High-Resolution Tap-Based IoT System for Flow Data Collection and Water End-Use Analysis

Man Ho Luk, Cheuk Wang Yau, Philip W.T. Pong, Angela P.Y. Lee, Edith C.H. Ngai, King Shan Lui

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Knowledge on water-use patterns in residential settings can help policymakers formulate well-targeted water conservation measures and evaluate the efficacy of such measures. Prior studies have applied machine learning techniques to disaggregate household-level water consumption data, collected by smart meters, into specific end-use categories, such as showering, basin use, kitchen use, and use by washing machine. However, the analysis of the effect of the sampling interval on the classification accuracy level remains an underinvestigated issue. This article seeks to fill this knowledge gap by identifying an optimal sampling interval that can achieve a high level of classification accuracy while overcoming constraints of on-device data storage, data transmission, and energy consumption. To understand the benefits of collecting fine-grained data for machine learning, we have built a high-resolution tap-based Internet of Things (IoT) system comprising a set of Wi-Fi-based tap sensors, gateway infrastructure, and a secure data processing pipeline. Based on empirical tap-based data collected over an eight-month period, we concluded that when the sampling interval decreases slightly from 5 to 1 s, the accuracy level of the end-use classification model increases significantly from 66.6% to 76.1 %. This article also highlights the challenges of deploying IoT sensors to collect water consumption data in a domestic setting. In order to collect sufficient ground-truth data for the training and verification of a generalizable water end-use disaggregation model, it is necessary to sophisticate the flow data collection system by adopting low-power wide-area network technologies and reducing the level of energy consumption of the flow sensing components.

Original languageEnglish (US)
Pages (from-to)22822-22835
Number of pages14
JournalIEEE Internet of Things Journal
Issue number22
StatePublished - Nov 15 2022

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Embedded systems
  • Internet of Things (IoT)
  • flowmeters
  • machine learning
  • sensor systems
  • smart meters


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