TSN Traffic Shaping for OPC UA Field Devices

Gogolev, Alexander; Braun, Roland; Bauer, Philipp

doi:10.1109/indin41052.2019.8972252

Cited by 20 publications

(6 citation statements)

References 4 publications

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“…Despite the research on different fields that comprise the IIoT, several authors mention that there are still issues that need to be improved in order to align with the IEEE1934-2018 Open Fog standards, such is the case of [ 86 ], which states that network connectivity for CPS through a standard Ethernet switch for NF (deterministic real-time networks), still has drawbacks in network synchronization, of which [ 87 ], mentions that mechanisms with the capacity of fault tolerance through TSN systems are currently being developed at the level of exhaustive simulations, so [ 88 ] makes a comparison between two modelers; TSN's own Time Aware Shaper (TAS) and Asynchronous Traffic Shaper (ATS) [ 89 ], on a network that emulates occasional randomly perturbed traffic characteristics suitable for ICS, other researchers delving even deeper such as [ 90 ], which mentions the importance in the unification and estimation provided by TSN and OPC-UA technologies, comparing the TAS and Credit-Based Shaping (CBS) modeling of TSN, when faced with a read request from the OPC-UA server, with high priority 7 window conditions and disturbances of up to 100 Mbps. The integration of the above scenarios is illustrated in Fig.…”

Section: Industrial Sector Implementations In Iot and Iiotmentioning

confidence: 99%

Analysis of architectures implemented for IIoT

Oñate¹,

Sanz²

2023

Heliyon

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Section: Industrial Sector Implementations In Iot and Iiotmentioning

confidence: 99%

Analysis of architectures implemented for IIoT

Oñate¹,

Sanz²

2023

Heliyon

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“…In practical applications, complex scenes using one algorithm is unable to cope with them, so a reasonable combination of multiple algorithms is chosen to not only better adapt to complex scenes but also to improve real-time transmission performance. In the literature [40] two shapers, credit-based shaper and time awareness shaper are explored and the question of how ready TSN and OPC UA are and what the requirements of the end devices and applications supporting these two new technologies for the implementation of TSN for industrial automation is explored.…”

Section: B Traffic Shaping In Tsnmentioning

confidence: 99%

A Survey of Traffic Shaping Technology in Internet of Things

Sun

et al. 2023

IEEE Access

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In the era of the Internet of Everything, the burst of traffic will bring many problems. Traffic shaping, as a common means to limit burst traffic rate, achieves ''peak and valley reduction'' to smooth the output rate, avoid network congestion, guarantee the quality of service (QoS) and improve the overall network transmission efficiency. This paper first explains the basic concept of traffic shaping, the related algorithms in traffic shaping, and compares the differences between traffic shaping and traffic policing. It then describes the research on traffic shaping in software-defined networks and the results from the combination of the two. This is followed by an introduction to relatively new technology for industry, timesensitive networks, and an exploration of the functions of various types of traffic shapers in time-sensitive networks. This is followed by a description of case studies of traffic shaping in IoT scenarios and an overview of these traffic shaping schemes, summarizing the usefulness of the shaping schemes. Finally, the future of traffic shaping is explored.

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“…Connecting those to an edge device or even to a complete machine network should therefore be easy. With OPC UA TSN data, transfer over Ethernet will even be possible in hard real time and thus provide a good solution for time critical applications [6]. The key is the application of "Time Sensitive Networks" (TSN) to warrant configurable time slots just for the transfer of real-time data like OPC UA data.…”

Section: Open Platform Communication Unified Architecture-opc Uamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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TSN Traffic Shaping for OPC UA Field Devices

Cited by 20 publications

References 4 publications

Analysis of architectures implemented for IIoT

Analysis of architectures implemented for IIoT

A Survey of Traffic Shaping Technology in Internet of Things

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

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