Relevance-Aware Information Dissemination in Vehicular Networks

As a step towards fully autonomous driving, Advanced Driver Assistance Systems provide convenience-and safety-related functions to drivers. In addition to data gathered by local sensors, these systems rely on events generated by other vehicles that need to be disseminated to a potentially large audience. Today, this geocast-functionality relies either on subscriptions covering certain areas (e.g., cities) or on individual route-based subscriptions. While the former exhibits suboptimal precision in filtering, the latter introduces significant complexity and assume that routes are known in advance. We propose a prediction-based assessment of the relevance of events without requiring prior route knowledge. Relevance is modeled based on the street network and spatio-temporal characteristics of events. We evaluate our approach in a realistic city setting, relying on the SUMO vehicular mobility simulator. Our first results show that relevance-aware information dissemination reduces the communication overhead by 68%, while at the same time achieving near perfect recall compared to route-based subscriptions.