Detail publikačního výsledku

AI-driven intent-based networking for 5G enhanced robot autonomy

SOPHOCLEOUS, M.; LESSI, C.; XU, Z.; ŠPAŇHEL, J.; QIU, R.; LENDINEZ, A.; CHONDROULIS, I.; BELIKAIDIS, I.

Originální název

AI-driven intent-based networking for 5G enhanced robot autonomy

Anglický název

AI-driven intent-based networking for 5G enhanced robot autonomy

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Innovative 5G orchestration architectures so far, have been mainly designed and optimized for Quality of Service (QoS), but are not aware of Quality of Experience (QoE). This makes intent recognition and End-to-End interpretability an inherited problem for orchestration systems, leading to possible creation of ineffective control policies. In this paper, an intent-based networking for autonomous robots is being proposed and demonstrated through the 5G-ERA project. In particular, to map an intent from individual vertical action to a global OSM control policy, a workflow of four tools is proposed: i) Action Sequence Generation, ii) Network Intent Estimation, iii) Resource Usage Forecasting, and iv) OSM Control Policy Generation. All of these tools are described in the paper with specific function descriptions, inputs, outputs and the semantic models/Machine Learning tools that have been used. Finally, the paper presents the developed intent-based dashboard for the visualization of the tools outputs, whilst taking QoE into consideration.

Anglický abstrakt

Innovative 5G orchestration architectures so far, have been mainly designed and optimized for Quality of Service (QoS), but are not aware of Quality of Experience (QoE). This makes intent recognition and End-to-End interpretability an inherited problem for orchestration systems, leading to possible creation of ineffective control policies. In this paper, an intent-based networking for autonomous robots is being proposed and demonstrated through the 5G-ERA project. In particular, to map an intent from individual vertical action to a global OSM control policy, a workflow of four tools is proposed: i) Action Sequence Generation, ii) Network Intent Estimation, iii) Resource Usage Forecasting, and iv) OSM Control Policy Generation. All of these tools are described in the paper with specific function descriptions, inputs, outputs and the semantic models/Machine Learning tools that have been used. Finally, the paper presents the developed intent-based dashboard for the visualization of the tools outputs, whilst taking QoE into consideration.

Klíčová slova

5G, Intent-based networking, Enhanced robot autonomy, 5G-ERA, Machine Learning, Semantic models, autonomous robots

Klíčová slova v angličtině

5G, Intent-based networking, Enhanced robot autonomy, 5G-ERA, Machine Learning, Semantic models, autonomous robots

Autoři

SOPHOCLEOUS, M.; LESSI, C.; XU, Z.; ŠPAŇHEL, J.; QIU, R.; LENDINEZ, A.; CHONDROULIS, I.; BELIKAIDIS, I.

Rok RIV

2023

Vydáno

04.04.2022

Nakladatel

Springer Nature Switzerland AG

Místo

Cham

Kniha

AIAI 2022 IFIP WG 12.5 International Workshops

Edice

IFIP Advances in Information and Communication Technology

ISSN

1868-422X

Periodikum

IFIP Advances in Information and Communication Technology

Svazek

652

Číslo

2022

Stát

neuvedeno

Strany od

61

Strany do

70

Strany počet

10

BibTex

@inproceedings{BUT182277,
  author="Marios {Sophocleous} and Christina {Lessi} and Zhao {Xu} and Jakub {Špaňhel} and Renxi {Qiu} and Adrian {Lendinez} and Ioannis {Chondroulis} and Ioannis {Belikaidis}",
  title="AI-driven intent-based networking for 5G enhanced robot autonomy",
  booktitle="AIAI 2022 IFIP WG 12.5 International Workshops",
  year="2022",
  series="IFIP Advances in Information and Communication Technology",
  journal="IFIP Advances in Information and Communication Technology",
  volume="652",
  number="2022",
  pages="61--70",
  publisher="Springer Nature Switzerland AG",
  address="Cham",
  doi="10.1007/978-3-031-08341-9\{_}6",
  issn="1868-422X"
}