面向智慧城市的多精度群智感知定价机制研究

Pricing Mechanism Research for Smart-City Oriented Multi-Precision Crowdsensing

本文将在面向智慧城市的多精度群智感知背景下,设计基于Stackelberg博弈模型的定价激励机制来解决这三方面问题。首先在完全博弈信息条件下确立最优定价的存在条件,然后在用户感知成本不确定的情况下,设计了基于Q学习的动态定价算法SPA。在多个场景下的仿真结果验证了算法具有很好的用户适应性、成本节约性和感知安全性。

This paper first establish the existence condition of the best pricing under the condition of complete game information. Then, under the condition of unknown users'sensing cost,dynamic pricing algorithms named SPA and DSSPA are designed based on Q-learning. Simulation results under different conditions confirm that these algorithms have good user group adaptability,cost awareness and sensing safety.