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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Veröffentlicht in:	Journal of cloud computing : advances, systems and applications Jg. 10; H. 1; S. 1 - 17
Hauptverfasser:	Lin, Bing, Lin, Kai, Lin, Changhang, Lu, Yu, Huang, Ziqing, Chen, Xinwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 08.06.2021 Springer Nature B.V SpringerOpen
Schlagworte:	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
Online-Zugang:	Volltext
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