Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computin...

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Vydané v:	Journal of cloud computing : advances, systems and applications Ročník 10; číslo 1; s. 1 - 17
Hlavní autori:	Lin, Bing, Lin, Kai, Lin, Changhang, Lu, Yu, Huang, Ziqing, Chen, Xinwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 08.06.2021 Springer Nature B.V SpringerOpen
Predmet:	Algorithms Computation offloading Computer Communication Networks Computer Science Computer System Implementation Computer Systems Organization and Communication Networks Connected and autonomous vehicle Deep learning Edge computing Edge-cloud computing cooperation for task offloading in internet-of-things Electric vehicles Energy consumption Failure rates Fatigue limit Information Systems Applications (incl.Internet) Machine learning Markov processes Offloading failure Reinforcement learning Simulated annealing Software Engineering/Programming and Operating Systems Special Purpose and Application-Based Systems Traffic safety Vehicle safety Connected and autonomous vehicle Energy consumption Computation offloading Reinforcement learning Simulated annealing Mobility Offloading failure
ISSN:	2192-113X, 2192-113X
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Abstract	Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.
AbstractList	Abstract Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading. Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.
ArticleNumber	33
Author	Chen, Xinwei Lin, Changhang Lin, Bing Huang, Ziqing Lu, Yu Lin, Kai
Author_xml	– sequence: 1 givenname: Bing surname: Lin fullname: Lin, Bing organization: College of Physics and Energy, Fujian Normal University, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Provincial Collaborative Innovation Center for Optoelectronic Semiconductors and Efficient Devices, Engineering Research Center of Big Data Application in Private Health Medicine, Putian University – sequence: 2 givenname: Kai surname: Lin fullname: Lin, Kai organization: College of Physics and Energy, Fujian Normal University, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering – sequence: 3 givenname: Changhang orcidid: 0000-0002-2477-0988 surname: Lin fullname: Lin, Changhang email: linchanghang@139.com organization: The School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University – sequence: 4 givenname: Yu surname: Lu fullname: Lu, Yu email: fzluyu@163.com organization: Concord University College, Fujian Normal University – sequence: 5 givenname: Ziqing surname: Huang fullname: Huang, Ziqing organization: College of Physics and Energy, Fujian Normal University, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials, Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering – sequence: 6 givenname: Xinwei surname: Chen fullname: Chen, Xinwei organization: Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Minjiang University
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Snippet	Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle... Abstract Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric...
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SubjectTerms	Algorithms Computation offloading Computer Communication Networks Computer Science Computer System Implementation Computer Systems Organization and Communication Networks Connected and autonomous vehicle Deep learning Edge computing Edge-cloud computing cooperation for task offloading in internet-of-things Electric vehicles Energy consumption Failure rates Fatigue limit Information Systems Applications (incl.Internet) Machine learning Markov processes Offloading failure Reinforcement learning Simulated annealing Software Engineering/Programming and Operating Systems Special Purpose and Application-Based Systems Traffic safety Vehicle safety
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Title	Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing
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