Walrasian Equilibrium-Based Pricing Mechanism for Health-Data Crowdsensing Under Information Asymmetry
Walrasian Equilibrium-Based Pricing Mechanism for Health-Data Crowdsensing Under Information Asymmetry
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DOI：
10.1109/tcss.2022.3171566
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发表时间：
2023-06
影响因子：
5
通讯作者：
Xinxin Guo;N. Kong;Haiyan Wang
中科院分区：
文献类型：
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作者：
Xinxin Guo;N. Kong;Haiyan Wang
虽然以前的研究设计了激励机制来吸引公众分享他们收集的数据，但他们往往忽略了数据请求者和收集者之间的信息不对称。在现实中，感知成本信息(时间成本、电池消耗、移动设备的带宽占用等)是采集者的私人信息，数据请求者不知道。在本文中，我们使用双层优化模型对健康数据请求者和收集者之间的战略交互进行建模。考虑到大众感知市场是开放的，参与者是平等的，我们提出了一种基于瓦尔拉斯均衡的定价机制来协调健康数据请求者和收集者之间的利益冲突。具体地说，基于交换经济理论，我们将双层优化问题转化为具有供需平衡约束条件的社会福利最大化问题，然后利用对偶分解将社会福利最大化问题分解为一组可由健康数据请求者和收集者解决的子问题。我们证明了最优任务价格等于收集者健康数据产生的边际效用。为了避免获取收集者的私有信息，设计了一种分布式迭代算法来获得最优任务定价策略。此外，我们进行了计算实验来评估所提出的定价机制的性能，并分析了内在奖励、感知成本对最优任务价格的影响以及收集者的健康数据供应。
While prior studies have designed incentive mechanisms to attract the public to share their collected data, they tend to ignore information asymmetry between data requesters and collectors. In reality, the sensing costs information (time cost, battery drainage, bandwidth occupation of mobile devices, and so on) is the private information of collectors, which is unknown by the data requester. In this article, we model the strategic interactions between health-data requester and collectors using a bilevel optimization model. Considering that the crowdsensing market is open and the participants are equal, we propose a Walrasian equilibrium-based pricing mechanism to coordinate the interest conflicts between health-data requesters and collectors. Specifically, based on the exchange economic theory, we transform the bilevel optimization problem into a social welfare maximization problem with the constraint condition that the balance between supply and demand, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by health-data requesters and collectors. We prove that the optimal task price is equal to the marginal utility generated by the collector’s health data. To avoid obtaining the collector’s private information, a distributed iterative algorithm is then designed to obtain the optimal task pricing strategy. Furthermore, we conduct computational experiments to evaluate the performance of the proposed pricing mechanism and analyze the effects of intrinsic rewards, sensing costs on optimal task prices, and collectors’ health-data supplies.