% % This file was created by the TYPO3 extension % bib % --- Timezone: UTC % Creation date: 2024-12-03 % Creation time: 17-20-10 % --- Number of references % 4 % @Inproceedings { 2023-wagner-lcn-repel, title = {Retrofitting Integrity Protection into Unused Header Fields of Legacy Industrial Protocols}, year = {2023}, month = {10}, url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-wagner-repel.pdf}, publisher = {IEEE}, booktitle = {48th IEEE Conference on Local Computer Networks (LCN), Daytona Beach, Florida, US}, event_place = {Daytona Beach, Florida, US}, event_name = {IEEE Conference on Local Computer Networks (LCN)}, event_date = {Oktober 1-5, 2023}, state = {accepted}, language = {en}, reviewed = {1}, author = {Wagner, Eric and Rothaug, Nils and Wolsing, Konrad and Bader, Lennart and Wehrle, Klaus and Henze, Martin} } @Inproceedings { 2023_pennekamp_benchmarking_comparison, title = {Designing Secure and Privacy-Preserving Information Systems for Industry Benchmarking}, year = {2023}, month = {6}, day = {15}, volume = {13901}, pages = {489-505}, abstract = {Benchmarking is an essential tool for industrial organizations to identify potentials that allows them to improve their competitive position through operational and strategic means. However, the handling of sensitive information, in terms of (i) internal company data and (ii) the underlying algorithm to compute the benchmark, demands strict (technical) confidentiality guarantees—an aspect that existing approaches fail to address adequately. Still, advances in private computing provide us with building blocks to reliably secure even complex computations and their inputs, as present in industry benchmarks. In this paper, we thus compare two promising and fundamentally different concepts (hardware- and software-based) to realize privacy-preserving benchmarks. Thereby, we provide detailed insights into the concept-specific benefits. Our evaluation of two real-world use cases from different industries underlines that realizing and deploying secure information systems for industry benchmarking is possible with today's building blocks from private computing.}, note = {Lecture Notes in Computer Science (LNCS), Volume 13901}, keywords = {real-world computing; trusted execution environments; homomorphic encryption; key performance indicators; benchmarking}, tags = {internet-of-production}, url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-industry-benchmarking.pdf}, publisher = {Springer}, booktitle = {Proceedings of the 35th International Conference on Advanced Information Systems Engineering (CAiSE '23), June 12-16, 2023, Zaragoza, Spain}, event_place = {Zaragoza, Spain}, event_name = {35th International Conference on Advanced Information Systems Engineering (CAiSE '23)}, event_date = {June 12-16, 2023}, ISBN = {978-3-031-34559-3}, ISSN = {0302-9743}, DOI = {10.1007/978-3-031-34560-9_29}, reviewed = {1}, author = {Pennekamp, Jan and Lohm{\"o}ller, Johannes and Vlad, Eduard and Loos, Joscha and Rodemann, Niklas and Sapel, Patrick and Fink, Ina Berenice and Schmitz, Seth and Hopmann, Christian and Jarke, Matthias and Schuh, G{\"u}nther and Wehrle, Klaus and Henze, Martin} } @Incollection { 2023_pennekamp_crd-a.i, title = {Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead}, year = {2023}, month = {2}, day = {8}, pages = {35-60}, abstract = {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.}, keywords = {Cyber-physical production systems; Data streams; Industrial data processing; Industrial network security; Industrial data security; Secure industrial collaboration}, tags = {internet-of-production}, url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-iop-a.i.pdf}, publisher = {Springer}, series = {Interdisciplinary Excellence Accelerator Series}, booktitle = {Internet of Production: Fundamentals, Applications and Proceedings}, ISBN = {978-3-031-44496-8}, DOI = {10.1007/978-3-031-44497-5_2}, reviewed = {1}, author = {Pennekamp, Jan and Belova, Anastasiia and Bergs, Thomas and Bodenbenner, Matthias and B{\"u}hrig-Polaczek, Andreas and Dahlmanns, Markus and Kunze, Ike and Kr{\"o}ger, Moritz and Geisler, Sandra and Henze, Martin and L{\"u}tticke, Daniel and Montavon, Benjamin and Niemietz, Philipp and Ortjohann, Lucia and Rudack, Maximilian and Schmitt, Robert H. and Vroomen, Uwe and Wehrle, Klaus and Zeng, Michael} } @Incollection { 2023_rueppel_crd-b2.ii, title = {Model-Based Controlling Approaches for Manufacturing Processes}, year = {2023}, month = {2}, day = {8}, pages = {221-246}, abstract = {The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP).}, keywords = {Process control; Model-based control; Data aggregation; Model identification; Model optimization}, tags = {internet-of-production}, url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-rueppel-iop-b2.i.pdf}, publisher = {Springer}, series = {Interdisciplinary Excellence Accelerator Series}, booktitle = {Internet of Production: Fundamentals, Applications and Proceedings}, ISBN = {978-3-031-44496-8}, DOI = {10.1007/978-3-031-44497-5_7}, reviewed = {1}, author = {R{\"u}ppel, Adrian Karl and Ay, Muzaffer and Biernat, Benedikt and Kunze, Ike and Landwehr, Markus and Mann, Samuel and Pennekamp, Jan and Rabe, Pascal and Sanders, Mark P. and Scheurenberg, Dominik and Schiller, Sven and Xi, Tiandong and Abel, Dirk and Bergs, Thomas and Brecher, Christian and Reisgen, Uwe and Schmitt, Robert H. and Wehrle, Klaus} }