面向车辆多址接入边缘计算网络的任务协同计算迁移策略

doi:10.11959/j.issn.2096-3750.2019.00089

Abstract

Abstract:

In order to solve the problem that traditional mobile edge computing network can’t be straightforwardly applied to the Internet of vehicles (IoV) due to high speed mobility and dynamic network topology,a vehicular edge multi-access computing network (VE-MACN) was introduced to realize collaborative computing offloading between roadside units and smart vehicles.In this context,the collaborative computation offloading was formulated as a joint multi-access model selection and task assignment problem to realize the good balance between long-term system utility,diverse needs of IoV applications and energy consumption.Considering the complex joint optimization problem,a deep reinforcement learning-based collaborative computing offloading scheme was designed to overcome the curse of dimensionality for Q-learning algorithm.The simulation results demonstrate that the feasibility and effectiveness of proposed offloading scheme.

Key words: mobile edge computing, multi-access technology, Internet of vehicles (IoV), computation offloading, deep reinforcement learning

CLC Number:

TN929.53

Guanhua QIAO,Supeng LENG,Hao LIU,Kaisheng HUANG,Fan WU. Task collaborative offloading scheme in vehicle multi-access edge computing network[J]. Chinese Journal on Internet of Things, 2019, 3(1): 51-59.

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References 20

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