# Implementing Network Applications for 5G-Enabled Robots Through the 5G-ERA Platform

Andreas Gavrielides, Marios Sophocleous, Christina C. Lessi, George Agapiou, Jakub Špaňhel, Adrian Lendinez, Renxi Qiu, Dayou Li

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Andreas Gavrielides, Marios Sophocleous, Christina C. Lessi, George Agapiou, Jakub Špaňhel, et al. Implementing Network Applications for 5G-Enabled Robots Through the 5G-ERA Platform. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.55-65, 10.1007/978-3-031-34171-7_4. hal-05411769

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# Implementing Network Applications for 5G-Enabled Robots Through the 5G-ERA Platform

Andreas Gavrielides<fnref n="1" />(✉) ID, Marios Sophocleous<fnref n="1" /> ID, Christina C. Lessi<fnref n="2" />, George Agapiou<fnref n="3" /> ID, Jakub Špaňhel<fnref n="4" /> ID, Adrian Lendinez<fnref n="5" />, Renxi Qiu<fnref n="5" /> ID, and Dayou Li<fnref n="5" />

**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.

**Keywords:** 5G · Intent-based networking · Enhanced robot autonomy · 5G-ERA · Machine Learning · Semantic models · autonomous robots

## 1 Introduction

In recent years, 5G technology has gained significant attention from both the scientific and industrial communities. Its advantages are particularly attractive to various sectors, including the robotics industry. However, current innovative 5G orchestration architectures have primarily been designed for service delivery [1–3] without vertical-specific knowledge. These architectures rely on information models to manage the life cycle of services and resources [3, 4], which are based on the concept of Anaemic Domain Models

<footnote n="1">¹ eBOS Technologies Ltd., Arch. Makariou III and Mesaorias 1, 2090 Lakatamia, Nicosia, Cyprus
andreasg@ebos.com.cy</footnote>

<footnote n="2">² Hellenic Telecommunications Organization (OTE) S.A., 99, Kifissias Avenue, 15124 Maroussi, Athens, Greece</footnote>

<footnote n="3">³ WINGS ICT Solutions, Nea Smymi, 189, Siggrou Avenue, 17121 Athens, Greece</footnote>

<footnote n="4">⁴ Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic</footnote>

<footnote n="5">⁵ School of Computer Science, University of Bedfordshire, Luton, UK</footnote>
[5] optimized for creating, reading, updating, and deleting (CRUD) services in a pro- cedural style. Consequently, implicit intents of the services are not considered, leading orchestrators to prioritize Quality of Service (QoS) over Quality of Experience (QoE). This results in inherited problems for orchestration systems, such as intent recognition and End-to-End (E2E) interpretability, potentially creating ineffective control policies [6]. While some existing 5G vertical applications have partially addressed this issue by limiting the number of technology stacks, the robotic vertical typically requires compo- nents from multiple vendors with various technology stacks. Therefore, a solution that maintains intent recognition and maps user requirements into measurable network KPIs is necessary for 5G experimental facilities involving multiple technology stacks [7–10].

The implementation of intent-based networking (IBN) is becoming increasingly essential for realizing the full potential of 5G-Enhanced Robot Autonomy (5G-ERA). IBN is a network architecture that leverages machine learning algorithms and artificial intelligence to provide automated network management, policy enforcement, and secu- rity, among other functions. In the context of 5G-ERA, IBN is utilized to streamline the deployment of Network Applications, which are tailored for specific vertical use cases such as Public Protection and Disaster Relief (PPDR), transport, healthcare, and industry 4.0.

The Intent-based, front-end Dashboard (IBD) is crucial in facilitating the onboarding and utilization of the associated Network Applications. It provides an intuitive interface for users to visualize and manage network resources and configure policies, among other functions. This allows users to easily understand the behaviour of the network, which in turn enables them to make informed decisions regarding the deployment and management of Network Applications.

Moreover, the IBD provides a unified view of the entire network infrastructure, which is crucial for ensuring the seamless integration of the various network components. This includes the middleware, which serves as a platform for hosting the Network Appli- cations, and the edge computing infrastructure, enabling real-time data processing and analysis.

In summary, the paper expands on how the IBD is a critical component of the IBN [11] architecture for 5G-ERA use cases. It facilitates the onboarding and utilization of Net- work Applications by providing an intuitive interface for managing network resources, monitoring network performance, and configuring policies. By providing a unified view of the entire network infrastructure, it enables users to make informed decisions regard- ing the deployment and management of Network Applications, thereby maximizing the potential of 5G-ERA.

## 2 5G-Enabled Autonomous Robots: An Intent-Based Networking Paradigm

The objective of intent-based networking for autonomous robots is to enhance the QoE of 5G orchestrators for vertical applications [12]. In order to achieve optimal experience for individual 5G-based autonomous robots, it is crucial to specify and allocate resources required by the connected intelligence optimally. The intent-based networking technique [13–17] predicts the need for intelligence based on the intents. It specifies policies
for individual applications to ensure efficient management, topology, placement, and resource optimization within 5G and cloud environments. Connected intelligence in autonomous use cases requires dynamic and repeated shifting of computing and storage among robots, edges, and the central cloud. To facilitate this process, partial information will be replicated among Network Services (NSs) deployed in different locations.

The challenge of deploying robotic applications at a large scale with robust capa-
bilities to react to real-world scenarios is a persistent issue. The concept of deployable
robotics involves enhancing the ability of robots to be deployed effectively in real-
world environments. Currently, this ability is constrained by the fragmented nature of
robot intelligence and limited scalability. To overcome these limitations, a paradigm
of transparent knowledge, connected intelligence, and scalable skills must be formu-
lated. Autonomous operation with the ability to handle unexpected situations is a key
characteristic of deployable robotics under this framework.

To tailor NSs under intent-based networking, different configurations of Virtual Net-
work Functions (VNFs) and slices are required [18–20]. The creation of slices, at the level
of complexity of the use cases, is still problematic for existing testbeds. 5G experimental
facilities must be adapted towards a cloud-native approach for efficient service delivery
on enhanced robot autonomy. Meanwhile, intent-based networking also enhances the
existing NSs using a cloud-native approach with respect to the scalability, availability
and feature velocity of the NSs as expected by autonomous robots. Therefore, the devel-
opment of intent-based networking fills the gaps between existing 5G product vision
and cloud-native deployment required by the robot autonomy enhancement. Overall, the
development of intent-based networking for autonomous robots reveals how operational
processes of essential robotic capabilities can be integrated into a rich domain model
supporting advanced orchestration.

In the 5G-enabled Non-Public Networks (NPNs) context, a middleware implemented
through the Platform as a Service (PaaS) paradigm facilitates intent-based networking.
This allows for creating customized virtual network platforms for specific vertical appli-
cations rather than requiring vertical applications to conform to the network infrastruc-
ture. By adopting this approach, the management of network resources can be optimized,
leading to an enhanced QoE for users. For instance, this methodology could facilitate 5G-
enhanced robot autonomy by enabling the offloading of learning tasks in a multi-domain
multi-administration environment.

3 The 5G-ERA Platform as a Network Application Facilitator

The IBD is an important component of the 5G-ERA project, as it enables the use of Semantic & Machine Learning (ML) tools for better network resource management. While each robot vendor may have an existing User Interface (UI) for controlling and communicating with their robots, there is no existing UI for the 5G-ERA's unique ML tool for resource usage prediction based on the user's intent as described by Sophocleous et al. [21].
## 3.1 Benefits of Intent-Based Networking

The 5G-ERA envisions an intent-based networking dashboard (IBD) that can offer significant benefits to users in effectively managing and optimizing their Network Applications. Our IBD provides users with a user-friendly interface to visualize and control their various Network Applications on the network infrastructure.

One of the main advantages of an intent-based networking dashboard is that it provides users with a single view of their Network Applications, irrespective of the underlying infrastructure. This allows users to manage their Network Applications more efficiently without worrying about the complexities of the infrastructure. For example, they can easily create, delete, or modify Network Applications or adjust the QoS settings for each application without having to deal with the technical details of the network infrastructure.

In addition to the benefits mentioned earlier, an intent-based networking dashboard can offer users several other advantages.

Firstly, it can provide an intuitive and customizable interface that allows users to interact with their Network Applications in a way that makes sense to them. This means that users can quickly and easily access the information that they need without having to navigate through complex menus or interfaces. Furthermore, a customizable interface means that users can tailor the dashboard to their specific needs, which can increase their efficiency and productivity. Secondly, an intent-based networking dashboard can enable users to automate certain tasks related to managing their Network Applications. Thirdly, an intent-based networking dashboard can provide users with insights into how their Network Applications are being used [22]. This can be particularly valuable for organizations with multiple departments or locations using the same network infrastructure. By providing detailed analytics and reporting, an intent-based networking dashboard can help users identify usage patterns, monitor resource utilization, and allocate resources more effectively.

Overall, our IBD will be a valuable tool for users who need to manage and optimize their Network Applications. By providing a user-friendly interface, it can enable users to achieve their business objectives more effectively and efficiently.

## 3.2 Functionalities Offered by the 5G-ERA Paradigm

The IBD's original design and functionalities were described by Sophocleous et al. [21]. To accommodate the needs of the Network Applications users and developers, we have incorporated a number of additional functionalities described in this section.

The IBD features a grid that provides robot-task information and allows users to view the number of tasks executed by the robot fleet and sort their task status by date. The IBD approach used in 5G-ERA is visualized in Fig. 1. A basic control grid has been implemented for the cloud/edge times in the topology, enabling users to easily view the number of clouds and their current status in terms of activity for supporting the robot fleet.

Through the IBD, users will be informed on what actions are in each action sequence loaded in the knowledge base and visualize the company topology and the action sequences with their respective relationships among robots and tasks. Through the IBD
<figure><img src="image_3.png" /><figcaption>Fig. 1. Workflow of developed tools for intent-based management</figcaption></figure>

and access control implementation, the system administrator can view, delete and mod-
ify existing middleware policies. This is necessary to change the way the middleware
plans and works.

A critical feature implemented is the ability to onboard models for middleware entities such as robots, edge, clouds, tasks, instances etc. [23], with their relationships. This will allow the system administrator to create and provision new robots, edges etc., into the middleware database.

By implementing a control grid for Network Applications, the user can view the onboarding process and information about the project's Network Applications, as well as the associated Robot Operating System (ROS) version and distribution. This control grid will also provide users with the information they may require, such as the Network Application distribution, what type of application the Network Application can be used for (e.g., perception, navigation) and the Network Application ID (for example, face recognition).

Finally, through implementing a control grid for the robots, users in this section of
the IBD have a table that describes the basic control grid for Robots like onboarding,
ID, ROS version, and distribution, as well as if the robot is online or offline.

4 5G-ERA Use Cases

As part of the 5G-ERA project [24], pertinent Network Applications will be prototyped for each selected use case [33]. This will confirm the innovative capabilities of the project's experimental facilities and showcase the advancements achieved in 5G-Enhanced Robot Autonomy across various vertical sectors. The project has identified four specific vertical sectors, namely PPDR, transport, healthcare, and industry 4.0, as the focus of its use cases.

4.1 Use Case 1: Public Protection and Disaster Relief (PPDR)

The 5G-ERA project is designing a novel Network Application specifically facilitat-
ing autonomous PPDR services in unstructured environments. This use case has been
carefully chosen to showcase the technological advancements made possible by 5G communication solutions and demonstrate their potential to serve as gateways between existing legacy technologies and cutting-edge communication solutions.

The primary objective of this use case is to enable close-to-seamless handover and switch capabilities between Edges [25] (i.e., the peripheral network elements) and Mobile Operators. Achieving this objective requires developing robust communication solutions that can seamlessly integrate and switch between different network environments, providing uninterrupted services to the end users. The use case specifications are summarized in Fig. 2.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. 5G-ERA Use Cases Specifications: Use Case 1 (upper left side), Use Case 2 (upper right side). Use Case 3 (bottom left side), Use Case 4 (bottom right side) [33].</figcaption></figure>
This use case seeks to address the challenges of unstructured environments in the context of PPDR services. By leveraging the advanced capabilities of 5G communication solutions, the Network Application currently being finalized, facilitates the deployment of autonomous PPDR services that can operate in diverse and unpredictable environments. The IBD, under continuous updates, provides the functionality required to support the Network Application associated and deliver edge simultaneous localization and mapping (SLAM) services driven by localization and mapping and the central cloud SLAM service for feature optimization and support of semantic maps based on global knowledge. Additionally, the provision of an intervention service that will provide troubleshooting and emergent interventions is also facilitated through the IBD.

## 4.2 Use Case 2: 5G Enhanced Semi-autonomous Transport

The primary goal of this use case is to enable robots to operate in teleoperated and semi-autonomous modes with the help of 5G technology [26]. By leveraging the capabilities of 5G communication solutions, this use case demonstrates the potential for efficient and reliable communication between robots and the transport infrastructure.

The Network Application currently under development supports semi-autonomous transport operations, providing enhanced real-time decision-making and navigation capabilities. The use of lightweight onboard unit virtualization techniques enables the creation of a flexible and scalable fog environment capable of adapting to the changing needs of the transport domain. The role of the IBD is to support the various data flows and QoS to map several data streams from sources such as sensors and video streaming, as well as facilitate the security of the Network Applications' endpoints to preserve sensitive and confidential data.

This use case showcases the potential of 5G technology to reduce service creation time and enhance the capabilities of the transport sector. By leveraging 5G communication solutions, the Network Application will enable seamless communication and collaboration between robots and the transport infrastructure, leading to more efficient and reliable transport operations. The use case specification is summarized in Fig. 2.

## 4.3 Use Case 3: 5G Enhanced Healthcare Robots

The 5G-ERA project features a use case focused on designing and developing a new Network Application that supports various data flows with different priorities and QoS. The primary goal of this use case is to enable the mapping of multiple data streams, including sensor data, alerts, and video streaming, while demonstrating the security of Network Application endpoints and data streams to protect sensitive and confidential data.

The Network Application developed under this use case will enable robots to carry supporting materials on behalf of healthcare workers, depending on their level of autonomy. Using 5G technology will allow for efficient and reliable communication between the robots and healthcare infrastructure, enabling the robots to carry out their tasks safely and effectively [27–29].

One of the key aspects of this use case is demonstrating the security of the Network Applications endpoints and their data streams. To achieve this, the Network Application
leverages the capabilities of 5G technology to ensure that sensitive and confidential data is protected at all times. The Network Application also supports the mapping of data flows with different priorities and QoS, allowing for efficient and effective communication between the robots and healthcare infrastructure. Additionally, the IBD will ensure that the Network Application endpoints are secured, ensuring that sensitive and confidential data are preserved. The IBD supports the various data flows that will be generated and prioritize the mapping of such streams coming from sensors, video streaming, etc. Through the IBD, users will be able to use the local and task planning services, which serve the purpose of planning the tasks based on local information and the edge task planning services, which in turn plan the tasks integrated with a smart environment. Finally, by providing a central cloud planning service, the IDB facilitates the feature and semantic maps optimization based on global knowledge and semantic-based task planning. The use case specifications are summarized in Fig. 2.

Overall, this use case contributes significantly to the 5G-ERA project's [24] goal of advancing the state-of-the-art in 5G-Enhanced Robot Autonomy. By showcasing the practical applications of 5G technology in the healthcare sector, this use case demonstrates the potential for robots to provide valuable support to healthcare workers during times of crisis while also ensuring that sensitive and confidential data is protected at all times.

## 4.4 Use Case 4: 5G-Remote Assistance for Manufacturing Process

Finally, the 5G-ERA project [24] features a use case that focuses on enabling an interface between the Robot Operating System – Industry (ROS-I) [30] and an industrial production cell. The primary goal of this use case is to leverage Industry 4.0 techniques and the 5G-ERA middleware to support the deployment of highly flexible and robust manufacturing for both small and medium-sized enterprises (SMEs) and large-scale fabricators.

To demonstrate the potential of this use case, the project uses a brazing cell, showcasing how the algorithms for human-robot learning control [31, 32] can be employed for collaborative output tasks. Using these algorithms, the use case demonstrates how the project achieves seamless communication between robots and human operators, enabling the production cell to be highly flexible and responsive to changing demands.

One of the key advantages of this use case is that it enables the deployment of highly flexible and robust manufacturing for both SMEs and large-scale fabricators. By leveraging Industry 4.0 techniques and the 5G-ERA middleware, the use case aims to create a seamless interface between the production cell and the ROS-I system, enabling the production cell to be responsive to changing demands. The IBD will serve a variety of services coming from the Network Application associated with this use case, including the brazing robot operation through the task planning based on the brazing process specification, the edge operation planning services, the central cloud service tasked with optimising features and semantic maps based on global knowledge, the learning service tasked with updating a dense and semantic map proactively as well as updating the robot skills set thus improving the accuracy and autonomy level. The features of this use case are supported by the connection of an external dashboard located at the premises of the use case provider. The specifications concerning this use case are summarized in Fig. 2.
Overall, this use case will contribute significantly to the 5G-ERA project's [24] goal of advancing the state-of-the-art in 5G-Enhanced Robot Autonomy. By showcasing the potential of Industry 4.0 techniques and the 5G-ERA middleware to enable highly flexible and robust manufacturing, this use case will demonstrate how robots can be integrated seamlessly into industrial production processes, enhancing productivity and efficiency.

## 5 Conclusions

The 5G-ERA project aims to showcase the potential of 5G and semantic models in achieving enhanced robot autonomy across various vertical sectors. Using machine learning tools in conjunction with the intent-based networking dashboard (IBD) will enable the development of cloud-native connected robotics that can utilize 5G and 6G resources. The IBD serves as a crucial component of the project by providing a platform for onboarding and utilization of Network Applications while simultaneously monitoring and optimizing QoE key performance indicators (KPIs) set for each use case.

Demonstrating the project's use cases can validate the functionalities of the IBD and the associated network applications. Developing a reference design for connected robotics that can leverage 5G resources and the scalability of the communication interface for ROS can facilitate the mass deployment of cloud-native connected robotics.

Using semantic models and machine learning tools in 5G-enhanced robot autonomy will pave the way for new and innovative applications in various sectors. The 5G-ERA project is a significant step towards achieving this goal and can contribute significantly to advancing robotics technology.

**Acknowledgements.** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Grant Agreement No 101016681.

## References

1. Open-source Management and Orchestration (OSM). https://osm.etsi.org/. Accessed 01 May 2020

2. Cloudify. https://cloudify.co/. Accessed 01 May 2020

3. 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)

4. 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

5. Anaemic Domain Models, M. Fowler. https://martinfowler.com/bliki/AnaemicDomainModel.html. Accessed 01 May 2020

6. 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)
7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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
22. 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

23. 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

24. 5G-ERA. 5th ssgeneration enhanced robot autonomy. european commission. https://5g-era.eu/, 2021–2024

25. 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

26. 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

27. Devi, D.H., et al.: 5G technology in healthcare and wearable devices: a review. Sensors **23**, 2519 (2023). https://doi.org/10.3390/s23052519

28. 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

29. 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

30. 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

31. 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)

32. 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

33. 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