Towards approximate spatial queries for large-scale vehicle networks

With advances in vehicle-to-vehicle communication, future vehicles will have access to a communication channel through which messages can be sent and received when two get close to each other. This enabling technology makes it possible for authenticated users to send queries to those vehicles of interest, such as those that are located within a geographic region, over multiple hops for various application goals. However, a naive method that requires flooding the queries to each active vehicle in a region will incur a total communication overhead that is proportional to the size of the area and the density of vehicles. In this paper, we study the problem of spatial queries for vehicle networks by investigating probabilistic methods, where we only try to obtain approximate estimates within desired confidence intervals using only sublinear overheads. We consider this to be particularly useful when spatial query results can be made approximate or not precise, as is the case with many potential applications. The proposed method has been tested on snapshots from real world vehicle network traces.