Implementation of Fuzzy Tsukamoto on Node MCU ESP8266 to optimize monitoring of water flow in pipes

Authors

  • April Firman Daru Faculty of Information and Communication Technology, Universitas Semarang, Tlogosari Kulon, 50196 Kota Semarang, Jawa Tengah, Indonesia
  • Alauddin Maulana Hirzan Faculty of Information and Communication Technology, Universitas Semarang, Tlogosari Kulon, 50196 Kota Semarang, Jawa Tengah, Indonesia
  • Muhammad Alfian Badrud Duja Faculty of Information and Communication Technology, Universitas Semarang, Tlogosari Kulon, 50196 Kota Semarang, Jawa Tengah, Indonesia
  • Paminto Agung Christianto Departmentof Informatics STMIK Widya Pratama, Patriot No.25, Pekalongan, Jawa Tengah,Indonesia

DOI:

https://doi.org/10.31436/ijpcc.v11i2.570

Keywords:

Internet of Things, NodeMCU ESP8266, Tsukamoto Fuzzy Logic, Artesian Monitoring

Abstract

The issue of excessive and uncontrolled artesian water consumption has become a critical concern that requires immediate attention. The lack of real-time monitoring mechanisms often leads to significant water wastage, particularly in urban areas where demand is high. Recent data on artesian water usage indicate a 20% increase in consumption in 2023 compared to the previous year, with approximately 25% of the total distributed water being wasted. To address this issue, an Internet of Things (IoT)-based monitoring system for well water consumption has been developed, integrating the Tsukamoto fuzzy logic method. This system employs a NodeMCU ESP8266 microcontroller to collect water consumption data from a flow sensor. The data is then processed using the Tsukamoto fuzzy logic method to classify water usage into three categories: efficient, normal, and excessive. The categorized water usage information is displayed on a 16x2 LCD and a mobile application, enabling users to monitor their water consumption patterns in real-time.By providing continuous monitoring and intelligent classification of water usage, this system aims to enhance user awareness of sustainable water consumption practices. The results of this study demonstrate that the application of fuzzy logic in the monitoring model enables accurate and adaptive water consumption predictions, which can be adjusted based on environmental conditions. The fuzzy logic values obtained in this study are low flow (0.0), normal flow (0.5), and high flow (0.0). This system is expected to contribute to reducing the risk of excessive water consumption, promoting more efficient resource management, and fostering a culture of conservation among users.

References

C. Tortajada, “Water management for a changing world,” Water Resources Management, vol. 24, no. 1, pp. 1–11, 2010.

M. Rajarajan and G. Dhanda, “Internet of Things for smart water management: A review,” Journal of Water Resources Planning and Management, vol. 145, no. 5, p. 03119001, 2019.

Y. Li and et al, “A smart water metering system based on wireless sensor networks,” IEEE Sensors Journal, vol. 16, no. 10, pp. 3521–3528, 2016.

Q. Zhang and et al, “An IoT-enabled water management system for smart cities,” IEEE Access, vol. 6, pp. 16903–16911, 2018.

A. F. Daru, K. D. Hartomo, and H. D. Purnomo, “IPv6 flood attack detection based on epsilon greedy optimized Q learning in single board computer,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 5, pp. 5782–5791, 2023.

Ö. Kisi and I. Yuksel, “Fuzzy logic modeling for short-term water demand forecasting,” Journal of Hydrology, vol. 562, pp. 672–681, 2018.

J. F. Adamowski and A. Karpatne, “Time series forecasting of water demand using wavelet transform and fuzzy logic,” Journal of Hydrology, vol. 420–421, pp. 1–12, 2012.

M. Moravej and et al, “A fuzzy-based decision support system for irrigation water management,” Agricultural Water Management, vol. 187, pp. 1–12, 2017.

M. Zarghami and K. Madani, “A multi-criteria decision making approach for water resource management,” Water Resources Management, vol. 30, no. 10, pp. 3481–3497, 2016.

W. Wu and et al, “A fuzzy logic-based system for real-time monitoring and control of water distribution networks,” Journal of Hydrology, vol. 529, pp. 698–708, 2015.

A. Stuart and et al, “Smart water management in urban areas: A hybrid approach combining IoT, fuzzy logic, and machine learning,” Journal of Environmental Management, vol. 246, pp. 683–694, 2019.

F. Fakhrurroja and others, “Real-time Water Quality Monitoring Using IoT and Fuzzy Logic,” IEEE Internet of Things Journal, vol. 10, no. 3, pp. 1201–1210, 2023.

M. Hirzan and others, “An IoT-Based Drinking Water Monitoring System Using Fuzzy Tsukamoto,” Journal of Advanced Computational Techniques in Automation, vol. 12, no. 4, pp. 223–235, 2024.

A. F. Daru, S. Susanto, and W. Adhiwibowo, “Arowana cultivation water quality monitoring and prediction using autoregressive integrated moving average,” International Journal of Reconfigurable and Embedded Systems, vol. 13, no. 3, p. 665, 2024.

R. Firdaus and others, “Smart Farming Control System Using Tsukamoto Fuzzy Logic,” IEEE Transactions on Smart Agriculture, vol. 8, no. 2, pp. 98–107, 2023.

A. Daru, A. Hirzan, F. Saputra, and P. Christianto, “Implementation of ESP8266 and Turbidity Sensor in Water Turbidity Monitoring Model Using Fuzzy Tsukamoto,” Journal of Advanced Computing Technology and Application (JACTA), vol. 6, no. 2, pp. 1–13, Nov. 2024, doi: 10.54554/jacta.2024.06.02.001.

A. Sufyan, “Water Feasibility Monitoring Using Arduino and Fuzzy Logic,” International Journal of Embedded Systems, vol. 15, no. 1, pp. 45–58, 2020.

L. Zhang and others, “Energy-Efficient Water Distribution Control Based on Fuzzy Logic,” IEEE Transactions on Sustainable Computing, vol. 7, no. 3, pp. 677–690, 2022.

Y. Kim and others, “Fuzzy Logic-Based Optimization in Water Purification Systems,” Journal of Environmental Engineering, vol. 149, no. 6, pp. 1298–1310, 2021.

P. Sharma, “Smart City Water Management Using Fuzzy Decision-Making,” IEEE Access, vol. 11, pp. 21089–21102, 2023.

K. Nakamura and others, “Industrial Water Monitoring and Chemical Dosing Control Using Fuzzy Logic,” International Journal of Automation and Control, vol. 18, no. 5, pp. 312–326, 2022.

J. Fernandez, “Leakage Detection in Water Pipelines Using Fuzzy Logic,” IEEE Sensors Journal, vol. 20, no. 8, pp. 3492–3505, 2021.

B. Patel, “Hybrid Fuzzy-PID Water Pressure Regulation System,” Journal of Control Engineering and Applied Informatics, vol. 24, no. 2, pp. 88–102, 2023.

L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.

T. J. Ross, Fuzzy Logic with Engineering Applications, 3rd ed. John Wiley & Sons, 2010.

J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall, 2001.

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Published

30-07-2025

How to Cite

Daru, A. F., Hirzan, A. M., Duja, M. A. B. ., & Christianto, P. A. (2025). Implementation of Fuzzy Tsukamoto on Node MCU ESP8266 to optimize monitoring of water flow in pipes . International Journal on Perceptive and Cognitive Computing, 11(2), 69–76. https://doi.org/10.31436/ijpcc.v11i2.570

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