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A resource allocation strategy for internet of vehicles using reinforcement learning in edge computing environment


Because the traditional computing model can no longer meet the particularity of Internet of Vehicles tasks, aiming at its characteristics of high bandwidth, low latency and high reliability, this paper proposes a resource allocation strategy for Internet of Vehicles using reinforcement learning in edge cloud computing environment. First, a multi-layer resource allocation model for Internet of Vehicles is proposed, which uses the cooperation mode of edge cloud computing servers and roadside units to dynamically coordinate edge computing and content caching. Then, based on the construction of communication model, calculation model and cache model, make full use of idle resources in Internet of Vehicles to minimize network delay under the condition of limited energy consumption. Finally, the optimization goal is solved by two-layer deep Q network model, and the best resource allocation plan is obtained. The simulation results based on the Internet of Vehicles model show that the computational energy consumption and system delay of proposed strategy do not exceed 400 J and 600 ms, respectively. Besides, the overall effect of resource allocation is better than other comparison strategies and it has certain application prospects.

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Majority of work, such as methodology, software, conceptualization, validation, investigation, data curation, writing original draft, writing review and editing, visualization, project administration was completed by YL. QT and ZL participate in research work include methodology, writing review and editing, conceptualization, investigation, validation.

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Correspondence to Yihong Li.

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Li, Y., Liu, Z. & Tao, Q. A resource allocation strategy for internet of vehicles using reinforcement learning in edge computing environment. Soft Comput 27, 3999–4009 (2023).

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  • Edge cloud computing
  • Reinforcement learning
  • Internet of vehicles
  • Resource allocation strategy
  • Double deep Q network model
  • Network delay
  • Computing energy consumption