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Related Experiment Video

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data.

Ricardo Perez-Castillo1, Ana G Carretero2, Ismael Caballero3

  • 1Information Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. ricardo.pdelcastillo@uclm.es.

Sensors (Basel, Switzerland)
|September 19, 2018
PubMed
Summary

Managing data quality in the Internet of Things (IoT) is crucial for Smart, Connected Products (SCPs). This study introduces DAQUA-MASS, a methodology based on ISO 8000-61, to ensure data quality throughout the IoT data lifecycle.

Keywords:
ISO 8000-61Internet-of-ThingsIoTSCPsSmart, Connected Productsdata qualitydata quality in sensorsdata quality management processes

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Area of Science:

  • Computer Science
  • Data Science
  • Engineering

Background:

  • The Internet of Things (IoT) involves numerous Smart, Connected Products (SCPs), generating vast amounts of heterogeneous data.
  • Maintaining data quality throughout the data lifecycle is critical for IoT system functionality.
  • Existing data quality (DQ) solutions are fragmented, necessitating standardized approaches.

Purpose of the Study:

  • To propose a standardized methodology for data quality management in IoT sensor networks.
  • To address the technical and managerial challenges of data quality in IoT systems.
  • To leverage established standards for a robust data quality framework.

Main Methods:

  • Introduction of the DAQUA-MASS methodology for data quality management in sensor networks.
  • Application of the ISO 8000-61 standard for a process-oriented approach to data quality.
  • Utilizing the Plan-Do-Check-Act (PDCA) cycle for structured implementation.

Main Results:

  • DAQUA-MASS provides a structured, four-step process for managing data quality in IoT sensor networks.
  • The methodology aligns with the ISO 8000-61 standard, promoting best practices.
  • Implementation of DAQUA-MASS aims to overcome challenges associated with isolated data quality activities.

Conclusions:

  • A standardized, process-based approach is essential for effective data quality management in IoT.
  • The DAQUA-MASS methodology offers a practical framework for ensuring data quality in sensor networks.
  • Adherence to standards like ISO 8000-61 enhances the reliability and performance of IoT systems.