On Designing Data Quality-Aware Truth Estimation and Surplus Sharing Method for Mobile Crowdsensing

Abstract

Mobile crowdsensing has become a novel and promising paradigm in collecting, analyzing, and exploiting massive amounts of data. However, the issue of data quality has not been carefully addressed. Low quality data contributions undermine the effectiveness and prospects of crowdsensing, and thus motivate the need for approaches to guarantee the high quality of the contributed data. In this paper, we integrate quality estimation and monetary incentive, and propose a quality-based truth estimation and surplus sharing method for crowdsensing. Specifically, we design an unsupervised learning approach to quantify the users' data qualities and long-term reputations, and exploit an outlier detection technique to filter out anomalous data items. Furthermore, we model the process of surplus sharing as a co-operative game, and propose a Shapley value-based method to determine each user's payment. We have conducted a real crowdsensing experiment and a large-scale simulation to evaluate our method. The evaluation results show that our approach achieves good performance in terms of both quality estimation and surplus sharing.