Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

In order to solve the problem of network congestion caused by a large number of data requests generated by intelligent vehicles in LTE-V network, a brand-new fog server with fog computing function is deployed on both the cellular base stations and vehicles, and an LTE-V-fog network is constructed to...

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Bibliographic Details
Main Authors: HaiZhong Tan, Limin Zhu
Format: Article
Language:English
Published: Wiley 2020-11-01
Series:The Journal of Engineering
Subjects:
learning (artificial intelligence)
mobile computing
minimisation
long term evolution
cloud computing
markov processes
cellular radio
decision theory
intelligent transportation systems
computing offloading strategy
vehicle fog computing
long-term evolution-vehicle
network congestion
dqn
weighted total cost minimisation
fog server
preclassification process
deep q-learning network
deep reinforcement learning method
q-learning method
fog computing system
markov decision process
optimisation
computing process
energy consumption
delay-sensitive service requests
lte-v-fog network
cellular base stations
fog computing function
intelligent vehicles
data requests
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0134

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https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0134

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