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

车载网络正变得越来越流行。为了使它们真正有用，应该消除车辆之间不相关的信息交换，以避免不必要的驾驶员分心。该项目旨在解决这个基本问题，即智能确定哪些信息传递给哪些车辆。该项目将以信息交换的形式，基于车辆之间的互动，研究车辆之间的亲密度，因此驾驶员可以根据亲密度信息确定接收到的信息是否相关。由于信息是由车辆的亲密“朋友”过滤的，因此它接收到的无关信息的数量将减少，从而实现有效的信息传播。该研究将产生一个有效的信息传播系统，以补充和增强现有的智能交通系统，互联车辆和车辆远程信息处理。该项目还将努力部署该系统，为司机提供更好的信息提供服务。本项目将培养两名博士生和几名本科生。研究人员建议使用车辆之间的交互来估计它们之间的距离，最重要的是，根据距离信息确定哪些数据应该传递给哪些车辆。该方法的关键是构建一个车载社交网络（VSN），使驾驶员能够将自己的社交网络与车辆网络相结合。车辆所访问的兴趣点（poi）列表被视为其基因组，具有相似遗传特征的车辆被视为在VSN中最初连接的车辆。然后，这些联系通过车辆之间的互动来培养。对于正、负和不确定的相互作用，具有直接相互作用的两辆车之间的紧密度建模为Dirichlet分布。对于没有直接互动的车辆，它们的亲密度是从它们之间的社会网络中推断出来的。pi将设计一个多项式时间的解决方案来解决大规模的亲密度评估问题，即计算VSN中从一个司机到所有其他司机的亲密度。研究人员还提出了一种高效的算法来解决全对亲密度评估问题，即计算VSN中任何一对车辆的亲密度。提出了一种云托管服务，以协调社会连接建设、VSN维护、亲密度评估和信息传播。

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.