TWC: Small: Evidence of Presence for Intelligent Vehicles using Environment-Based Security

TWC：小：使用基于环境的安全性的智能车辆的存在证据

• 批准号: 1618398
• 负责人: Chiu Tan
• 金额: $ 41.69万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618398&HistoricalAwards=false
• 关键词: TWC; Small; Evidence; Presence; Intelligent

Emerging intelligent automobiles will be able to harness advance on-car sensors to support new applications such as pollution detection, road condition monitoring, and traffic control. All these applications require the ability to verify both the location and the time of a reading. This project involves the design of verification methods that make use of environment factors, such as the presence of light and shadows and the measured wireless signal strength, instead of conventional public key infrastructure-based methods, in order to verify when and where data was collected. This new environment-based paradigm is resilient against insider attacks, easier to deploy, and protects the individual car owner's privacy. This research will help in realizing applications arising from the increasing presence of smarter vehicles on our roads which can benefit the public's wellbeing. The project also incorporates outreach components for high school and college students through the organization of science competitions, national undergraduate workshops, and summer research camp activities. This project is the first effort to use the wireless communication and video recording abilities of modern automobiles to capture natural environment characteristics to securely verify spatial-temporal claims in a vehicular network setting. This is an interdisciplinary research effort combining wireless networking and computer vision to address vehicular network security. The main project goals are: (1) explore new vision analytic algorithms in order to identify the location and time images are captured by an automobile camera; (2) research new algorithms for identifying optimal roadside unit locations for location disambiguation to support wireless spatial-temporal verification; (3) develop new techniques for utilizing encounters with public vehicles for verifying spatial-temporal claims; (4) developing a fusion framework to combine wireless measurements, vehicular encounters, and visual images for spatial-temporal verification; and (5) perform realistic experiments on real roads and vehicular test bed to collect image and wireless datasets and evaluate the research. These datasets are of interest to both wireless networking and computer vision research communities, and will be made available to the public.

新兴的智能汽车将能够利用先进的车载传感器来支持新的应用，如污染检测、路况监测和交通控制。所有这些应用都需要能够验证读数的位置和时间。该项目涉及设计利用环境因素的验证方法，例如光和阴影的存在以及测量的无线信号强度，而不是传统的基于公钥基础设施的方法，以便验证数据是在何时何地收集的。这种基于环境的新模式对内部攻击具有弹性，更易于部署，并保护个人车主的隐私。这项研究将有助于实现越来越多的智能汽车出现在我们的道路上，从而造福公众的应用。该项目还通过组织科学竞赛、全国本科生讲习班和暑期研究营活动，纳入了针对高中生和大学生的外展内容。该项目是首次利用现代汽车的无线通信和视频记录能力来捕捉自然环境特征，以安全地验证车载网络环境中的时空主张。这是一项结合无线网络和计算机视觉的跨学科研究成果，旨在解决车载网络安全问题。项目的主要目标是：(1)探索新的视觉分析算法，以识别汽车摄像头拍摄的位置和时间图像；(2)研究新的算法，以确定位置消除歧义的最佳路边单元位置，以支持无线时空验证；(3)开发新技术，利用与公共车辆的相遇来验证时空索赔；(4)开发融合框架，将无线测量、车辆相遇和视觉图像结合起来进行时空验证；以及(5)在真实道路和车辆试验台上进行现实实验，收集图像和无线数据集，并对研究进行评估。这些数据集对无线网络和计算机视觉研究界都很感兴趣，并将向公众提供。