Owing to the acceleration of urbanization and the rapid development of mobile Internet, mobile crowd sensing (MCS) has been recognized as a promising method to acquire massive volume of data. However, due to the massive perception data in participatory MCS system, the data privacy of mobile users and the response speed of data processing in cloud platform are hard to guarantee. Stimulating the enthusiasm of participants could be challenging at the same time. In this paper, we first propose a three-layer MCS architecture which introduces edge servers to process raw data, protects users’ privacy and improve response time. In order to maximize social welfare, we consider two-stage game in three-layer MCS architecture. Then, we formulate a Markov decision process (MDP)-based social welfare maximization model and investigate a convex optimization pricing problem in the proposed three-layer architecture. Combined with the market economy model, the problem could be considered as a Walrasian equilibrium problem according to market exchange theory. We propose a pricing approach toward incentive mechanisms based on Lagrange multiplier method, dual decomposition and subgradient iterative method. Finally, we derive the experimental data from real-world dataset and extensive simulations demonstrate the performance of our proposed method.

中文翻译：

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边缘计算中参与者移动人群感知激励机制的定价方法

由于城市化进程的加快和移动互联网的迅速发展，移动人群感知（MCS）被认为是一种获取海量数据的有前途的方法。但是，由于参与式MCS系统中的感知数据量很大，因此难以保证移动用户的数据隐私和云平台中数据处理的响应速度。同时激发参与者的热情可能会充满挑战。在本文中，我们首先提出了一个三层的MCS体系结构，该体系结构引入了边缘服务器来处理原始数据，保护用户的隐私并缩短了响应时间。为了最大化社会福利，我们考虑在三层MCS体系结构中进行两阶段博弈。然后，我们建立了一个基于马尔可夫决策过程（MDP）的社会福利最大化模型，并研究了所提出的三层体系结构中的凸优化定价问题。结合市场经济模型，根据市场交换理论，该问题可被视为瓦尔拉斯均衡问题。我们提出了一种基于拉格朗日乘数法，双重分解和次梯度迭代法的激励机制定价方法。最后，我们从真实的数据集中获得实验数据，大量的仿真证明了我们提出的方法的性能。我们提出了一种基于拉格朗日乘数法，双重分解和次梯度迭代法的激励机制定价方法。最后，我们从真实的数据集中获得实验数据，大量的仿真证明了我们提出的方法的性能。我们提出了一种基于拉格朗日乘数法，双重分解和次梯度迭代法的激励机制定价方法。最后，我们从真实的数据集中获得实验数据，大量的仿真证明了我们提出的方法的性能。