratory calibration, reference-based comparison, field validation, data-transmission assessment, and threshold-based alert evaluation. The results demonstrated strong calibration performance across monitored parameters, although field accuracy varied according to sensor type and environmental conditions. Temperature and pH exhibited comparatively stable performance, whereas turbidity and dissolved oxygen showed greater field-related measurement variability. The system maintained high data completeness and communication reliability while successfully capturing short-duration water-quality changes that periodic sampling could potentially miss. Field validation confirmed that strong laboratory calibration alone did not guarantee equivalent performance under natural environmental conditions. The study concludes that low-cost IoT instrumentation provides a promising fit-for-purpose platform for continuous surveillance, temporal pattern detection, and early warning when supported by rigorous calibration and field validation. The proposed end-to-end framework strengthens the development of scalable, reliable, and context-sensitive water-quality monitoring systems for environmental management.

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References

Asghar, M. Z., Imran, H., shan, A., Lu, B., & Lim, S. (2025). Development of a wireless vitamin B2 sensor by using flexible and disposable screen-printed electrode modified with g-C3N4 based ternary composite. Analytica Chimica Acta, 1378, 344702. https://doi.org/https://doi.org/10.1016/j.aca.2025.344702

Bernardes, G. F. L. R., Ishibashi, R., Ivo, A. A. S., Rosset, V., & Kimura, B. Y. L. (2023). Prototyping low-cost automatic weather stations for natural disaster monitoring. Digital Communications and Networks, 9(4), 941–956. https://doi.org/https://doi.org/10.1016/j.dcan.2022.05.002

Bitra, V. S., Verma, S., & Rao, B. T. (2024). TinyML-Raman: A novel IoT based field-deployable spectra analysis for accurate identification of pharmaceuticals and trace dye-pesticide mixtures from facile SERS method. Analytica Chimica Acta, 1322, 343063. https://doi.org/https://doi.org/10.1016/j.aca.2024.343063

Casari, M., Kowalski, P. A., & Po, L. (2024). Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations. Ecological Informatics, 83, 102781. https://doi.org/https://doi.org/10.1016/j.ecoinf.2024.102781

Chatterjee, D., & Tiwari, I. (2025). Electrochemical sensing of benzodiazepines: Tracing the evolution of carbonaceous nano-hybrid materials from 3D to 0D, their integration into smart technologies for real-time monitoring. Carbon Trends, 20, 100548. https://doi.org/https://doi.org/10.1016/j.cartre.2025.100548

Chaves, A. J., Martín, C., Llopis Torres, L., Díaz, M., Fernández-Ortega, J., Barberá, J. A., & Andreo, B. (2025). A soft sensor open-source methodology for inexpensive monitoring of water quality: A case study of NO3? concentrations. Journal of Computational Science, 85, 102522. https://doi.org/https://doi.org/10.1016/j.jocs.2024.102522

Chavhan, N., Bhattad, R., Khot, S., Patil, S., Pawar, A., Pawar, T., & Gawli, P. (2025). APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater. Digital Chemical Engineering, 14, 100217. https://doi.org/https://doi.org/10.1016/j.dche.2025.100217

Cherif, I., Cherqui, F., Perret, F., Bourjaillat, B., Lord, L., Bertrand-Krajewski, J.-L., Walcker, N., Gisi, M., Bacot, L., & Navratil, O. (2025). LevelWAN: a cost-effective, open-source IoT system for water level monitoring in highly dynamic aquatic environments. HardwareX, 23, e00685. https://doi.org/https://doi.org/10.1016/j.ohx.2025.e00685

Chicaiza, W. D., Topa, A. O., Sánchez, A. J., Escaño, J. M., & Álvarez, J. D. (2025). Model design for photovoltaic facilities based on fuzzy neural network as core of its digital twin. Energy Conversion and Management, 342, 120001. https://doi.org/https://doi.org/10.1016/j.enconman.2025.120001

Corsino, V., Ruiz-Díez, V., & Sánchez-Rojas, J. L. (2025). Smart density and viscosity sensing based on edge machine learning and piezoelectric MEMS for edible oil monitoring. Sensors and Actuators A: Physical, 385, 116258. https://doi.org/https://doi.org/10.1016/j.sna.2025.116258

Cowell, N., Chapman, L., Bloss, W., Srivastava, D., Bartington, S., & Singh, A. (2023). Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring. Environmental Science: Atmospheres, 3(1), 65–84. https://doi.org/https://doi.org/10.1039/d2ea00124a

Deng, S., Zhang, Z., Wang, Y., Li, B., & Zheng, W. (2025). Design and validation of a low-cost data-driven poultry heat and moisture production monitoring system. Poultry Science, 104(11), 105889. https://doi.org/https://doi.org/10.1016/j.psj.2025.105889

Dhote, G. M., Kandavalli, S. R., Prasanna Kumar, K., Madanan, M., Victor, G. J., & Muniyandy, E. (2025). Intelligent carbon nanomaterial electrodes for rapid heavy metal detection in complex water matrices. Microchemical Journal, 218, 115225. https://doi.org/https://doi.org/10.1016/j.microc.2025.115225

Escobar-Díaz, A., Villarreal, A., Zárate-Nicolas, B., & Rojas-Olivos, A. (2025). System design for monitoring REDOX potential in treated water using an ORP sensor. HardwareX, 24, e00717. https://doi.org/https://doi.org/10.1016/j.ohx.2025.e00717

Gragnaniello, C., Mariniello, G., Pastore, T., & Asprone, D. (2024). BIM-based design and setup of structural health monitoring systems. Automation in Construction, 158, 105245. https://doi.org/https://doi.org/10.1016/j.autcon.2023.105245

Hawari, F. A., Muhammad, Y., Suryadi, Salim, A. Q., Lathifah, K., Pradana, Y. R. A., Saputra, A., Kurniawan, H., Ansari, A. S., & Rochman, N. T. (2024). Study of Low-Cost Network-Enabled dissolved oxygen sensor. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2024.03.021

Jabbar, W. A., Mei Ting, T., I. Hamidun, M. F., Che Kamarudin, A. H., Wu, W., Sultan, J., Alsewari, A. A., & Ali, M. A. H. (2024). Development of LoRaWAN-based IoT system for water quality monitoring in rural areas. Expert Systems with Applications, 242, 122862. https://doi.org/https://doi.org/10.1016/j.eswa.2023.122862

Jangir, J., Kiran, Ranolia, A., Priyanka, Chahal, S., Singh, S., Duhan, A., Dhaka, R. K., Singh, D., Kumar, P., & Sindhu, J. (2025). Colorimetric reversibility: Aqueous phase recognition of cyanide using smart phone-based device with real sample analysis. Microchemical Journal, 208, 112259. https://doi.org/https://doi.org/10.1016/j.microc.2024.112259

Jena, T., Shinde, S., Roselin, S. E., Sujana, C., Rao, V. S., & Immanuvel Arokia James, K. (2025). Deep learning-optimized carbon quantum dot biosensors for emerging contaminant monitoring. Microchemical Journal, 218, 115389. https://doi.org/https://doi.org/10.1016/j.microc.2025.115389

Khanghah, M. S., Bonvin, A., & Moghaddam, F. (2025). IoT-enabled experimental validation of fuzzy logic control for in solar thermal collector systems. Solar Energy Advances, 5, 100125. https://doi.org/https://doi.org/10.1016/j.seja.2025.100125

King, B. A., & Shellie, K. C. (2023). A crop water stress index based internet of things decision support system for precision irrigation of wine grape. Smart Agricultural Technology, 4, 100202. https://doi.org/https://doi.org/10.1016/j.atech.2023.100202

Landa, J., Barrios, G., & Huelsz, G. (2025). IoT smartwatch based on open technologies for the collection of thermal comfort data. HardwareX, 22, e00633. https://doi.org/https://doi.org/10.1016/j.ohx.2025.e00633

Lodhi, E., Liu, X., Xiong, G., Khan, M. A., Lodhi, Z., Nawaz, T., Dilawar, A., Tarkoma, S., & Wang, F. (2025). SmartPV-AIoT: an AIoT-integrated framework for fault diagnosis and remote monitoring in photovoltaic systems. Energy Conversion and Management: X, 27, 101117. https://doi.org/https://doi.org/10.1016/j.ecmx.2025.101117

Mai, Y., Ghiasvand, A., Gupta, V., Edwards, S., Cahoon, S., Debruille, K., Mikhail, I., Murray, E., & Paull, B. (2024). Application of a portable ion chromatograph for real-time field analysis of nitrite and nitrate in soils and soil pore waters. Talanta, 274, 126031. https://doi.org/https://doi.org/10.1016/j.talanta.2024.126031

Medina-García, J., Gómez-Galán, J. A., Vilaplana-Guerrero, J. M., & Bogeat, J. A. (2025). Efficient irrigation system using a combined wireless sensor network based on LoRaWAN and IEEE 802.15.4 technologies and photosynthetically active radiation measurements. Internet of Things, 34, 101801. https://doi.org/https://doi.org/10.1016/j.iot.2025.101801

Morales-Aragón, I. P., Gilabert, J., Torres-Sánchez, R., & Soto-Valles, F. (2024). Design and development of a new stand-alone profiler for marine monitoring purposes. Applied Ocean Research, 152, 104199. https://doi.org/https://doi.org/10.1016/j.apor.2024.104199

Moretti, A., Ivan, H. L., & Skvaril, J. (2024). A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible? Journal of Water Process Engineering, 60, 105061. https://doi.org/https://doi.org/10.1016/j.jwpe.2024.105061

Ngwenya, B., Paepae, T., & Bokoro, P. N. (2025). Advancing SDG 6.3.2 with machine learning-based virtual sensors for high-frequency nutrient monitoring. Journal of Water Process Engineering, 79, 108831. https://doi.org/https://doi.org/10.1016/j.jwpe.2025.108831

Obeidat, Y. M., & Rawashdeh, A. M. (2025). Electrochemical lead detection in water using Nafion coated platinum electrodes: A sensitive approach. Results in Chemistry, 18, 102745. https://doi.org/https://doi.org/10.1016/j.rechem.2025.102745

Oliveira, H. M., Tugnolo, A., Fontes, N., Marques, C., Geraldes, Á., Jenne, S., Zappe, H., Graça, A., Giovenzana, V., Beghi, R., Guidetti, R., Piteira, J., & Freitas, P. (2024). An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation. Computers and Electronics in Agriculture, 217, 108599. https://doi.org/https://doi.org/10.1016/j.compag.2023.108599

Ospina-Rojas, E., Botero-Valencia, J., Betancur-Vasquez, D., & Pearce, J. M. (2025). Open-source three-dimensional IoT anemometer for indoor air quality monitoring. HardwareX, 23, e00656. https://doi.org/https://doi.org/10.1016/j.ohx.2025.e00656

Penchala, A., Patra, A. K., Mishra, N., & Santra, S. (2025). Field calibration and performance evaluation of low-cost sensors for monitoring airborne PM in the occupational mining environment. Journal of Aerosol Science, 184, 106519. https://doi.org/https://doi.org/10.1016/j.jaerosci.2024.106519

Queijo, A. R., Frydel, L., Valente, A., Styszko, K., & Rego, R. (2025). Emerging contaminants: the application of a homemade electrochemical IoT-enabled device to solve a global challenge. Electrochimica Acta, 532, 146395. https://doi.org/https://doi.org/10.1016/j.electacta.2025.146395

Ramachandran, R. P., Clément, A., & Erkinbaev, C. (2025). Miniaturized spectroscopy and AI-driven probes in food industry automation. Food Research International, 214, 116646. https://doi.org/https://doi.org/10.1016/j.foodres.2025.116646

Relvas, H., Lopes, D., & Armengol, J. M. (2025). Empowering communities: Advancements in air quality monitoring and citizen engagement. Urban Climate, 60, 102344. https://doi.org/https://doi.org/10.1016/j.uclim.2025.102344

Sato, D. T., Belo, O. M. O., Castro, A. P., Pacheco, V. M. G., Rodrigues, C. G., Coimbra, A. P., & Calixto, W. P. (2025). Hybrid machine learning model for disinfectant dosing in small-scale water treatment under data scarcity. Journal of Water Process Engineering, 78, 108736. https://doi.org/https://doi.org/10.1016/j.jwpe.2025.108736

Srivastava, R. P., Kumar, S., & Tiwari, A. (2024). Continuous emission monitoring systems (CEMS) in India: Performance evaluation, policy gaps and financial implications for effective air pollution control. Journal of Environmental Management, 359, 120584. https://doi.org/https://doi.org/10.1016/j.jenvman.2024.120584

Sun, T., Lu, C., Shi, Z., Zou, M., Bi, P., Xu, X., Xie, Q., Jiang, R., Liu, Y., Cheng, R., Xu, W., Wang, H., Zhang, Y., & Xu, P. (2025). PlantRing: A high-throughput wearable sensor system for decoding plant growth, water relations, and innovating irrigation. Plant Communications, 6(5), 101322. https://doi.org/https://doi.org/10.1016/j.xplc.2025.101322

Tesanovic, M., Bardel, T., Karl, R., & Berensmeier, S. (2025). Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separator. Current Research in Biotechnology, 10, 100324. https://doi.org/https://doi.org/10.1016/j.crbiot.2025.100324

Varadharajan, R., Lee, W., & Dong, Y. (2025). Cost-Effective voltage and current sensing technique for smart agricultural systems. Computers and Electronics in Agriculture, 239, 111045. https://doi.org/https://doi.org/10.1016/j.compag.2025.111045

Venkatesh, J., Partheeban, P., Baskaran, A., Krishnan, D., & Sridhar, M. (2024). Wireless sensor network technology and geospatial technology for groundwater quality monitoring. Journal of Industrial Information Integration, 38, 100569. https://doi.org/https://doi.org/10.1016/j.jii.2024.100569

Voitiuk, K., Seiler, S. T., de Melo, M. P., Geng, J., van der Molen, T., Hernandez, S., Schweiger, H. E., Sevetson, J. L., Parks, D. F., Robbins, A., Torres-Montoya, S., Ehrlich, D., Elliott, M. A. T., Sharf, T., Haussler, D., Mostajo-Radji, M. A., Salama, S. R., & Teodorescu, M. (2025). A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring. Internet of Things, 33, 101671. https://doi.org/https://doi.org/10.1016/j.iot.2025.101671

Zhang, W., Zhao, X., Guo, Y., Wang, Y., Li, L., Zhao, Y., Zhang, Y., & Zhang, W. (2025). Multifunctional dopamine-coated FeVO4 composites applied in supercapacitors and sensors for human motion, gas and light monitoring, and secure transmission of information. Chemical Engineering Journal, 519, 165107. https://doi.org/https://doi.org/10.1016/j.cej.2025.165107

Authors

Muhammad Firdaus Abduh
daud.ustj@gmail.com (Primary Contact)
Zain Nizam
Rashid Rahman
Abduh, M. F., Nizam, Z., & Rahman, R. (2026). IOT-BASED LOW-COST SENSOR INSTRUMENTATION FOR REAL-TIME WATER QUALITY MONITORING: DESIGN, CALIBRATION, AND FIELD VALIDATION. Scientechno: Journal of Science and Technology, 5(4), 369–387. https://doi.org/10.70177/scientechno.v5i4.4249

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