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

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

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

Content Provider	IET Digital Library
Author	Tan, Hai Zhong Zhu, Limin
Abstract	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 deal with delay-sensitive service requests in the Internet of vehicles. The weighted total cost combines delay and energy consumption is taken as the optimisation goal. First a reinforcement learning algorithm Q-learning based on Markov decision process is proposed to solve the problem for minimising weighted total cost. Furthermore, this study specifically explains the setting method of three elements for reinforcement learning-state, action and reward in the fog computing system. Then for reducing the scale of problems and improving efficiency, the authors set up a pre-classification process before reinforcement learning to control the possible values of actions. However, considering that as the number of vehicles in system increases, Q-learning method based on recorded Q values may fall into a dimensional disaster. Therefore, the authors propose a deep reinforcement learning method, deep Q-learning network (DQN), which combines deep learning and Q-learning. Experimental results show that the proposed method has advantages.
Starting Page	1080
Ending Page	1087
Page Count	8
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 11, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0134
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-08-05
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Cellular Base Stations Cellular Radio Cloud Computing Computing Offloading Strategy Computing Process Data Requests Decision Theory Deep Q-learning Network Deep Reinforcement Learning Method Delay-sensitive Service Requests DQN Energy Consumption Fog Computing Function Fog Computing System Fog Server Game Theory Intelligent Transport System Intelligent Vehicle Internet Software Knowledge Engineering Technique Learning in AI Long Term Evolution Long-term Evolution-vehicle LTE-V-fog Network Markov Decision Process Markov Processes Minimisation Mobile Computing Mobile Radio System Network Congestion Optimisation Optimisation Technique Pervasive Computing Preclassification Process Q-learning Method Traffic Engineering Computing Ubiquitous Vehicle Fog Computing Weighted Total Cost Minimisation
Content Type	Text
Resource Type	Article

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