Resource scheduling in edge computing: A survey
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless
networks, the surging demand for data communications and computing calls for the …
networks, the surging demand for data communications and computing calls for the …
Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach
Abstract Unmanned Aerial Vehicle (UAV) can play an important role in wireless systems as it
can be deployed flexibly to help improve coverage and quality of communication. In this …
can be deployed flexibly to help improve coverage and quality of communication. In this …
Mobility-aware joint task scheduling and resource allocation for cooperative mobile edge computing
Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency
services by enabling computation offloading at the network edge. Nevertheless, human …
services by enabling computation offloading at the network edge. Nevertheless, human …
Latency minimization for D2D-enabled partial computation offloading in mobile edge computing
We consider Device-to-Device (D2D)-enabled mobile edge computing offloading scenario,
where a device can partially offload its computation task to the edge server or exploit the …
where a device can partially offload its computation task to the edge server or exploit the …
Service-oriented energy-latency tradeoff for IoT task partial offloading in MEC-enhanced multi-RAT networks
The development of the 5G network is envisioned to offer various types of services like
virtual reality/augmented reality and autonomous vehicles applications with low-latency …
virtual reality/augmented reality and autonomous vehicles applications with low-latency …
Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments
Abstract Offloading Internet of Things (IoT) tasks to the cloud for further processing might not
always lead to an optimal execution time, particularly in situations such as resource …
always lead to an optimal execution time, particularly in situations such as resource …
Federated deep reinforcement learning-based task offloading and resource allocation for smart cities in a mobile edge network
X Chen, G Liu - Sensors, 2022 - mdpi.com
Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent
manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to …
manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to …
5G multi-RAT URLLC and eMBB dynamic task offloading with MEC resource allocation using distributed deep reinforcement learning
In this article, a deep reinforcement learning (DRL) control scheme is proposed to satisfy the
strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication …
strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication …
[图书][B] Mobile edge computing
Y Zhang - 2022 - library.oapen.org
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile
Edge Computing (MEC), which is a very promising technology for achieving intelligence in …
Edge Computing (MEC), which is a very promising technology for achieving intelligence in …
Reinforcement learning for joint optimization of communication and computation in vehicular networks
Y Cui, L Du, H Wang, D Wu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Ultra reliability and low latency communications (URLLC) are considered as one of the most
important use cases for computation tasks in the Internet of Vehicles (IoV) edge computing …
important use cases for computation tasks in the Internet of Vehicles (IoV) edge computing …