GRank - An Information-Centric Autonomous and Distributed Ranking of Popular Smart Vehicles

Modern cars are transforming towards autonomous cars capable to make intelligent decisions to facilitate our travel comfort and safety. Such "Smart Vehicles" are equipped with various sensor platforms and cameras that are capable to constantly sense tremendous amount of heterogeneous data from urban streets. This paper aims to identify the appropriate vehicles, important to be selected as information hubs for the efficient collection, storage and distribution of such massive data. Therefore, we propose an Information-Centric algorithm, "GRank" for vehicles to autonomously find their importance based on their reachability for different location-aware information in a collaborative manner, without relying on any infrastructure network. GRank is the first step to identify socially important information hubs to be used in the network. Results from scalable simulations using realistic vehicular mobility traces show that GRank is an efficient ranking algorithm to find important vehicles in comparison to other ranking metrics in the literature.