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Deep reinforcement learning based offloading decision algorithm for vehicular edge computing

Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network r...

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Autores principales: Hu, Xi, Huang, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575847/
https://www.ncbi.nlm.nih.gov/pubmed/36262145
http://dx.doi.org/10.7717/peerj-cs.1126
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author Hu, Xi
Huang, Yang
author_facet Hu, Xi
Huang, Yang
author_sort Hu, Xi
collection PubMed
description Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can’t maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG.
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spelling pubmed-95758472022-10-18 Deep reinforcement learning based offloading decision algorithm for vehicular edge computing Hu, Xi Huang, Yang PeerJ Comput Sci Autonomous Systems Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can’t maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG. PeerJ Inc. 2022-10-11 /pmc/articles/PMC9575847/ /pubmed/36262145 http://dx.doi.org/10.7717/peerj-cs.1126 Text en © 2022 Hu and Huang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Autonomous Systems
Hu, Xi
Huang, Yang
Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title_full Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title_fullStr Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title_full_unstemmed Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title_short Deep reinforcement learning based offloading decision algorithm for vehicular edge computing
title_sort deep reinforcement learning based offloading decision algorithm for vehicular edge computing
topic Autonomous Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575847/
https://www.ncbi.nlm.nih.gov/pubmed/36262145
http://dx.doi.org/10.7717/peerj-cs.1126
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AT huangyang deepreinforcementlearningbasedoffloadingdecisionalgorithmforvehicularedgecomputing