Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource
management for expected long-term performance optimization in a Manhattan grid vehicle …
management for expected long-term performance optimization in a Manhattan grid vehicle …
Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning
X Zhao, M Liu, M Li - Ad Hoc Networks, 2023 - Elsevier
Driven by the construction of smart cities, networks and communication technologies are
gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such …
gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such …
Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network
This paper presents the first study on scheduling for cooperative data dissemination in a
hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication …
hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication …
Latency-energy tradeoff in connected autonomous vehicles: A deep reinforcement learning scheme
Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing
closer to user equipment (UEs) at the edge of the access network by delivering various …
closer to user equipment (UEs) at the edge of the access network by delivering various …
Deep reinforcement learning-based adaptive computation offloading for MEC in heterogeneous vehicular networks
H Ke, J Wang, L Deng, Y Ge… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The vehicular network needs efficient and reliable data communication technology to
maintain low latency. It is very challenging to minimize the energy consumption and data …
maintain low latency. It is very challenging to minimize the energy consumption and data …
Vehicular task offloading via heat-aware MEC cooperation using game-theoretic method
Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular
task offloading. Due to the limited computing resource of individual MEC servers, it faces …
task offloading. Due to the limited computing resource of individual MEC servers, it faces …
Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning
RQ Hu - IEEE Transactions on Vehicular Technology, 2018 - ieeexplore.ieee.org
This paper studies the joint communication, caching and computing design problem for
achieving the operational excellence and the cost efficiency of the vehicular networks …
achieving the operational excellence and the cost efficiency of the vehicular networks …
DeepReserve: Dynamic edge server reservation for connected vehicles with deep reinforcement learning
Edge computing is promising to provide computational resources for connected vehicles.
Resource demands for edge servers vary due to vehicle mobility. It is then challenging to …
Resource demands for edge servers vary due to vehicle mobility. It is then challenging to …
EPtask: Deep reinforcement learning based energy-efficient and priority-aware task scheduling for dynamic vehicular edge computing
The increasing complexity of vehicles has led to a growing demand for in-vehicle services
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
Proactive scheduling and resource management for connected autonomous vehicles: a data science perspective
S Malik, HA Khattak, Z Ameer, U Shoaib… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
Carpooling and the ride-sharing idea are currently resolving many issues faced by modern
societies. The issues regarding the overuse of oil, traffic jams, inefficient use of time …
societies. The issues regarding the overuse of oil, traffic jams, inefficient use of time …