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A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation
Published
Author(s)
Richard Candell, Mohamed T. Hany, Yongkang Liu, Karl R. Montgomery, Sebti Foufou
Abstract
The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A new testbed was recently designed and developed to facilitate such studies in work-cells by replicating various data flows in an emulated production environment. In this paper, an approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed was constructed using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database approach was implemented to capture network flight data and operational event data among the actors within the testbed. A proposed schema is developed and elaborated, the database is then populated with events from the tested, the resulting graph is presented. Query commands are then presented as a means to examine and analyze network performance and relationships within the actors of the network. Additionally, we demonstrate how to extract correlations between receive signal power and network delay within the testbed using the graph database query language. Finally, using the inherent interconnected nature of the graph database, we discuss applying the graph database approach toward examining more complex relationships between the wireless communications network and the operational system.
Proceedings Title
Proceedings of the 2020 IEEE International Conference on Industrial Technology
Conference Dates
February 26-28, 2020
Conference Location
Buenos Aires
Conference Title
2020 IEEE International Conference on Industrial Technology
Candell, R.
, Hany, M.
, Liu, Y.
, Montgomery, K.
and Foufou, S.
(2020),
A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation, Proceedings of the 2020 IEEE International Conference on Industrial Technology, Buenos Aires, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928722
(Accessed October 13, 2025)