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，以避免不必要的司机分心。这个项目旨在解决这个根本问题，即智能地确定将什么信息传递给哪辆车(S)。该项目将以信息交换的形式研究车辆之间的接近程度，这样司机就可以根据接近程度信息来确定接收到的消息是否相关。由于信息由车辆的亲密朋友过滤，因此它接收的无关信息量将减少，从而实现高效的信息传播。这项研究将产生一个高效的信息传播系统，对现有的智能交通系统、互联车辆和车载远程信息处理进行补充和增强。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。两名博士生和几名本科生将在这个项目中接受培训。研究人员建议使用车辆之间的相互作用来估计它们的接近程度，最重要的是，根据接近程度信息来确定应该向哪个工具(S)传递哪些数据。他们方法的关键是构建一个车载社交网络(VSN)，使司机能够将他们的社交网络与车载网络整合在一起。车辆访问的兴趣点(POI)列表被认为是其基因组，具有相似遗传特征的车辆被认为最初连接在VSN中。然后，这些联系通过车辆之间的相互作用来培养。在有正、负和不确定相互作用的情况下，具有直接相互作用的两辆车辆之间的接近程度被建模为狄利克雷分布。对于没有直接互动的车辆，它们之间的亲密度是从它们之间的社交网络推断出来的。PI将设计一个多项式时间解决方案来解决大规模的贴近度评估问题，即计算从一个司机到VSN中所有其他司机的贴近度。研究人员还提出了一种有效的算法来解决所有车辆对的贴近度评估问题，即计算车辆网络中任意一对车辆的贴近度。建议使用云托管服务来协调社交连接建设、VSN维护、封闭性评估和信息传播。

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.