Mobile Crowd Sensing Online Quality Awareness Incentive Mechanism Based on Taxation and Data Aggregation

Mobile Crowd-Sensing (MCS) has emerged as a significant approach in various domains for collecting and disseminating sensing data. However, it is a challenging issue to select appropriate participants for a sensing task. This paper introduces a Quality Awareness Incentive Mechanism based on Taxation and Data Aggregation (QIM-TDA), which can choose the reliable participants without losing the platform utility and the data quality. Firstly, we define a reputation model to measure the reliability of participant and select more reliable participants based on their reputation in order to maximize platform utility. Secondly, a truth discovery algorithm is proposed to aggregate the sensing data and ensure the data quality of MCS. Finally, a normalized taxation mechanism is discussed in order to prevent the excessive accumulation of reputation for participant and further enhance the data quality. The simulation results prove that QIM-TDA can significantly improve the data quality and task completion rate compared to some typical mechanisms.