Implementation of ESP8266 and Turbidity Sensor in Water Turbidity Monitoring Model Using Fuzzy Tsukamoto
Abstract
Drinking water quality is critical to public health. The 2020 Household Drinking Water Quality Study (SKAMRT) by the Indonesian Ministry of Health revealed that 70% of households consume water contaminated with bacteria, including Escherichia coli (E-coli). Although 93% of Indonesia's population has access to adequate drinking water, only 11.9% meets safety standards. Regular quality testing, especially for turbidity, is essential with increasing water consumption. However, effective real-time monitoring remains a challenge. Advances in the Internet of Things (IoT) offer an efficient approach to water quality monitoring. This study develops an IoT-based system using the Fuzzy Tsukamoto method to monitor drinking water quality. The system integrates a turbidity sensor, NodeMCU ESP8266 microcontroller, and Firebase for data storage. Turbidity values in Nephelometric Turbidity Unit (NTU) are processed by the Fuzzy Tsukamoto method to assess quality. The research results show that bottled drinking water with a turbidity level of 0.83 NTU meets the standards set by the Indonesian Minister of Health Regulation 492/Menkes/Per/IV/2010 and SNI 01-3553-2006, which means it is safe based on the turbidity level.
References
J. Irianto et al., “Studi Kualitas Air Minum Rumah Tangga di Indonesia,” Puslitbang Upaya Kesehatan Masyarakat, Jakarta, Research Result, 2020. [Online]. Available: https://repository.badankebijakan.kemkes.go.id/4936/1/Laporan%20SKAM-RT%20Balitbangkes.pdf
W. H. Organization, Guidelines for drinking-water quality: fourth edition incorporating the first and second addenda, 4th ed. World Health Organization, 2022. [Online]. Available: https://books.google.co.id/books?id=x3RyEAAAQBAJ
F. Jan, N. Min-Allah, and D. Düştegör, “IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications,” Water, vol. 13, no. 13, 2021, doi: 10.3390/w13131729.
S. Bijekar et al., “The State of the Art and Emerging Trends in the Wastewater Treatment in Developing Nations,” Water, vol. 14, no. 16, 2022,
doi: 10.3390/w14162537.
L. Lin, H. Yang, and X. Xu, “Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review,” Frontiers in Environmental Science, vol. 10, 2022, doi: 10.3389/fenvs.2022.880246.
M. Fida, P. Li, Y. Wang, S. M. K. Alam, and A. Nsabimana, “Water Contamination and Human Health Risks in Pakistan: A Review,” Exposure and Health, vol. 15, no. 3, pp. 619–639, Sep. 2023, doi: 10.1007/s12403-022-00512-1.
R. Bogdan, C. Paliuc, M. Crisan-Vida, S. Nimara, and D. Barmayoun, “Low-Cost Internet-of-Things Water-Quality Monitoring System for Rural Areas,” Sensors, vol. 23, no. 8, 2023, doi: 10.3390/s23083919.
P. S. Munoz, N. Tran, B. Craig, B. Dezfouli, and Y. Liu, “Analyzing the Resource Utilization of AES Encryption on IoT Devices,” in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA: IEEE, Nov. 2018, pp. 1200–1207. doi: 10.23919/APSIPA.2018.8659779.
W.-J. Li, C. Yen, Y.-S. Lin, S.-C. Tung, and S. Huang, “JustIoT Internet of Things based on the Firebase real-time database,” in 2018 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE), IEEE, 2018, pp. 43–47.
K. Dwi Hartomo, A. Firman Daru, and H. Dwi Purnomo, “A new approach of scalable traffic capture model with Pi cluster,” IJECE, vol. Vol 13, No 2: April 2023, no. 2, 2023, doi: 10.11591/ijece.v13i2.pp2186-2196.
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,” IJECE, vol. 13, no. 5, pp. 5782–5791, 2023, doi: http://doi.org/10.11591/ijece.v13i5.pp5782-5791.
M. M. Rahman and Others, “Laser-Based Turbidity Sensor Development,” Environmental Monitoring and Assessment, vol. 193, no. 12, pp. 1–12, 2021, doi: 10.1007/s10661-021-09723-4.
J. Trevathan, W. Read, and A. Sattar, “Implementation and calibration of an IoT light attenuation turbidity sensor,” Internet of Things, vol. 19, p. 100576, 2022, doi: 10.1016/j.iot.2022.100576.
P. Mahajan and P. Shahane, “An IoT Based System for Water Quality Monitoring,” SSRN Electronic Journal, no. Icicnis, 2021, doi: 10.2139/ssrn.3769765.
A. R. Zain, M. Agustin, P. Oktivasari, N. F. Soelaiman, and M. F. Fahroji, “Design of Monitoring System for Water Levels and Turbidity Water Canals Based on Nodemcu,” in 2022 16th International Conference on Telecommunication Systems, Services, and Applications (TSSA), Oct. 2022, pp. 1–4. doi: 10.1109/TSSA56819.2022.10063905.
J. Trevathan, W. Read, and A. Sattar, “Implementation and Calibration of an
IoT Light Attenuation Turbidity Sensor,” Internet of Things, vol. 19, p. 100576, 2022, doi: https://doi.org/10.1016/j.iot.2022.100576.
H. Rachmat, I. Putra, and A. Wicaksono, “Monitoring System for Water Turbidity Using IoT Technology,” Indonesian Journal of Electrical Engineering and Informatics, vol. 9, no. 3, pp. 402–410, 2021.
L. A. Zadeh, “Fuzzy logic,” in Granular, Fuzzy, and Soft Computing, Springer, 2023, pp. 19–49.
G. D. Astudillo, L. E. Garza-Castañon, and L. I. Minchala Avila, “Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment,” IEEE Access, vol. 8, pp. 51129–51144, 2020, doi: 10.1109/ACCESS.2020.2980736.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a copyright form (JACTA) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
