车联网中基于三方Stackelberg博弈的动态多媒体定价方案
Dynamic multimedia pricing scheme based on three-party Stackelberg game in Internet of vehicles
张海波 1王新月 1王冬宇 2刘富3
作者信息
- 1. 重庆邮电大学通信与信息工程学院,重庆 400065
- 2. 北京邮电大学人工智能学院,北京 100876
- 3. 重庆市城市照明中心,重庆 400023
- 折叠
摘要
在当前车联网的应用场景下,中继车辆数据转发的积极性低下与存储空间有限,导致用户体验质量(QoE)降低,为此提出基于三方Stackelberg博弈的动态多媒体定价方案.为了激励中继车辆参与转发多媒体内容,提出多媒体内容定价框架,其中中继车辆获得全额佣金后向路侧单元(RSU)支付部分佣金.设计基于Stackel-berg博弈的动态定价模型,根据中继车辆、用户车辆与RSU三方的存储空间利用率、内容数据大小和成本因素,建立各自的效用函数,并将其转化为三方四阶段Stackelberg定价模型.通过反向归纳法证明纳什均衡的存在,实现三方之间的动态定价以得到各自最优策略.仿真结果表明,所提方案有效解决了中继车辆存储空间过载问题,并提高了中继车辆积极性,且在提升用户QoE方面较传统方案具有优势.
Abstract
The user quality of experience(QoE)is reduced due to the low enthusiasm of the relay vehicle data forwarding and the limited storage space in the current Internet of vehicles(IoV)application scenarios.Thus,a dynamic multimedia pricing scheme based on the three-party Stackelberg game was proposed.Aiming at incentivizing relay vehicles to participate in forwarding multimedia content,a new multimedia content pricing framework was proposed,in which the relay vehicle received a full commission and then paid a partial commission to the roadside unit(RSU).A dynamic pricing model based on Stackelberg game was designed to establish a utility function,which was based on the storage space utilization,the content data size and the cost of the relay vehicle,the user vehicle and the RSU.The utility function was transformed into a three-party,four-stage Stackelberg pricing model.The existence of the Nash equilibrium solution was proved using backward induction technique,and the dynamic pricing process among the three parties was finally realized to achieve their respective optimal strategies.The simulation results showed that the proposed scheme effectively solved the problem of overloaded storage space in the relay vehicle and improved the enthusiasm of the relay vehicle,and it had advantages over the traditional scheme in improving user QoE.
关键词
车联网(IoV)/动态定价/Stackelberg博弈/QoE/反向归纳法Key words
Internet of vehicles(IoV)/dynamic pricing/Stackelberg game/QoE/backward induction tech-nique引用本文复制引用
基金项目
国家自然科学基金资助项目(62271094)
长江学者和创新团队发展计划基金资助项目(IRT16R72)
重庆市留创计划创新类资助项目(cx2020059)
出版年
2024
NETL
NSTL
万方数据