Real-time Performance Evaluation of LTE for IIoT

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 a...

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Bibliographic Details
Published in2018 IEEE 43rd Conference on Local Computer Networks (LCN) pp. 623 - 631
Main Authors Zhohov, Roman, Minovski, Dimitar, Johansson, Per, Andersson, Karl
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
critical IoT
Data mining
delay
Delays
IIoT
jitter
Long Term Evolution
LTE
QoS
Quality of service
real-time
Real-time systems
Streaming media
Online AccessGet full text
DOI10.1109/LCN.2018.8638081

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Summary: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-to-end 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%.
DOI:10.1109/LCN.2018.8638081