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

Published

October 1, 2020

Author(s)

Mohamed Hany, Yongkang Liu, Karl Montgomery, Rick Candell

Abstract

The lack of widespread adoption of wireless technologies in cyber-physical systems (CPS) is partly due to not fully understanding the detailed impact of wireless deployment on the physical processes. In this paper, we introduce an approach to integrate wireless network traffic data and physical processes data to evaluate the impact of wireless communications on the performance of a manufacturing work-cell. A testbed that emulates a robotic manufacturing factory work-cell is constructed using two collaborative grade robot arms, machine emulators, and wireless communication devices. All network traffic data is collected and physical process data, including the robots and machines states and various supervisory control commands, is also collected and synchronized to the network data. The data is then integrated where redundant data is removed and correlated actions are connected in a graph database. A data model is proposed, developed, and elaborated; the database is then populated with events from the testbed, and the resulting graph is presented. Moreover, we detail the way by which this approach is used to study the impact of wireless communications on the physical processes and illustrate the impact of various wireless network parameters on the performance of the emulated manufacturing work-cell. This approach can be deployed as a building block of various descriptive and predictive wireless analysis tools for CPS.

Citation

ASME Journal of Computing and Information Science in Engineering

Pub Type

Journals

Download Paper

https://doi.org/10.1115/1.4048205

Local Download

Keywords

industrial wireless, factory automation, testbed, measurement, instrumentation, graph database

Citation

Hany, M. , Liu, Y. , Montgomery, K. and Candell, R. (2020), Wireless Cyber-Physical Systems Performance Evaluation through a Graph Database Approach, ASME Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4048205, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929563 (Accessed October 7, 2024)