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Model-based Cosimulation for Industrial Wireless Networks

Published

Author(s)

Ging Jeng, Honglei Li, Yanzhou Liu, Yongkang Liu, Mohamed Hany, Rick Candell, Shuvra Bhattacharyya

Abstract

Wireless communications technology has the potential to provide major benefits in lowering the cost and increasing the efficiency of factory automation (FA) systems. However, design of FA systems that employ wireless networks involves stringent constraints on real-time performance and reliability, and requires the assessment of and experimentation with complex interactions among process control, factory topology construction (layout and connectivity of subsystems, such as machines, rails, etc.), and wireless communication. In this paper, we introduce a novel simulation framework to support such assessment and experimentation in the design of next- generation FA systems. Our simulation framework employs model-based design principles to enhance design reliability, and enable systematic and efficient integration of control, topology, and network modeling aspects. We demonstrate the utility of our framework through a case study that involves topology design and scalability analysis for a large class of FA systems. Our results demonstrate the ability of the proposed framework to provide insights on complex design trade-offs, while the underlying model-based features enhance efficient and reliable system-level integration.
Proceedings Title
Proceedings of the 14th IEEE International Workshop on Factory Communication Systems
Conference Dates
June 12-15, 2018
Conference Location
Imperia, IT
Conference Title
14th IEEE International Workshop on Factory Communication Systems

Keywords

wireless, automation, factory communication

Citation

Jeng, G. , Li, H. , Liu, Y. , Liu, Y. , Hany, M. , Candell, R. and Bhattacharyya, S. (2018), Model-based Cosimulation for Industrial Wireless Networks, Proceedings of the 14th IEEE International Workshop on Factory Communication Systems, Imperia, IT, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925541 (Accessed March 5, 2024)
Created June 14, 2018, Updated October 12, 2021