参与式感知系统中用户累积行为信誉计算

参与式感知系统中,由于感知数据质量可能受参与者影响,提出了基于用户累积行为的信誉计算模型以帮助选择可信赖用户.针对感知环境中用户群体的广泛性及核心用户的不确定性,该模型采用OPTICS聚类算法定义用户场景并划分行为数据集,建立用户累积行为信誉计算模型,同时引入时间戳标记信息抛弃部分旧行为以更新用户信誉.实验表明,该信誉模型能够结合新旧行为较好地计算并调整用户信誉,在感知环境用户信誉评价中具有良好的应用前景.

Reputation Calculation Model Based on Cumulative Behavior of Users in Participatory Sensing System

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.