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Wireless Cyber-Physical Systems Performance Evaluation through a Graph Database Approach.

Mohamed Kashef1, Yongkang Liu1, Karl Montgomery1

  • 1National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899.

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This study introduces a method to integrate wireless network and physical process data for smart manufacturing. It evaluates wireless communication impacts on cyber-physical systems, aiding adoption in low-latency, high-reliability scenarios.

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Area of Science:

  • Industrial Automation
  • Wireless Communication Systems
  • Cyber-Physical Systems

Background:

  • Smart manufacturing adoption of wireless technologies is hindered by a lack of understanding of their impact on physical processes, especially for low-latency, high-reliability applications.
  • Existing approaches often fail to comprehensively link wireless network performance with the operational dynamics of manufacturing cyber-physical systems (CPS).

Purpose of the Study:

  • To develop and validate an approach for integrating wireless network traffic data with physical process data in manufacturing.
  • To evaluate the impact of wireless communication deployment on the performance of a manufacturing factory work-cell.
  • To provide a framework for analyzing wireless performance in CPS for improved adoption.

Main Methods:

  • Construction of a testbed emulating a robotic manufacturing work-cell with collaborative robots and machine emulators.
  • Collection and synchronization of wireless network traffic data and physical process data (robot/machine states, control commands).
  • Integration of data using a graph database with a proposed data model to remove redundancy and connect correlated activities.

Main Results:

  • A synchronized dataset correlating network and physical process events was created and stored in a graph database.
  • Query commands were developed to analyze network performance and inter-component relationships.
  • The approach demonstrated the ability to study the impact of wireless parameters on the emulated manufacturing work-cell performance.

Conclusions:

  • The proposed data integration approach provides a robust method for evaluating wireless communication impacts in manufacturing CPS.
  • This framework facilitates a deeper understanding of wireless performance, supporting the wider adoption of wireless technologies in smart manufacturing.
  • The approach serves as a foundational block for developing advanced descriptive and predictive wireless analysis tools for CPS.