Based on Bid and Data Quality Incentive Mechanisms for Mobile Crowd Sensing Systems

Abstract

Compared with traditional fixed-point sensors, the mobile crowd sensing (MCS) system has the advantages of simple deployment, easy maintenance of the network, low collection cost, and easy expansion, and has been widely used, but there are still problems such as quality of data collected by online users and low enthusiasm of user participation. How to motivate more users to participate in sensing tasks and provide high-quality data has become one of the hot issues in MCS research. In this paper, we propose MCS incentive mechanism named Data Quality and Bid based Incentive Mechanism (DQBIM), which selects high quality and low bid users as the winner and maximizes the benefit of the platform. The winner is selected by considering the quality of the data submitted by the user and the bid and using a reverse auction algorithm, and the winner is given a certain monetary incentive, and finally, the quality of the data submitted by the winner is updated according to the amount of qualified data submitted. DQBIM is an incentive mechanism with truthfulness, personal rationality, and high computational efficiency. Simulation experiments show that the model can effectively improve data quality, reduce system overhead, increase user participation, and achieve long-lasting incentives.