Task Offloading Strategy of Internet of Vehicles Based on Stackelberg Game

Shuo Xiao, Shengzhi Wang, Zhenzhen Huang, Tianyu Wang, Wei Chen, Guopeng Zhang
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引用次数: 1

Abstract

Moving vehicles generate a large amount of sensor data every second. To ensure automatic driving in a complex driving environment, it needs to fulfill a large amount of data transmission, storage, and processing in a short time. Real-time perception of traffic, target characteristics, and traffic density are important to achieve safe driving and a stable driving experience. However, it is very difficult to adjust the pricing strategy according to the actual demand of the network. In order to analyze the interaction between task vehicle and service vehicle, the Stackelberg game model is introduced. Considering the communication model, calculation model, optimization objectives, and delay constraints, this paper constructs the utility function of service vehicle and task vehicle based on the Stackelberg game model. Based on the utility function, we can obtain the optimal price strategy of service vehicles and the optimal purchase strategy of task vehicles.
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基于Stackelberg博弈的车联网任务卸载策略
移动的车辆每秒都会产生大量的传感器数据。为了保证复杂驾驶环境下的自动驾驶,需要在短时间内完成大量的数据传输、存储和处理。实时感知交通、目标特征和交通密度对于实现安全驾驶和稳定的驾驶体验至关重要。然而,根据网络的实际需求来调整定价策略是非常困难的。为了分析任务车辆与服务车辆之间的相互作用,引入了Stackelberg博弈模型。考虑通信模型、计算模型、优化目标和时延约束,基于Stackelberg博弈模型构建了服务车和任务车的效用函数。基于效用函数,可以得到服务车的最优价格策略和任务车的最优购买策略。
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