Data driven service orchestration for vehicular networks

A Dalgkitsis, PV Mekikis… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
As technology progresses, cars can not only be considered as a transportation medium but
also as an intelligent part of the cellular network that generates highly valuable data and …

Deep reinforcement learning-based dynamic service migration in vehicular networks

Y Peng, L Liu, Y Zhou, J Shi, J Li - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
Mobile edge computing (MEC)-enabled vehicular networks can improve the quality of
service (QoS) of vehicular networks, such as the round-trip time (RTT) and transmission …

5G vehicular network resource management for improving radio access through machine learning

SK Tayyaba, HA Khattak, A Almogren, MA Shah… - IEEE …, 2020 - ieeexplore.ieee.org
The current cellular technology and vehicular networks cannot satisfy the mighty strides of
vehicular network demands. Resource management has become a complex and …

Mobility management in 5G-enabled vehicular networks: Models, protocols, and classification

N Aljeri, A Boukerche - ACM Computing Surveys (CSUR), 2020 - dl.acm.org
Over the past few years, the next generation of vehicular networks is envisioned to play an
essential part in autonomous driving, traffic management, and infotainment applications. The …

Pre-migration of vehicle to network services based on priority in mobile edge computing

X Yu, M Guan, M Liao, X Fan - IEEE Access, 2018 - ieeexplore.ieee.org
In 5G mobile networks, the convergence of cloud computing and communication leads to
mobile edge computing, benefiting vehicular networks. However, the advent of a wide …

A deep reinforcement learning approach for service migration in mec-enabled vehicular networks

A Abouaomar, Z Mlika, A Filali… - 2021 IEEE 46th …, 2021 - ieeexplore.ieee.org
Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular
network. Due to the vehicles mobility, their requested services (eg, infotainment services) …

A federated deep learning empowered resource management method to optimize 5G and 6G quality of services (QoS)

H Alsulami, SH Serbaya… - Wireless …, 2022 - Wiley Online Library
The quality of service (QoS) in 5G/6G communication enormously depends upon the
mobility and agility of the network architecture. An increase in the possible uses of 5G …

Service migration across edge devices in 6G-enabled Internet of Vehicles networks

X Xu, L Yao, M Bilal, S Wan, F Dai… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet of Vehicles (IoV) environment consists of a number of latency-critical and data-
intensive application (eg, real-time video analytics). In this article, we posit the potential of …

Client-based intelligence for resource efficient vehicular big data transfer in future 6G networks

B Sliwa, R Adam, C Wietfeld - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
Vehicular big data is anticipated to become the “new oil” of the automotive industry which
fuels the development of novel crowdsensing-enabled services. However, the tremendous …

Leveraging mobile edge computing to improve vehicular communications

N Slamnik-Kriještorac, HCC de Resende… - 2020 IEEE 17th …, 2020 - ieeexplore.ieee.org
Due to the varying conditions in traffic and resource availability in networks nowadays,
maintaining continuity of network service and satisfying QoS and QoE requirements became …