Abstract:In a participatory sensing system, since the quality of the perceived data may be affected by the participants, a reputation calculation model based on the cumulative behavior of users is proposed to help select the trustworthy users. According to the extensiveness of the user groups and the uncertainty of the core users in the perceived environment, this model uses the OPTICS clustering algorithm to define the user scenarios and divide the behavioral data set. Furthermore, it introduces time stamps to label information and discard some old behaviors, thus updating the user reputation. The experimental results show that the proposed reputation model can combine old and new behaviors to calculate and adjust the user reputation well, displaying a good application prospect with respect to the evaluation of user reputation in the perceived environment.