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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing
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PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing

机译：PUEGM：一种基于发布者-用户进化博弈模型的移动人群感知用户收益选择方法

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摘要

Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue.
机译：移动人群感知（MCS）是一种使用社交资源实时解决高精度环境意识问题的方法。发布者希望以相对较低的成本收集尽可能多的感知数据，而用户希望以低成本获得更多收入。低质量的数据将降低MCS的效率并导致收入损失。但是，现有的工作缺乏在确保数据质量的前提下选择用户收益的研究。在本文中，我们提出了一种基于非合作进化博弈理论的发布者－用户进化博弈模型（PUEGM）和一种收益选择方法来解决进化稳定均衡问题。首先，将用户收入的选择建模为发布者-用户进化游戏模型。其次，基于错误消除决策理论，我们在PUEGM中结合了数据质量评估算法，旨在消除低质量的数据并提高用户数据的整体质量。最后，从演化稳定性策略（ESS）解决方案和稳定性分析中获得了不同条件下的最佳用户收益策略。为了验证所提出解决方案的效率，使用了一些实际数据集进行了广泛的实验。实验结果表明，该方法具有较高的数据质量评估精度和合理的用户收益选择能力。

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