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Real-time Performance Evaluation of LTE for IIoT
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. InfoVista Sweden.ORCID iD: 0000-0003-1139-6998
InfoVista Sweden.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2018 (English)In: Proceedings of the 43rd IEEE Conference on Local Computer Networks (LCN) / [ed] Soumaya Cherkaoui, Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Industrial Internet of Things (IIoT) is claimed to be a global booster technology for economic development. IIoT brings bulky use-cases with a simple goal of enabling automation, autonomation or just plain digitalization of industrial processes. The abundance of interconnected IoT and CPS generate additional burden on the telecommunication networks, imposing number of challenges to satisfy the key performance requirements. In particular, the QoS metrics related to real-time data exchange for critical machine-to-machine type communication. This paper analyzes a real-world example of IIoT from a QoS perspective, such as remotely operated underground mining vehicle. As part of the performance evaluation, a software tool is developed for estimating the absolute, one-way delay in end-toend transmissions. The measured metric is passed to a machine learning model for one-way delay prediction based on LTE RAN measurements using a commercially available cutting-edge software tool. The achieved results prove the possibility to predict the delay figures using machine learning model with a coefficient of determination up to 90%.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018.
Keywords [en]
IIoT, LTE, QoS, delay, jitter, real-time, critical IoT
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-70316DOI: 10.1109/LCN.2018.8638081Scopus ID: 2-s2.0-85062855153OAI: oai:DiVA.org:ltu-70316DiVA, id: diva2:1237755
Conference
43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-04-08

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Minovski, DimitarAndersson, Karl

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CiteExportLink to record
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