Authors : Hervé Kinkete Mfumabi; Gracia Grace Disanka; Daniel Kibala Mfumangimbi; Evodie Ikene Mushid; Kenayah Ebonge Maya; Kéren Mayele Loba; Jean-Beni Lokuli Isekongo; Sephora Deko Kila; Ferdinand Sangunza

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/ymsyed6e

DOI : https://doi.org/10.38124/ijisrt/26aug874

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Intermittent water supply creates nonstationary pressure, uncertain availability, leakage risk, and operational blind spots, while cloud-only monitoring is fragile where connectivity and power are unreliable. This article presents MAYIEDGE as an architecture-led reference study with a reproducible algorithmic demonstration, an executable offline transaction contract, and a prespecified confirmatory protocol. The proposed offline-first cyber-physical architecture couples flow, pressure, and tank-level sensing with ESP32-S3-class edge processing, explicitly frozen quality control and inference, transactional local buffering, MQTT-TLS communication, server-side services, and a versioned EPANET/WNTR hydraulic-twin design. A deterministic synthetic fixture demonstrates the frozen detector logic under a declared surrogatereference substitution, alert persistence within the prespecified 60-s window, backlog behavior, checksum-confirmed acknowledgement, and server de-duplication. It does not claim that an EPANET twin, complete physical prototype, MQTT deployment, or field system has already been executed. The defensible novelty is the joint treatment of intermittent supply, offline-first edge inference, transactional store-and-forward, a versioned hydraulic-twin design, resource-constrained deployment, and confirmatory evaluation prespecified before testing. Four statistical hypotheses (H1–H4) and a separate engineering validation gate (H5) define the future campaign. Parameters, temporal splits, baseline selection, ablations, calibration, missingness, network stress, energy profiling, human evaluation, multiplicity, and fixed sample sizes are specified in advance.

Keywords : Digital Twin; Edge AI; EPANET; Intermittent Water Supply; Internet of Things; Leak Detection; Offline-First Systems; Reproducible Protocol.

References :

1. A. Pagano, D. Garlisi, I. Tinnirello, et al., “A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deployment,” Ad Hoc Networks, vol. 168, 103714, 2025. doi:10.1016/j.adhoc.2024.103714.
2. R. J. L. Taloma, F. Cuomo, D. Comminiello, and P. Pisani, “Machine learning for smart water distribution systems: exploring applications, challenges and future perspectives,” Artificial Intelligence Review, vol. 58, art. 120, 2025. doi:10.1007/s10462-024-11093-7.
3. P. Conejos Fuertes, F. Martínez Alzamora, M. Hervás Carot, and J. C. Alonso Campos, “Building and exploiting a Digital Twin for the management of drinking water distribution networks,” Urban Water Journal, vol. 17, no. 8, pp. 704–713, 2020. doi:10.1080/1573062X.2020.1771382.
4. J. E. Pesantez, V. Alghamdi, S. Sabu, A. Mahinthakumar, and E. Berglund, “Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic,” Sustainable Cities and Society, vol. 77, 103520, 2022. doi:10.1016/j.scs.2021.103520.
5. P. Ghorbani Bam et al., “Digital Twin Applications in the Water Sector: A Review,” Water, vol. 17, no. 20, 2957, 2025. doi:10.3390/w17202957.
6. E. Q. Shahra et al., “Intelligent Edge–Cloud Framework for Water Quality Monitoring,” Water, vol. 16, no. 2, 196, 2024. doi:10.3390/w16020196.
7. S. El-Zahab and T. Zayed, “Leak detection in water distribution networks: an introductory overview,” Smart Water, vol. 4, art. 5, 2019. doi:10.1186/s40713-019-0017-x.
8. L. Romero-Ben, D. Alves, J. Blesa, M. G. Cembrano, V. Puig, and E. Duviella, “Leak detection and localization in water distribution networks: Review and perspective,” Annual Reviews in Control, vol. 55, pp. 392–419, 2023. doi:10.1016/j.arcontrol.2023.03.012.
9. A. Pagano, D. Garlisi, F. Giuliano, T. Cattai, R. J. L. Taloma, and F. Cuomo, “Introducing and evaluating SWI-FEED: A smart water IoT framework designed for large-scale contexts,” Computer Communications, vol. 237, 108146, 2025. doi:10.1016/j.comcom.2025.108146.
10. L. A. Rossman et al., EPANET 2.2 User Manual. Cincinnati, OH: U.S. Environmental Protection Agency, EPA/600/R-20/133, 2020.
11. U.S. Environmental Protection Agency, “EPANET: Application for Modeling Drinking Water Distribution Systems,” 2025. https://www.epa.gov/water-research/epanet (accessed 17 Aug. 2026).
12. K. Klise, M. Bynum, D. Moriarty, and R. Murray, “A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study,” Environmental Modelling & Software, vol. 95, pp. 420–431, 2017. doi:10.1016/j.envsoft.2017.06.022.
13. J. Brooke, “SUS: A ‘quick and dirty’ usability scale,” in Usability Evaluation in Industry, P. W. Jordan et al., Eds. London: Taylor & Francis, 1996, pp. 189–194.
14. OASIS, MQTT Version 5.0, OASIS Standard, 2019. https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html.
15. A. M. Lieb, C. H. Rycroft, and J. Wilkening, “Optimizing intermittent water supply in urban pipe distribution networks,” SIAM Journal on Applied Mathematics, vol. 76, no. 4, pp. 1492–1514, 2016. doi:10.1137/15M1038979.
16. ISO/IEC, ISO/IEC 27001:2022, Information security management systems—Requirements, Geneva, 2022.
17. M. Zuñiga-Uribe et al., “Artificial Intelligence in Water Distribution Networks: A Systematic Review of Models, Input Variables, Databases, and Output Strategies for Leak Detection,” Smart Cities, vol. 9, art. 45, 2026. doi:10.3390/smartcities9030045.
18. N. T. Mücke, P. Pandey, S. Jain, S. M. Bohté, and C. W. Oosterlee, “A probabilistic digital twin for leak localization in water distribution networks using generative deep learning,” Sensors, vol. 23, no. 13, 6179, 2023. doi:10.3390/s23136179.
19. V. Travaš, E. Gal, I. Lučin, and E. Žic, “Digital twin for a real-time leakage detection and localization in pressurized piping systems,” Journal of Hydroinformatics, vol. 27, no. 4, pp. 755–770, 2025. doi:10.2166/hydro.2025.304.
20. T. A. Syed et al., “Smart Water Management with Digital Twins and Multimodal Transformers: A Predictive Approach to Usage and Leakage Detection,” Water, vol. 16, no. 23, 3410, 2024. doi:10.3390/w16233410.
21. P. Locatelli, T. Cattai, P. Spadaccino, M. Casalbore, and F. Cuomo, “TwinAI: A digital twin and graph reinforcement learning framework for real-time management of water distribution networks,” Internet of Things, vol. 37, 101911, 2026. doi:10.1016/j.iot.2026.101911.
22. E. Steins and A. Cominola, “Addressing Practical Challenges of Stochastic Process Control for Leakage Detection in Water Distribution Networks: A Comparative Analysis,” Journal of Water Resources Planning and Management, 2025. doi:10.1061/JWRMD5.WRENG-6969.
23. H. Wang, “AI for detecting and localizing concurrent abrupt and incipient leaks in water distribution networks,” Water Research, vol. 295, 125538, 2026. doi:10.1016/j.watres.2026.125538.
24. K. V. Shah and H. M. Patel, “Source Contamination Detection and Identification of Affected Spots in the Intermittent Water Supply System Using Mixed Tool-Based and Data-Driven Machine Learning Techniques,” Journal of Water Resources Planning and Management, vol. 152, no. 3, 2026. doi:10.1061/JWRMD5.WRENG-7171.