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 …
Outlier detection: Methods, models, and classification
A Boukerche, L Zheng, O Alfandi - ACM Computing Surveys (CSUR), 2020 - dl.acm.org
Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …
to the design of efficient outlier detection techniques while taking into consideration …
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 …
A survey on task offloading in multi-access edge computing
With the advent of new technologies in both hardware and software, we are in the need of a
new type of application that requires huge computation power and minimal delay …
new type of application that requires huge computation power and minimal delay …
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 …
Computation offloading in mobile edge computing networks: A survey
Computation offloading is one of the key technologies in Mobile Edge Computing (MEC),
which makes up for the deficiencies of mobile devices in terms of storage resource …
which makes up for the deficiencies of mobile devices in terms of storage resource …
Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things
Nowadays, driven by the rapid development of smart mobile equipments and 5G network
technologies, the application scenarios of Internet of Things (IoT) technology are becoming …
technologies, the application scenarios of Internet of Things (IoT) technology are becoming …
Multiagent deep reinforcement learning for vehicular computation offloading in IoT
X Zhu, Y Luo, A Liu, MZA Bhuiyan… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable
environment for users. Various emerging vehicular applications using artificial intelligence …
environment for users. Various emerging vehicular applications using artificial intelligence …
NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of Things
Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key
approaches for enabling computation-intensive yet delay-sensitive services in future …
approaches for enabling computation-intensive yet delay-sensitive services in future …
A cloud–MEC collaborative task offloading scheme with service orchestration
M Huang, W Liu, T Wang, A Liu… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
Billions of devices are connected to the Internet of Things (IoT). These devices generate a
large volume of data, which poses an enormous burden on conventional networking …
large volume of data, which poses an enormous burden on conventional networking …