Optimization Algorithm of Incentive Mechanism of Parking Space Crowdsensing System

In the application of crowdsensing technology to the perception of parking space status, a set of incentive mechanism optimization algorithms are designed to address the problem that crowdsensing systems using traditional auction incentive mechanisms are susceptible to interference from non-platform users and thus reduce credibility. First, the user's feedback information is verified to reduce the impact of interference information on the platform; second, the user's additional rewards are adjusted through the dynamically updated reputation value to encourage users to provide more high-quality perception data. The simulation results show that the optimization model has better performance than the traditional model in terms of platform revenue and the iterative evolution of the proportion of trusted users.