Localization Based on Social Big Data Analysis in the Vehicular Networks

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.

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