A Privacy Preservation Based Scheme for Task Assignment in Internet of Things

With the rapid increasing demands of social services, mobile crowd sensing (MCS) is getting growing attraction with the wide applicability of mobile applications. This effective paradigm depends on sensing services from crowd contributors for sensing data collection and sharing. However, on one hand, the sensing data is tampered by malicious users without the reputation evaluation. On the other hand, the privacy information is exposed when mobile users submit sensing data to others. Moreover, the efficiency of task assignment is low without incentives. Therefore, we propose a privacy preservation based scheme for task assignment in internet of things (IoT) to improve the performance of crowd sensing. Firstly, a novel trusted framework is developed to judge mobile user's reputation value. In the proposed framework, the reputation evaluation scheme is established based on the social trust, recommendation credibility, and history evaluation. Secondly, the location-privacy preservation algorithm (LPA) based differential privacy method is designed to protect mobile user's location privacy and select the credible users to complete sensing tasks. Thirdly, to incentivize mobile users, we model the transaction process between mobile requesters and mobile responders as the first-order sealed price auction game. After that, the task assignment based bidding-preferred (TAB) algorithm is proposed to assign tasks to mobile users. Finally, simulation results demonstrate that the proposed scheme can effectively improve the number of finished tasks and bring more utilities to mobile users compared with the conventional methods.