Publication result detail

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.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

RIV year

2023

Released

04.04.2022

Publisher

Springer Nature Switzerland AG

Location

Cham

Book

AIAI 2022 IFIP WG 12.5 International Workshops

Edition

IFIP Advances in Information and Communication Technology

ISBN

1868-422X

Periodical

IFIP Advances in Information and Communication Technology

Volume

652

Number

2022

State

unknown

Pages from

61

Pages to

70

Pages count

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"
}