Extraction de connaissances dans les réseaux ad hoc inter-véhicules

Our work focus on data management in Vehicular Ad Hoc Networks (VANETs). Many pieces of information may be exchanged in such networks, for instance to warn drivers when a potentially dangerous event arises (accident, emergency braking, obstacle in the road, etc.) or to try to assist them (available parking spaces, traffic congestions, real-Time traffic conditions, etc.). Existing systems only use the data exchanged to warn the driver. Then, the data is considered obsolete and is deleted. In this paper, we rather propose to aggregate the data once it becomes obsolete. Our objective is to produce additional knowledge to be used by drivers when no relevant data has been communicated by neighbouring vehicles. For example, it becomes so possible to dynamically detect potentially dangerous road segments or to determine the areas where the probability to find an available parking space is high when none has been received.