The main objective of the project is to provide security and dependability in the management and orchestration of network and data resources in 5G Multi-access Edge Computing (MEC).
The fifth generation (5G) of cellular networks is currently consisting of a huge improvement of performance to 4G, but the future releases of 5G are going to revolutionize the wireless communications. One of the technologies that will make the 5G revolution possible is the Multi-access Edge Computing (MEC).
The MEC in 5G consists in the provision of computing and storage resources within the edge of the Radio Access Network. The 5G MEC will effectively enable low latency in the communication of the future.
Security and Dependability
One of the most critical services that 5G is meant to provide is Ultra-Reliable Low-Latency Communication (URLLC), which is relevant in use cases such as eHealth, Smart City, Industry 4.0, and automotive. If the low latency is enabled by 5G MEC, how to provide ultra reliability is still an open challenge.
For this reason, the project focuses on security and dependability, particularly with respect to the management and orchestration of 5G MEC.
Resource Allocation of Data and Network Resources
In the management and orchestration of 5G, the project focuses on the allocation of data and network resources. For jointly allocating data and network resources, the project will propose innovative algorithmic solutions based on Artificial Intelligence.
One of the use cases that can take advantage of having a secure and dependable 5G MEC is automotive. In the project, a testbed on Vehicle-to-everything (V2X) communciations will be developed to test the proposed solutions. The testbed will consist of remote-controlled small cars equipped with radio and computational devices.
- Consortium partners: University of Bucharest, University of Pisa, University of Stavanger
- UiS Researchers: Associate Professor Gianfranco Nencioni, PhD Research Fellows Prachi V. Wadatkar, Thilina Pathirana and Annisa Sarah, and postdoc Muhidul I. Khan
- Funding: Norwegian Research Council
More information on the project's external website: https://5g-modanei.ux.uis.no/