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Work item:
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Y.IoT-WSP-fr
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Subject/title:
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Framework for IoT-enabled diagnostics of water supply pipelines using on-device AI
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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2028-Q4 (Medium priority)
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Liaison:
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ISO/TC 224
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Supporting members:
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ETRI, Korea (Rep. of), Daejeon Univ., China Telecom
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Summary:
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Water supply pipeline networks are critical infrastructure for reliable drinking water services and sustainable urban development. As pipeline systems age worldwide, water utilities face growing challenges in maintaining infrastructure reliability, preventing leakage incidents, and supporting data-driven asset management.
Conventional pipeline diagnostics rely on manual inspection and traditional non-destructive testing methods. These approaches involve human-dependent interpretation, operational constraints such as water supply interruption, and fragmented diagnostic data management. As a result, diagnostic consistency is reduced and inspection data are difficult to integrate into digital infrastructure management systems.
Recent technological advances are transforming pipeline diagnostics. Emerging technologies such as AI-based video interpretation, ultrasonic corrosion scanning, and in-situ tensile measurement enable the acquisition of large volumes of digital inspection data.
In particular, on-device AI enables diagnostic data collected from field inspection devices to be processed directly at the edge, supporting automated defect detection, corrosion assessment, and material condition evaluation. This improves diagnostic objectivity, enhances operational safety, and generates structured diagnostic data for asset management and digital infrastructure platforms.
However, despite these advances, there is currently no standardized framework ensuring interoperability among these diagnostic data.
This draft Recommendation therefore defines a “Framework for IoT-enabled diagnostics of water supply pipelines using on-device AI”. The framework defines the functional requirements, framework and procedures required to support interoperable diagnostic workflows across field devices, edge AI systems, and enterprise platforms.
This draft Recommendation supports intelligent water infrastructure management and contributes to smart water management in smart city environments.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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First registration in the WP:
2026-05-27 13:49:33
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Last update:
2026-06-15 10:41:53
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