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Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing
International Journal of Distributed Sensor Networks ( IF 2.3 ) Pub Date : 2021-06-24 , DOI: 10.1177/15501477211023010
Xiaoxiao Yang 1 , Jing Zhang 1 , Jun Peng 1 , Lihong Lei 1
Affiliation  

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.



中文翻译:

声誉约束下基于Stackelberg博弈的移动众筹激励机制

鼓励一定数量的用户持续参与感知任务以在一定预算下收集高质量的感知数据是移动人群感知的新挑战。用户的历史声誉反映了他们过去在完成传感任务方面的表现,历史声誉高的用户在历史任务中表现突出。因此,本研究提出一种基于Stackelberg博弈的声誉约束激励机制算法来解决上述问题。首先,应用用户的历史信誉来选择一些可信赖的用户来收集高质量的传感数据。然后利用两阶段Stackelberg博弈分析用户在感知任务中的资源贡献水平和服务器平台的最优激励机制。通过确定用户的最优响应策略,验证了 Stackelberg 均衡的存在唯一性。最后,提出了两种用户总收益的换算方法,以保证用户收益在移动人群感知网络中的灵活应用。仿真实验表明,选择的可信用户的历史信誉高于随机选择的用户,服务器平台和用户具有良好的效用。

更新日期:2021-06-24
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