Short paper: Towards data-similarity-based clustering for inter-vehicle communication

Many applications of inter-vehicle communication networks (IVC) rely on multi-hop dissemination of information using vehicle-to-vehicle communication. An example are traffic information systems, which aim to provide up-to-date information about traffic jams to drivers. Due to the large amount of information and wide dissemination ranges, naïve protocols fail to scale to multi-hop dissemination. Therefore, a wide range of efficient protocols has been investigated. While clustering protocols are successfully used for similar applications in other domains, such as wireless sensor networks, they have been neglected by IVC research, because they often fail due to high vehicle mobility. In this paper, we present a data-similarity-based approach for clustering in inter-vehicle networks, which is built to work in high mobility settings. In contrast to existing clustering proposals, we explicitly draw from knowledge gained during the development of other multi-hop dissemination mechanisms in general and in-network aggregation in particular. We demonstrate our approach with a clustering protocol proposal and show first evaluation results using theoretical analyses.