Investigating the Applicability of In-Network Computing to Industrial Scenarios

Kunze, Ike; Glebke, René; Scheiper, Jan; Bodenbenner, Matthias; Schmitt, Robert; Wehrle, Klaus

doi:10.1109/icps49255.2021.9468247

Cited by 23 publications

(6 citation statements)

References 18 publications

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“…Additionally, we can source from a plethora of realistic real-world use cases, which allow us to approach research challenges in a goal-oriented manner. Our work on coordinate transformations, e.g., demonstrates that novel networking paradigms can be effective solutions for the specific challenges of metrology systems (Kunze et al 2021a). Moreover, we illustrated how to utilize generative software development to reduce the integration effort of measuring systems by generating interoperable communication interfaces based on a unified data model (Bodenbenner et al 2020).…”

Section: Discussionmentioning

confidence: 94%

“…These systems use different formats to capture locations, e.g., using different coordinate systems, and metrology information thus needs to be transformed into a common scheme. In one of our works, we investigate how well the required coordinate transformations can be deployed on programmable networking hardware (Kunze et al 2021a). While we find that there are indeed challenges requiring heavy workarounds, we also see that INC can achieve low latencies and high accuracy, as well as significantly higher packet processing rates than end-hostbased applications.…”

Section: Data Stream Management and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Pennekamp,

Belova,

Bergs

et al. 2023

Interdisciplinary Excellence Accelerator Series

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The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL.

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Section: Discussionmentioning

confidence: 94%

Section: Data Stream Management and Analysismentioning

confidence: 99%

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Pennekamp,

Belova,

Bergs

et al. 2023

Interdisciplinary Excellence Accelerator Series

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, complementary safety measures, such as emergency stops, can also be realized in networking hardware, as is demonstrated by Cesen et al [31]. The accuracy and computing speed of such approaches is generally capable of reaching levels similar to userspace applications, while networking devices are especially capable of processing significantly higher packet rates [86]. Yet, implementing the required functionality on the current generation of programmable networking hardware is still challenging [86].…”

Section: Interconnected and Industry-capable Infrastructurementioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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“…Consequently, to enable automation and autonomous decision-making, we have to deal with associated big-data challenges [28,29] that are imminent due to virtually infinite volumes of available sensor data and the increased need for high-frequency sensing [1,2]. In that regard, efficiently utilizing existing computing and network infrastructure could be a key driver [30,31]. However, big data also provides opportunities when properly extracting its encapsulated knowledge [1].…”

Section: Motivation and Potentialsmentioning

confidence: 99%

The Road to Accountable and Dependable Manufacturing

et al. 2021

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The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust.

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Investigating the Applicability of In-Network Computing to Industrial Scenarios

Cited by 23 publications

References 18 publications

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

A Computer Science Perspective on Digital Transformation in Production

The Road to Accountable and Dependable Manufacturing

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