—Vehicles today are equipped with various sensors and cameras to gather and share tremendous amount of heterogeneous data from urban streets. The challenge is to identify the appropriate vehicles from the fleet of vehicles, important for the collection, distribution and storage of such massive data. This paper addresses the autonomous identification of such "Smart Vehicles" without relying on the infrastructure network. Therefore , we propose " InfoRank " as an Information-centric algorithm for a vehicle to first rank different location-aware information associated to it. It then uses the information importance to analytically find its relative importance in the network. InfoRank is the first step towards identifying the best information hubs to be used in the network for the efficient collection, storage and distribution of urban sensory information. Results from scalable simulations using realistic vehicular mobility traces show that InfoRank is an efficient ranking algorithm to find popular information facilitator vehicles in comparison to other ranking metrics in the literature.