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

NeTS：EAGER：基于社交计算的车联网智能信息传播

• 批准号: 1761641
• 负责人: Qing Yang
• 金额: $ 13.56万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-31 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761641&HistoricalAwards=false
• 关键词: NeTS; EAGER; Intelligent; Information; Dissemination

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.

车辆网络越来越受欢迎。为了使它们真正有用，应消除车辆之间无关的信息交流，以避免驾驶员分散注意力。该项目旨在解决这个基本问题，其中智能确定了哪些信息。该项目将以信息交换的形式研究基于车辆之间的互动之间的亲密关系，因此驾驶员可以根据亲密信息确定收到的消息是否相关。由于信息被车辆的亲密“朋友”过滤，因此将减少其接收到的无关信息的量，从而实现有效的信息传播。这项研究将产生有效的信息传播系统，以补充并增强现有的智能运输系统，互联车辆和车辆远程信息处理。该项目还将包括部署系统以为驾驶员提供更好的信息提供服务的努力。将在该项目中对两名博士学位学生和几名本科生进行培训。研究人员建议使用车辆之间的互动来估算他们的亲密关系，最重要的是，根据亲密信息，确定应向哪些车辆（S）传递哪些数据。他们方法的关键是构建车辆社交网络（VSN），该网络使驾驶员能够将其社交网络与车辆网络集成在一起。 访问车辆的兴趣点（POI）列表被认为是其基因组，并且具有相似遗传特征的车辆最初被认为是在VSN中连接的。然后，通过车辆之间的相互作用来培养这些连接。通过积极，负和不确定的相互作用，具有直接相互作用的两辆车辆之间的亲密关系被建模为差异分布。对于没有直接互动的车辆，它们之间的亲密关系是从他们之间的社交网络中推断出来的。 PI将设计一个多项式时间解决方案，以解决巨大的亲密评估问题，即计算VSN中从驱动程序到所有其他驱动程序的亲密关系。研究人员还提出了一种有效的算法，以解决全等接近度评估问题，即计算VSN中任何一对车辆的亲密关系。提出了一项云托管服务，以协调社会连接构建，VSN维护，紧密评估和信息传播。

项目成果

Trust Assessment in Vehicular Social Network Based on Three-Valued Subjective Logic

• DOI: 10.1109/tmm.2019.2891417
• 发表时间: 2019-01
• 期刊: IEEE Transactions on Multimedia
• 影响因子: 7.3
• 作者: Tong Cheng;Guangchi Liu;Q. Yang;Jianguo Sun

Trustworthy Website Detection Based on Social Hyperlink Network Analysis

• DOI: 10.1109/tnse.2018.2866066
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Xiaofei Niu;Guangchi Liu;Q. Yang

Trajectory Comparison in a Vehicular Network I: Computing a Consensus Trajectory

车载网络中的轨迹比较 I：计算共识轨迹

• DOI: 10.1007/978-3-030-23597-0_43
• 发表时间: 2019
• 期刊: and Applications
• 作者: Zou, Peng;Qingge, Letu;Yang, Qing;Zhu, Binhai
• 通讯作者: Zhu, Binhai

Trajectory Comparison in a Vehicular Network II: Eliminating the Redundancy

• DOI: 10.1007/978-3-030-23597-0_21
• 发表时间: 2019-06
• 作者: L. Qingge;Peng Zou;Lihui Dai;Qing Yang;B. Zhu

Exploring timeliness for accurate recommendation in location-based social networks

• DOI: 10.3934/mfc.2018002
• 发表时间: 2018-02
• 期刊: Math. Found. Comput.
• 作者: Yi Xu;Qing Yang;Dianhui Chu