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From brown-field to future industrial networks, a case study
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Division of Industrial Systems, RISE-Research Institutes of Sweden, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7159-7508
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-2419-2735
2021 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 7, article id 3231Article in journal (Refereed) Published
Abstract [en]

The network infrastructures in the future industrial networks need to accommodate, manage and guarantee performance to meet the converged Internet technology (IT) and operational technology (OT) traffics requirements. The pace of IT-OT networks development has been slow despite their considered benefits in optimizing the performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for green-field configuration to lack of outlines for evolving from brown-fields to the converged network. Focusing on the brown-field, this study provides additional insight into a brown-field characteristic to set a baseline that enables the subsequent step development towards the future’s expected converged networks. The case study highlights differences between real-world network behavior and the common assumptions for analyzing the network traffic covered in the literature. Considering the unsatisfactory performance of the existing methods for characterization of brownfield traffic, a performance and dynamics mixture measurement is proposed. The proposed method takes both IT and OT traffic into consideration and reduces the complexity, and consequently improves the flexibility, of performance and configuration management of the brown-field.

Place, publisher, year, edition, pages
MDPI AG , 2021. Vol. 11, no 7, article id 3231
Keywords [en]
Brown-fields characteristics, Converged networks, Network performance measurement
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Communication Systems
Identifiers
URN: urn:nbn:se:mdh:diva-53928DOI: 10.3390/app11073231ISI: 000638324800001Scopus ID: 2-s2.0-85104080852OAI: oai:DiVA.org:mdh-53928DiVA, id: diva2:1546608
Available from: 2021-04-22 Created: 2021-04-22 Last updated: 2023-03-13Bibliographically approved
In thesis
1. Evolving Industrial Networks: Data-Driven Network Traffic Modelling and Monitoring
Open this publication in new window or tab >>Evolving Industrial Networks: Data-Driven Network Traffic Modelling and Monitoring
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The concept of Industrial IoT encompasses the joint applicability of operation and information technologies to expand the efficiency expectation of automation to green and flexible processes with innovative products and services. Future industrial networks need to accommodate, manage and guarantee the performance of converged traffic from different technologies. The network infrastructures are transforming to enable data availability for advanced applications and enhance flexibility. Nonetheless, the pace of IT–OT networks development has been slow despite their considered benefits in optimising performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for greenfield configuration to the lack of outlines for evolving from brownfield installations without interrupting the operation of ongoing processes. One tangible gap is the lack of insight into the brownfield installation in operation. This dissertation explores the possible evolutionary steps from brownfield installations to future industrial networks.The goal is to ensure the uninterrupted performance of brownfield installations on the path of evolving to the envisioned smart factories. It addresses the gap between the state of the art and state of practice, and the technical prerequisites of the integrated traffic classes for the development of an IIoT monitoring mechanism. A novel lightweight learning algorithm at the sensor level for an IIoT compliance monitoring system, together with a case study of traffic collected from a brownfield installation, provides the baseline of comparative analysis between the common assumptions and the state of practice. The identified gaps and challenges to address them directs the research for proposing a two-step aggregated traffic modelling by introducing new measurement method and performance indicator parameters for capturing the communication dynamics. Lastly, the sensor-level learning algorithm is refined with the knowledge gained from practice and research contributions to propose an in-band telemetry mechanism for monitoring aggregated network traffic.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 375
National Category
Communication Systems Computer Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-62063 (URN)978-91-7485-588-3 (ISBN)
Public defence
2023-06-13, Lambda, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2023-03-14 Created: 2023-03-13 Last updated: 2023-05-23Bibliographically approved

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Lavassani, MehrzadÅkerberg, JohanBjörkman, Mats

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