A Method of Flow Compensation Incentive based on Q-Learning for User Privacy Protection

To solve the incentive problem for MCS (Mobile Crowdsensing) users based on privacy protection, we proposed an incentive method of flow compensation for the privacy protection of users and designed a system model which combined MCS with MEC (Mobile Edge Computing). The EC (Edge Center) uploaded the perception results to the MCS diminishing MCS’s overhead. We also designed an incentive model based on Q-Learning algorithm for privacy protection of user data, which can reduce the incentive expenditure and improve users’ enthusiasm for participation. Compared with the existing incentive method based on privacy protection, our method improves the perceptual result precision, decreases MCS cloud overhead, and declines flow compensation cost.