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Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
Mid Sweden University, Sweden.ORCID iD: 0000-0001-5808-1382
Mid Sweden University, Sweden.ORCID iD: 0000-0002-1797-1095
Mid Sweden University, Sweden.
Mid Sweden University, Sweden.
2018 (English)In: Sensors, E-ISSN 1424-8220, Vol. 18, no 5, article id 1532Article in journal (Refereed) Published
Abstract [en]

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

Place, publisher, year, edition, pages
2018. Vol. 18, no 5, article id 1532
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:mdh:diva-62051DOI: 10.3390/s18051532ISI: 000435580300231PubMedID: 29757227Scopus ID: 2-s2.0-85047063861OAI: oai:DiVA.org:mdh-62051DiVA, id: diva2:1742727
Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-04-12Bibliographically 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, Mehrzad

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