NeTS: EAGER: Intelligent Information Dissemination in Vehicular Networks based on Social Computing

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Vehicular networks are becoming increasingly popular. To make them truly useful, irrelevant information exchanges among vehicles should to be eliminated to avoid unnecessary driver distraction. This project aims to tackle this fundamental problem, wherein what information is delivered to which vehicle(s) is intelligently determined. The project will study the closeness between vehicles based their interactions, in the form of information exchange, so a driver can determine whether a received message is relevant based on the closeness information. Because information is filtered by a vehicle's close 'friends', the amount of irrelevant information it receives will be reduced, and thus efficient information dissemination is achieved. The research will produce an efficient information dissemination system that complements and enhances existing intelligent transportation systems, connected vehicles, and vehicular telematics. The project will also include efforts to deploy the system to offer a better information provision service to drivers. Two PhD students and several undergraduate students will be trained in this project.The researchers propose to use interactions between vehicles to estimate their closeness, and most importantly, to determine what data should be delivered to which vehicle(s) based on the closeness information. The key to their approach is constructing a vehicular social network (VSN) that enables drivers to integrate their social network with vehicular network. The list of points of interest (POIs) that a vehicle visited is considered its genome, and vehicles with similar genetic features are considered initially connected in a VSN. These connections are then cultivated by the interactions among vehicles. With positive, negative, and uncertain interactions, the closeness between two vehicles having direct interactions is modeled as a Dirichlet distribution. For vehicles that have no direct interactions, their closeness is inferred from the social network between them. The PIs will design a polynomial-time solution to addressing the massive closeness assessment problem, i.e., computing the closeness from a driver to all others in a VSN. The researchers also propose an efficient algorithm for the all-pair closeness assessment problem, i.e., computing the closeness of any pair of vehicles in a VSN. A cloud-hosted service is proposed to coordinate social connection construction, VSN maintenance, closeness assessment, and information dissemination.