UAV assistance paradigm: State-of-the-art in applications and challenges
Abstract Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to
be used in industries and various sectors of human life to provide a wide range of …
be used in industries and various sectors of human life to provide a wide range of …
A survey on computation offloading modeling for edge computing
H Lin, S Zeadally, Z Chen, H Labiod, L Wang - Journal of Network and …, 2020 - Elsevier
As a promising technology, edge computing extends computation, communication, and
storage facilities toward the edge of a network. This new computing paradigm opens up new …
storage facilities toward the edge of a network. This new computing paradigm opens up new …
MEC-assisted immersive VR video streaming over terahertz wireless networks: A deep reinforcement learning approach
J Du, FR Yu, G Lu, J Wang, J Jiang… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced
immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user …
immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user …
Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization
In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing
(MEC) with the objective to optimize computation offloading with minimum UAV energy …
(MEC) with the objective to optimize computation offloading with minimum UAV energy …
Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN
In this article, we investigate a computing task scheduling problem in space-air-ground
integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the …
integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the …
3D UAV trajectory design and frequency band allocation for energy-efficient and fair communication: A deep reinforcement learning approach
Unmanned Aerial Vehicle (UAV)-assisted communication has drawn increasing attention
recently. In this paper, we investigate 3D UAV trajectory design and band allocation problem …
recently. In this paper, we investigate 3D UAV trajectory design and band allocation problem …
Completion time and energy optimization in the UAV-enabled mobile-edge computing system
C Zhan, H Hu, X Sui, Z Liu… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two
important design aspects in UAV-enabled applications. In this article, we consider a UAV …
important design aspects in UAV-enabled applications. In this article, we consider a UAV …
AI-assisted network-slicing based next-generation wireless networks
The integration of communications with different scales, diverse radio access technologies,
and various network resources renders next-generation wireless networks (NGWNs) highly …
and various network resources renders next-generation wireless networks (NGWNs) highly …
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 …
Satellite-terrestrial integrated edge computing networks: Architecture, challenges, and open issues
STN has been considered a novel network architecture to accommodate a variety of
services and applications in future networks. Being a promising paradigm, MEC has been …
services and applications in future networks. Being a promising paradigm, MEC has been …