A content centric approach to dissemination of information in vehicular networks

Abstract

Data dissemination in dynamic environments such as vehicular networks has been a critical challenge. One of the key characteristics of vehicular networks is the high intermittent connectivity. Recent studies have investigated and proven the feasibility of a content-centric networking paradigm for vehicular networks. Content-centric information dissemination has a potential number of applications in vehicular networking, including advertising, traffic and parking notifications and emergency announcements. It is clear and evident that knowledge about the type of content and its relevance can enhance the performance of data dissemination in VANETs. In this paper we address the problem of information dissemination in vehicular network environments and propose a model and solution based on a content-centric approach of networking. We leverage the expansion properties of interacting nodes in a cluster to be interpreted in terms of social connections among nodes and perform a selective random network coding approach. We compare the reliability performance of our method with a conventional random network coding approach and comment on the complexity of the proposed solution.