Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing
Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …
Deep reinforcement learning for collaborative edge computing in vehicular networks
Mobile edge computing (MEC) is a promising technology to support mission-critical
vehicular applications, such as intelligent path planning and safety applications. In this …
vehicular applications, such as intelligent path planning and safety applications. In this …
Collaborative data scheduling for vehicular edge computing via deep reinforcement learning
With the development of autonomous driving, the surging demand for data communications
as well as computation offloading from connected and automated vehicles can be expected …
as well as computation offloading from connected and automated vehicles can be expected …
Joint service caching and computation offloading scheme based on deep reinforcement learning in vehicular edge computing systems
Vehicular edge computing (VEC) is a new computing paradigm that enhances vehicular
performance by introducing both computation offloading and service caching, to resource …
performance by introducing both computation offloading and service caching, to resource …
Task offloading in vehicular edge computing networks via deep reinforcement learning
Given the rapid increase of various applications in vehicular networks, it is crucial to
consider a flexible architecture to improve the Quality of Service (QoS). Utilizing Multi-access …
consider a flexible architecture to improve the Quality of Service (QoS). Utilizing Multi-access …
Learning based energy efficient task offloading for vehicular collaborative edge computing
Extensive delay-sensitive and computation-intensive tasks are involved in emerging
vehicular applications. These tasks can hardly be all processed by the resource constrained …
vehicular applications. These tasks can hardly be all processed by the resource constrained …
BARGAIN-MATCH: A game theoretical approach for resource allocation and task offloading in vehicular edge computing networks
Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular
networks (VNs) by deploying the cloud computing resources at the edge of the VNs …
networks (VNs) by deploying the cloud computing resources at the edge of the VNs …
Asynchronous deep reinforcement learning for data-driven task offloading in MEC-empowered vehicular networks
Mobile edge computing (MEC) has been an effective paradigm to support real-time
computation-intensive vehicular applications. However, due to highly dynamic vehicular …
computation-intensive vehicular applications. However, due to highly dynamic vehicular …
Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks
The promise of low latency connectivity and efficient bandwidth utilization has driven the
recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC) …
recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC) …
Deep reinforcement learning for vehicular edge computing: An intelligent offloading system
The development of smart vehicles brings drivers and passengers a comfortable and safe
environment. Various emerging applications are promising to enrich users' traveling …
environment. Various emerging applications are promising to enrich users' traveling …
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