Smart Water Quality Monitoring Using Low-Cost IoT Sensors

  • Authors

    • Dr. Gabor Kezdi Research Associate Professor, University of Michigan, USA. Author
    • Dr. Jurgen Willman Professor of Radiology, Stanford University, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2018PIIX7TQ

    Published 12-04-2018

  • IoT, Water Quality Monitoring, Low-Cost Sensors, Smart Systems, Environmental Monitoring, Wireless Sensor Networks, Real-Time Data

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    G. Kezdi and J. Willman, “Smart Water Quality Monitoring Using Low-Cost IoT Sensors”, ijmiet, vol. 1, no. 2, pp. 01–12, Dec. 2018, doi: 10.67228/30715628/IJMIET-2018PIIX7TQ.

  • Abstract

    Rising industrialization and urbanization have significantly impacted water quality, making effective monitoring essential. Traditional monitoring systems are often expensive, time-consuming, and lack real-time capabilities, especially in developing regions. This paper proposes a low-cost, IoT-based water quality monitoring system that continuously tracks key parameters such as pH, turbidity, temperature, and dissolved oxygen. The system uses affordable sensors, microcontrollers, and wireless communication to enable real-time data collection and transmission. Its modular and scalable design allows deployment even in rural and resource-limited areas. Data collected from multiple sensing nodes is sent to a cloud platform for storage and analysis. Continuous monitoring helps in early detection of contamination, while data analytics supports anomaly detection and improved decision-making. The study reviews existing systems and highlights their limitations, including high cost and lack of scalability. Experimental results show that the proposed system provides reasonably accurate measurements compared to standard laboratory equipment. Overall, the system offers a cost-effective and efficient solution for real-time water quality monitoring, with future scope for integrating machine learning and large-scale smart city applications.

  • References

    [1] Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative Adversarial Networks. Advances in Neural Information Processing Systems (NeurIPS).

    [2] Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).

    [3] Boden, M. A. (2016). AI: Its Nature and Future. Oxford University Press.

    [4] Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative Adversarial Networks. Proceedings of ICCC.

    [5] Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260.

    [6] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436–444.

    [7] Kamar, E. (2016). Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. IJCAI.

    [8] Dellermann, D., et al. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637–643.

    [9] Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint.

    [10] Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104(3), 671–732.

    [11] Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors, 39(2), 230–253.

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