RT Journal Article
T1 AI-driven, Context-Aware Profiling for 5G and Beyond Networks
A1 Koursioumpas, Nikolaos
A1 Barmpounakis, Sokratis
A1 Stavrakakis, Ioannis
A1 Alonistioti, Nancy
AB In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the -often scarce -computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework.
PB IEEE
SN 1932-4537 (Electronic)
YR 2022
FD 2022-06
LK https://hdl.handle.net/10016/34179
UL https://hdl.handle.net/10016/34179
LA eng
NO This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709).
DS e-Archivo
RD 1 sept. 2024