Abstract
Novel orchestration architectures for 5G networks have primarily focused on enhancing Quality of Service, yet have neglected to address Quality of Experience concerns. Consequently, these systems struggle with intent recognition and End-to-End interpretability, resulting in the possibility of suboptimal control policies being developed. The 5G-ERA project has proposed and demonstrated an AI-driven intent-based networking solution for autonomous robots to address this issue. Specifically, the proposed solution employs a workflow consisting of four tools - Action Sequence Generation, Network Intent Estimation, Resource Usage Forecasting, and OSM Control Policy Generation - to map an individual vertical action's intent to a global OSM control policy. The paper describes how the 5G-ERA platform enables the onboarding and control of 5G-enabled robots and how we demonstrate the platform’s capabilities through the project’s use cases.
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References
Open-source Management and Orchestration (OSM). https://osm.etsi.org/. Accessed 01 May 2020
Cloudify. https://cloudify.co/. Accessed 01 May 2020
Trakadas, P.; Karkazis, P.; Leligou, H.C, et. al.: Comparison of management and orchestration solutions for the 5G Era. J. Sens. Actuator Netw. 4(9), (2020)
OSM Experience with NFV architecture, interfaces and information models (May 2018). https://osm.etsi.org/wikipub/index.php/Release_notes_and_whitepapers. Accessed 01 May 2020
Anaemic Domain Models, M. Fowler. https://martinfowler.com/bliki/AnemicDomainModel.html. Accessed 01 May 2020
Desot, T., Portet, F., Vacher, M.: Towards end-to-end spoken intent recognition in smart home. In: Chioreanu, I., Stan, A., Burileanu, D., (Eds.) Speech Technology and Human-Computer Dialogue: 10th International Conference, SpeD 2019, Timisoara, Romania, October 18–19, 2019, Revised Selected Papers (pp. 1–8) (2019)
Soldani, D., Rajatheva, R., Liyanage, M., Liyanage, C., Seneviratne, A.: 5G mobile systems for healthcare. In: Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring) (pp. 1–5). IEEE (2017). https://doi.org/10.1109/VTCSpring.2017.8108602
Lv, Z., Qiao, L., Wang, Q.: Cognitive robotics on 5G networks. ACM Trans. Internet Technol. 21(4), Article 92, 18 (2021). https://doi.org/10.1145/3414842
Yu, H., Lee, H., Jeon, H.: What is 5G? Emerging 5G mobile services and network requirements. Sustainability 2017, 9 (1848). https://doi.org/10.3390/su9101848
Raunholt, T., Rodriguez, I., Mogensen, P., Larsen, M.: Towards a 5G mobile edge cloud planner for autonomous mobile robots. In: Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) (pp. 01–05). IEEE (2021). https://doi.org/10.1109/VTC2021-Fall52928.2021.9625208
Abbas, K., Khan, T.A., Afaq, M., Song, W.-C.: Network slice lifecycle management for 5G mobile networks: an intent-based networking approach. IEEE Access 9, 80128–80146 (2021). https://doi.org/10.1109/ACCESS.2021.3084834
Gramaglia, M., Serrano, P., Banchs, A., Costa-Pérez, X., Gutierrez-Estevez, D., Sciancalepore, V.: Flexible connectivity and QoE/QoS management for 5G networks: the 5G NORMA view. In: 2016 IEEE International Conference on Communications Workshops (ICC) (pp. 373–379). Kuala Lumpur, Malaysia (2016). https://doi.org/10.1109/ICCW.2016.7503816
ETSI GR NFV-IFA 022 V3.1.1 (2018–04) Network Functions Virtualisation (NFV) Release 3; Management and orchestration; Report on management and connectivity for multi-site services
Femminella, M., Nencioni, G., Garroppo, R.G., Gonzalez, A.J., Helvik, B.E., Procissi, G.: Orchestration and control in software-defined 5G networks: research challenges. Wireless Commun. Mobile Comput. 2018, 6923867 (2018). https://doi.org/10.1155/2018/6923867
ETSI White Paper on Developing Software for Multi-Access Edge Computing, https://www.etsi.org/images/files/ETSIWhitePapers/etsi_wp20ed2_MEC_SoftwareDevelopment.pdf. Accessed 01 May 2020
Beshley, M., et al.: Customer-oriented quality of service management method for the future intent-based networking. Appl. Sci. 10(22), 8223 (2020). https://doi.org/10.3390/app10228223
Zheng, X., Leivadeas, A., Falkner, M.: Intent-Based Networking management with conflict detection and policy resolution in an enterprise network. Comput. Netw. 219, 109457 (2022). https://doi.org/10.1016/j.comnet.2022.109457
Leivadeas, A., Falkner, M.: VNF Placement Problem: a multi-tenant intent-based networking approach. In: Proceedings of the 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) 2021 (pp. 143–150). IEEE (2021). https://doi.org/10.1109/ICIN51074.2021.9385553
Paganelli, F., Paradiso, F., Gherardelli, M., Galletti, G.: Network service description model for VNF orchestration leveraging intent-based SDN interfaces. In: Proceedings of the 2017 IEEE Conference on Network Softwarization (NetSoft) (pp. 1–5). IEEE (2017). https://doi.org/10.1109/NETSOFT.2017.8004210
Rafiq, A., Mehmood, A., Ahmed Khan, T., Abbas, K., Afaq, M., Song, W.-C.: Intent-based end-to-end network service orchestration system for multi-platforms. Sustainability 12(7), 2782 (2020). https://doi.org/10.3390/su12072782
Sophocleous, M., et al.: AI-Driven intent-based networking for 5G enhanced robot autonomy. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08341-9_6
Khalid, O., Khan, I.A., Abbas, A.: Insights into software-defined networking and applications in fog computing. In: Zomaya, A., Abbas, A., Khan, S., Zomaya, A.Y. (eds.) Fog Computing: Theory and Practice. First published: 25 April 2020. Chapter 16 (2020). https://doi.org/10.1002/9781119551713.ch16
Sahni, Y., Cao, J., Jiang, S.: Middleware for multi-robot systems. In: Ammari, H.M. (ed.) Mission-Oriented Sensor Networks and Systems: Art and Science. SSDC, vol. 164, pp. 633–673. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-92384-0_18
5G-ERA. 5th sssgeneration enhanced robot autonomy. european commission. https://5g-era.eu/, 2021–2024
Monir, N., et al.: Seamless handover scheme for MEC/SDN-based vehicular networks. J. Sens. Actuator Netw. 11, 9 (2022). https://doi.org/10.3390/jsan11010009
Hakak, S., et al.: Autonomous vehicles in 5G and beyond: a survey. Veh. Commun. 39, 100551 (2023). https://doi.org/10.1016/j.vehcom.2022.100551ss
Devi, D.H., et al.: 5G technology in healthcare and wearable devices: a review. Sensors 23, 2519 (2023). https://doi.org/10.3390/s23052519
Siriwardhana, Y., Gür, G., Ylianttila, M., Liyanage, M.: The role of 5G for digital healthcare against COVID-19 pandemic: opportunities and challenges. ICT Express, 7(2), 244–252 (2021). ISSN 2405–9595. https://doi.org/10.1016/j.icte.2020.10.002
Huseien, G.F., Shah, K.W.: A review on 5G technology for smart energy management and smart buildings in Singapore. Energy AI 7, 100116 (2022). ISSN 2666–5468. https://doi.org/10.1016/j.egyai.2021.100116
Siurana, J.L., Dimas, M., Barrientos, A., Pairet, È., Pujol, M.: ROS-Industrial: towards an open and scalable industry 4.0 automation system. In: Jeschke, S., Brecher, C., Song, H., Rawat, D.B. (eds.) Industrial Internet of Things. Volume 67 of the book series “Advances in Intelligent Systems and Computing”. Springer International Publishing, pp. 491–503 (2017). https://doi.org/10.1007/978-3-319-42559-7_38
Schaal, S.: Learning robot control. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 983–987. MIT Press, Cambridge, MA (2002)
Schaal, S., Atkeson, C.G.: Learning control in robotics. IEEE Robot. Autom. Mag. 17(2), 20–29 (2010). https://doi.org/10.1109/MRA.2010.936957
Špaňhel, J., Kapinus, M., Dobeš, P., Materna, Z., Juránek, R., Klepárník, P.: Reference NetApps for 5G-ERA - Initial version. In: D4.3, 5th Generation Enhanced Robot Autonomy, European Commission, 30 June 2022
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No 101016681.
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Gavrielides, A. et al. (2023). Implementing Network Applications for 5G-Enabled Robots Through the 5G-ERA Platform. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_4
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