Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks
Technological advancements of urban informatics and vehicular intelligence have enabled
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-based Deep Reinforcement Learning Approach
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach
The developments of connected vehicles are heavily influenced by information and
communications technologies, which have fueled a plethora of innovations in various areas …
communications technologies, which have fueled a plethora of innovations in various areas …
Deep reinforcement learning for shared offloading strategy in vehicle edge computing
X Peng, Z Han, W Xie, C Yu, P Zhu, J Xiao… - IEEE Systems …, 2022 - ieeexplore.ieee.org
Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by
offloading computing tasks from vehicle terminals to edge servers. However, offloading of …
offloading computing tasks from vehicle terminals to edge servers. However, offloading of …
Mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning in internet of vehicles
J Wang, L Wang - Journal of Ambient Intelligence and Humanized …, 2021 - Springer
Mobile edge computing has been deeply integrated with internet of vehicles (IoV) due to its
efficient computing capabilities close to devices. However, the inefficiency of storage and …
efficient computing capabilities close to devices. However, the inefficiency of storage and …
Deep Reinforcement Learning Based Distributed Computation Offloading in Vehicular Edge Computing Networks
L Geng, H Zhao, J Wang, A Kaushik… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Vehicular edge computing has emerged as a promising paradigm by offloading computation-
intensive latency-sensitive tasks to mobile-edge computing (MEC) servers. However, it is …
intensive latency-sensitive tasks to mobile-edge computing (MEC) servers. However, it is …
Machine learning-based workload orchestrator for vehicular edge computing
The Internet of Vehicles (IoV) vision encompasses a wide range of novel intelligent highway
scenarios that rely on vehicles with an ever-increasing degree of autonomy and the prospect …
scenarios that rely on vehicles with an ever-increasing degree of autonomy and the prospect …
Toward reliable dnn-based task partitioning and offloading in vehicular edge computing
Modern vehicles have become typical consumer electronics with the development of
sensing, transmission, and computation technologies. The emerging intelligent …
sensing, transmission, and computation technologies. The emerging intelligent …
Task offloading in vehicular edge computing networks: A load-balancing solution
Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-
sensitive vehicular applications such as automatic driving, auto navigation. Note that …
sensitive vehicular applications such as automatic driving, auto navigation. Note that …
Extensive edge intelligence for future vehicular networks in 6G
The 6th generation mobile network (6G) is expected to achieve a fully connected world. As a
key enabling technology in 6G, edge intelligence (EI) combines artificial intelligence (AI) …
key enabling technology in 6G, edge intelligence (EI) combines artificial intelligence (AI) …