A Two-Way Auction Approach Toward Data Quality Incentive Mechanisms for Mobile Crowdsensing

With the rapid growth of smart devices, mobile crowdsensing is becoming one of the most important and attractive paradigms to acquire information from physical environments. Low-quality data, a notorious but widely found issue, degrades the availability and preciseness of sensing services, especially for these complex sensing task scenarios. However, few existing incentive mechanisms frequently ignore the issue of data quality. In this paper, we define user reputation and user task preferences in a new perspective, while predicting the number of users likely to upload high-quality data by combining Poisson distribution. Then, the maximum expectation algorithm is employed to evaluate the parameter values of the Poisson distribution. Subsequently, a two-way auction mechanism is proposed, which encourages users to participate in the sensing task and improves the match between tasks and users. We adopt the number of high-quality data that the user may upload as a factor in the user’s offer to maximize the quality of data received by the platform. The analysis based on the model lays a theoretical foundation on the incentive process of mobile crowdsensing considering data quality. The evaluation results show that our mechanism outperforms other existing techniques, in terms of robustness and efficiency.