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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68415
Volume:
10 |
10 |
Oct 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and Communication Engineering
K RAMAKRISHNAN COLLEGE OF TECHNOLOGY
Electronics and Communication Engineering
K RAMAKRISHNAN COLLEGE OF TECHNOLOGY
Electronics and Communication Engineering
K RAMAKRISHNAN COLLEGE OF TECHNOLOGY
Electronics and Communication Engineering
K RAMAKRISHNAN COLLEGE OF TECHNOLOGY
Water leakage in underground and distribution pipelines can lead to significant water loss, property damage, and increased maintenance costs. Early identification of leakage is therefore important for utilities, industries, and other organizations responsible for water distribution. This paper presents the concept of a Smart Water Leakage Detection & Analytics Platform that combines Internet of Things (IoT) sensors, a field gateway, cloud analytics, and a monitoring interface. Flow, pressure, and acoustic sensors collect complementary information about pipeline behaviour. An IoT gateway pre-processes the measurements and transmits them through communication technologies such as LoRaWAN, NB-IoT, or Wi-Fi to a cloud analytics engine. The engine compares incoming readings with expected operating patterns and identifies deviations that may indicate a leak or abnormal pressure. Alerts can then be presented to operators through a web dashboard and mobile application. Where suitable hardware and confirmation logic are available, a smart shut-off valve can be used to isolate the affected pipeline section. The platform is intended to support continuous monitoring, faster investigation, historical reporting, and more efficient water management. The supplied project description does not provide measured field-test results; therefore, detection accuracy and actual water savings must be validated through implementation and experimental evaluation.
KEYWORDS
The Smart Water Leakage Detection & Analytics Platform proposes an IoT-based method for monitoring pipeline conditions using flow, pressure, and acoustic sensors. Sensor data is collected through a gateway and transmitted to a cloud analytics engine, where unusual patterns can be flagged for operator review. A web dashboard and mobile application provide a means of viewing current conditions, alerts, and historical readings. Optional smart shut-off valve control can provide a response mechanism when a leak is confirmed and the necessary hardware is available. The modular architecture is relevant to municipal distribution, smart-city infrastructure, industrial pipelines, and agricultural water systems. It offers a practical basis for further development, but its operational value must be established through systematic testing. Future work should focus on sensor calibration, anomaly-detection validation, communication reliability, safe valve operation, and measurement of actual water and cost outcomes.
[1] X. Wan, P. Khorsandi Kuhanestani, R. Farmani, and E. Keedwell, “Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors,” Journal of Water Resources Planning and Management, vol. 148, no. 10, 2022. [2] A. W. Z. Chew, Z. Y. Wu, R. Kalfarisi, X. M. Meng, and J. Pok, “Generalized Acoustic Data Analysis Framework for Leakage Detection and Localization in Field Operational Water Distribution Networks,” Journal of Water Resources Planning and Management, vol. 149, no. 11, 2023. [3] H. Fan, S. Tariq, and T. Zayed, “Acoustic Leak Detection Approaches for Water Pipelines,” Automation in Construction, 2022. [4]“An IoT Based Pipeline Management and Leakage Detection System,” in Proceedings of the 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 2024. [5] “IoT and GIS Based Real Time Water Pipeline Surveillance and Leak Detection System,” in Proceedings of the 2024 International Conference on Smart Electronics and Communication Systems (ISENSE), 2024. [6] “Review of Model-Based and Data-Driven Approaches for Leak Detection and Location in Water Distribution Systems,” Water Supply, vol. 21, no. 7, pp. 3282–3306, 2021. [7] “Leak Detection and Localization in Water Distribution Networks Using Conditional Deep Convolutional Generative Adversarial Networks,” Water Research, vol. 238, article 120012, 2023. [8] “Artificial Intelligence in Water Distribution Networks: A Systematic Review of Models, Input Variables, Databases, and Output Strategies for Leak Detection,” Smart Cities, vol. 9, no. 3, article 45, 2026. [9] “Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview,” IEEE Access, vol. 5, pp. 20272– 20291, 2017. [10] “A Pressure-Based Method for Monitoring Leaks in a Pipe Distribution System: A Review,” Renewable and Sustainable Energy Reviews, vol. 69, pp. 902–911, 2017.
Publication Date
10/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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