FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3166236194> ?p ?o ?g. }

• W3166236194 abstract "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." @default.
• W3166236194 created "2021-06-22" @default.
• W3166236194 creator A5009824003 @default.
• W3166236194 creator A5032308763 @default.
• W3166236194 creator A5040339760 @default.
• W3166236194 creator A5044799470 @default.
• W3166236194 creator A5079372376 @default.
• W3166236194 creator A5083015892 @default.
• W3166236194 date "2021-06-08" @default.
• W3166236194 modified "2023-09-26" @default.
• W3166236194 title "Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing" @default.
• W3166236194 cites W2009751454 @default.
• W3166236194 cites W2244651199 @default.
• W3166236194 cites W2320180085 @default.
• W3166236194 cites W2626588362 @default.
• W3166236194 cites W2747537200 @default.
• W3166236194 cites W2783495847 @default.
• W3166236194 cites W2798350475 @default.
• W3166236194 cites W2893733132 @default.
• W3166236194 cites W2895973886 @default.
• W3166236194 cites W2896167869 @default.
• W3166236194 cites W2900804979 @default.
• W3166236194 cites W2903362341 @default.
• W3166236194 cites W2905978464 @default.
• W3166236194 cites W2954039338 @default.
• W3166236194 cites W2963076009 @default.
• W3166236194 cites W2963872309 @default.
• W3166236194 cites W2964335916 @default.
• W3166236194 cites W2972128755 @default.
• W3166236194 cites W2980360843 @default.
• W3166236194 cites W2983555454 @default.
• W3166236194 cites W3003623928 @default.
• W3166236194 cites W3004718830 @default.
• W3166236194 cites W3005889076 @default.
• W3166236194 cites W3010178570 @default.
• W3166236194 cites W3010723141 @default.
• W3166236194 cites W3010993393 @default.
• W3166236194 cites W3013158795 @default.
• W3166236194 cites W3013767198 @default.
• W3166236194 cites W3013810395 @default.
• W3166236194 cites W3023720662 @default.
• W3166236194 cites W3024155406 @default.
• W3166236194 cites W3026449395 @default.
• W3166236194 cites W3038782910 @default.
• W3166236194 cites W3045049179 @default.
• W3166236194 cites W3045259134 @default.
• W3166236194 cites W3089786558 @default.
• W3166236194 cites W3097472721 @default.
• W3166236194 cites W3109422073 @default.
• W3166236194 doi "https://doi.org/10.1186/s13677-021-00246-6" @default.
• W3166236194 hasPublicationYear "2021" @default.
• W3166236194 type Work @default.
• W3166236194 sameAs 3166236194 @default.
• W3166236194 citedByCount "9" @default.
• W3166236194 countsByYear W31662361942022 @default.
• W3166236194 countsByYear W31662361942023 @default.
• W3166236194 crossrefType "journal-article" @default.
• W3166236194 hasAuthorship W3166236194A5009824003 @default.
• W3166236194 hasAuthorship W3166236194A5032308763 @default.
• W3166236194 hasAuthorship W3166236194A5040339760 @default.
• W3166236194 hasAuthorship W3166236194A5044799470 @default.
• W3166236194 hasAuthorship W3166236194A5079372376 @default.
• W3166236194 hasAuthorship W3166236194A5083015892 @default.
• W3166236194 hasBestOaLocation W31662361941 @default.
• W3166236194 hasConcept C105795698 @default.
• W3166236194 hasConcept C106189395 @default.
• W3166236194 hasConcept C111919701 @default.
• W3166236194 hasConcept C11413529 @default.
• W3166236194 hasConcept C120314980 @default.
• W3166236194 hasConcept C153083717 @default.
• W3166236194 hasConcept C154945302 @default.
• W3166236194 hasConcept C159886148 @default.
• W3166236194 hasConcept C162307627 @default.
• W3166236194 hasConcept C18903297 @default.
• W3166236194 hasConcept C2776061582 @default.
• W3166236194 hasConcept C2778456923 @default.
• W3166236194 hasConcept C2780165032 @default.
• W3166236194 hasConcept C2781041963 @default.
• W3166236194 hasConcept C2781055072 @default.
• W3166236194 hasConcept C33923547 @default.
• W3166236194 hasConcept C41008148 @default.
• W3166236194 hasConcept C45374587 @default.
• W3166236194 hasConcept C79974875 @default.
• W3166236194 hasConcept C86803240 @default.
• W3166236194 hasConcept C97541855 @default.
• W3166236194 hasConceptScore W3166236194C105795698 @default.
• W3166236194 hasConceptScore W3166236194C106189395 @default.
• W3166236194 hasConceptScore W3166236194C111919701 @default.
• W3166236194 hasConceptScore W3166236194C11413529 @default.
• W3166236194 hasConceptScore W3166236194C120314980 @default.
• W3166236194 hasConceptScore W3166236194C153083717 @default.
• W3166236194 hasConceptScore W3166236194C154945302 @default.
• W3166236194 hasConceptScore W3166236194C159886148 @default.
• W3166236194 hasConceptScore W3166236194C162307627 @default.
• W3166236194 hasConceptScore W3166236194C18903297 @default.
• W3166236194 hasConceptScore W3166236194C2776061582 @default.
• W3166236194 hasConceptScore W3166236194C2778456923 @default.
• W3166236194 hasConceptScore W3166236194C2780165032 @default.
• W3166236194 hasConceptScore W3166236194C2781041963 @default.
• W3166236194 hasConceptScore W3166236194C2781055072 @default.

• next