Good work deserves good pay: A quality-based surplus sharing method for participatory sensing

Participatory sensing has become a novel and promising paradigm in environmental data collection. However, the issue of data quality has not been carefully addressed. Low quality data contributions may undermine the effectiveness and prospects of participatory sensing, and thus motivates 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 surplus sharing method for participatory sensing. 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 cooperative game, and propose a Shapley value-based method to determine each user's payment. We have conducted a participatory sensing experiment, and the experiment results show that our approach achieves good performance in terms of both quality estimation and surplus sharing.