Deep reinforcement learning (DRL)-based resource management in software-defined and virtualized vehicular ad hoc networks
Vehicular ad hoc networks (VANETs) have attracted great interests from both industry and
academia. The developments of VANETs are heavily influenced by information and …
academia. The developments of VANETs are heavily influenced by information and …
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 …
Resource allocation in software-defined and information-centric vehicular networks with mobile edge computing
Recent advances in networking, caching and computing have significant impacts on the
developments of vehicular networks. Nevertheless, these important enabling technologies …
developments of vehicular networks. Nevertheless, these important enabling technologies …
Meta-hierarchical reinforcement learning (MHRL)-based dynamic resource allocation for dynamic vehicular networks
With the rapid development of vehicular networks, there is an increasing demand for
extensive networking, computting, and caching resources. How to allocate multiple …
extensive networking, computting, and caching resources. How to allocate multiple …
A Hybrid Deep Reinforcement Learning Approach for Jointly Optimizing Offloading and Resource Management in Vehicular Networks
CL Chen, B Bhargava, V Aggarwal… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Satisfying the quality of service of data-intensive autonomous driving applications has
become challenging. In this work, we propose a novel methodology that optimizes …
become challenging. In this work, we propose a novel methodology that optimizes …
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 …
Reinforcement learning for resource provisioning in the vehicular cloud
MA Salahuddin, A Al-Fuqaha… - IEEE Wireless …, 2016 - ieeexplore.ieee.org
This article presents a concise view of vehicular clouds that incorporates various vehicular
cloud models that have been proposed to date. Essentially, they all extend the traditional …
cloud models that have been proposed to date. Essentially, they all extend the traditional …
Scheduling the operation of a connected vehicular network using deep reinforcement learning
Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development …
Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks
In this paper, we study joint allocation of the spectrum, computing, and storing resources in a
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …
Resource allocation in MEC-enabled vehicular networks: A deep reinforcement learning approach
Mobile edge computing (MEC) is a promising technique to liberate mobile vehicles from
increasingly intensive computation workloads and improve the quality of computation …
increasingly intensive computation workloads and improve the quality of computation …
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