How Blockchain can extend the remote-control range of a Robot while Machine learning is keeping it profitable
Kiril Antevski, PhD Student at University Carlos III of Madrid, Madrid, Spain
External Presentation (External Speaker)
This talk consists of two articles:
1) DLT Federation for Edge Robotics – where the author shows how the remote-control range of a robot can be extended and spread dynamically across different administrative domains with the use of Blockchain.
The concept of federation in 5G and NFV networks aims to provide orchestration of services across multiple administrative domains. Edge robotics, as a field of robotics, implements the robot control on the network edge by relying on low-latency and reliable access connectivity. In this paper, we propose a solution that enables Edge robotics service to expand its service footprint or access coverage over multiple administrative domains. We propose application of Distributed ledger technologies (DLTs) for the federation procedures to enable private, secure and trusty interactions between undisclosed administrative domains. The solution is applied on a real-case Edge robotics experimental scenario. The results show that it takes around 19 seconds to deploy & federate an Edge robotics service in an external/anonymous domain without any service down-time.
2) A Q-learning strategy for federation of 5G services – a Machine learning (ML) strategy to generate profitable federation decision for a service provider, such as the extension of a remote-control range, in order to be still profitable for the service provider.
5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive “greedy” approach in a realistic scenario.
About Kiril Antevski
Kiril Antevski received the B.S. degree in telecommunication engineering from the Saints Cyril and Methodius University of Skopje, Macedonia in 2012 and the M.S. degree in telecommunication engineering from the Politecnico di Milano, Milan, Italy in 2016. He is currently pursuing the Ph.D. degree in telematics engineering at University Carlos III Madrid (UC3M), Spain. His research interest includes the development of mechanisms (mainly Blockchain and Machine learning) to integrate and enhance NFV and MEC for 5G Networks in dynamic and heterogeneous environments.
This event will be conducted in English