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Car Rank: An Information-Centric Identification of Important Smart Vehicles for Urban Sensing

Future cars are becoming powerful sensor platforms capable to collect, store and share large amount of sensory data by constant monitoring of urban streets. It is quite challenging to upload such data from all vehicles to the infrastructure due to limited bandwidth resources and high upload cost. This invoke the need to identify the appropriate vehicles within the Vehicular Ad-hoc Network, that are important for different urban sensing tasks based on their natural mobility and availability. This paper address this problem leveraging the self-decision making ability of a "Smart Vehicle" regarding its importance in the network. To do so, we present CarRank, an Information-Centric algorithm for a vehicle to first rank different location-aware information. It then uses the information importance, its spatio-temporal availability and neighborhood topology to analytically find its relative importance in the network. CarRank is the first step towards identifying the best set of information hubs to be used in the network for the efficient collection, storage and distribution of urban sensory information. We evaluate CarRank under a scalable simulation environment using realistic vehicular mobility traces. Results show that CarRank is an efficient ranking algorithm to identify socially important vehicles in comparison to other ranking metrics used in the literature.