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

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.