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论文类型：期刊论文

发表时间：2018-01-01

发表刊物：INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES

收录刊物：SCIE

卷号：14

期号：1,SI

页面范围：88-105

ISSN号：1741-1106

关键字：crowdsourcing; Kalman filter; matrix completion; trajectory tracking

摘要：In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.