摘要
随着智能汽车应用的增多,用户对车辆的计算能力提出了更高的要求。由于车辆的计算能力有限,使得其自身很难完全满足任务的需求。针对这一问题,采用车载边缘计算进行任务卸载,但任务交付会消耗通信资源,因此需要制定有效的激励机制。本文提出了一种基于Stackelberg博弈的任务卸载策略,首先设计了一种基于Stackelberg博弈的激励机制;其次推导出相应条件下的纳什均衡解,制定最优定价策略,进行任务卸载。仿真结果表明,该算法能有效地减少任务处理时延,同时最大限度的提高整体效用。
With the increase of smart car applications,users have put forward higher requirements on the computing power of vehicles.Due to the limited computing power of the vehicle,it is difficult to fully meet the needs of the task itself.In response to this problem,on-board edge computing is used to offload tasks,but task delivery consumes communication resources,so an effective incentive mechanism needs to be developed.This paper proposes a task offloading strategy based on the Stackelberg game.Firstly,an incentive mechanism based on the Stackelberg game is designed.Secondly,the Nash equilibrium solution under the corresponding conditions is derived,and the optimal price strategy is formulated to perform task offloading.This algorithm can effectively reduce the task processing delay,and at the same time maximize the overall utility.
作者
张俊
王杨
张聪聪
许闪闪
ZHANG Jun;WANG Yang;ZHANG Cong-cong;XU Shan-shan(School of Network Engineering,Wuhu Institute of Technology,Wuhu 241000,China;School of Computer and Information,Anhui Normal University,Wuhu 241000,China)
出处
《安徽师范大学学报（自然科学版）》
CAS
2020年第6期527-536,共10页
Journal of Anhui Normal University(Natural Science)
基金
国家自然科学基金项目(61871412)
安徽省自然科学基金重点项目(KJ2019A0938)
安徽省重大人文社科基金项目(SK2014ZD033)
芜湖职业技术学院项目(Kjcxpt202003,Wzykj2018A01).
