TY - GEN
T1 - AI-driven intent-based networking for 5G enhanced robot autonomy
AU - Sophocleous, Marios
AU - Lessi, Christina C.
AU - Xu, Zhao
AU - Špaňhel, Jakub
AU - Qiu, Renxi
AU - Lendinez Ibanez, Adrian
AU - Chondroulis, Ioannis
AU - Belikaidis, Ioannis
N1 - Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022/6/10
Y1 - 2022/6/10
N2 - 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 AI-driven intent-based networking for autonomous robots is 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.
AB - 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 AI-driven intent-based networking for autonomous robots is 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.
KW - 5G
KW - 5G-ERA
KW - autonomous robots
KW - enhanced robot autonomy
KW - intent-based networking
KW - machine learning
KW - semantic models
KW - Enhanced robot autonomy
KW - Intent-based networking
KW - Semantic models
KW - Machine learning
KW - Autonomous robots
UR - https://www.scopus.com/pages/publications/85133263129
U2 - 10.1007/978-3-031-08341-9_6
DO - 10.1007/978-3-031-08341-9_6
M3 - Conference contribution
SN - 9783031083419
VL - 652
T3 - IFIP Advances in Information and Communication Technology
SP - 61
EP - 70
BT - Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops - MHDW 2022, 5G-PINE 2022, AIBMG 2022, ML@HC 2022, and AIBEI 2022, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Macintyre, John
A2 - Cortez, Paulo
PB - Springer
T2 - 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI)
Y2 - 17 June 2022 through 20 June 2022
ER -