NeTS: Small: SOAR: Leveraging Traffic Signatures to Improve Performance of Vehicular Networks

NeTS：小型：SOAR：利用流量签名提高车辆网络的性能

• 批准号: 1423221
• 负责人: Guoliang Xing
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423221&HistoricalAwards=false
• 关键词: NeTS; Small; SOAR; Leveraging; Traffic

Vehicular networking is an enabling technology for the next-generation transportation services, such as vehicle safety communication, remote vehicle diagnosis, and network-assisted autonomous driving. A major challenge to support these applications is to achieve real-time, reliable communication using limited wireless network spectrum resource, in the presence of noise, interference, and mobility-induced wireless channel variations. This project develops a novel cross-layer framework that improves the performance of vehicular networks without incurring extra spectrum overhead. The key observation is that traffic typically exhibits high predictability due to protocol-specific header structures and the spatiotemporal correlation of application data, such as vehicle positions, on-board sensor readings, and the range to surrounding objects. This project takes a system approach to leveraging such traffic signatures to optimize vehicular network performance. The approach is based on a top-down cross-layer architecture, which consists of 1) a traffic signature extraction algorithm that achieves assured accuracy under the dynamics of network traffic; 2) traffic signature-aware PHY algorithms that improve the robustness and resilience to channel variations and interference; and 3) a comprehensive link layer that exploits traffic signatures in fine-grained optimization of power control, rate adaptation and error recovery.This project has broad implications for future vehicular network systems in multiple application domains that demand high communication reliability under spectrum resource constraints. The solution framework is transparent to vehicular network applications and can be integrated with various wireless standards, including 802.11p, WiMAX and LTE, for providing better Quality of Service (QoS) in both inter-vehicle and vehicle-to-infrastructure networks. Educational and outreach activities include introduction of vehicular network systems into new graduate courses with software, testbed, and labs developed in the course of this project, and recruitment of women and minority students for participation in the project.

车辆网络是车辆安全通信、远程车辆诊断、网络辅助自动驾驶等下一代交通服务的使能技术。支持这些应用的一个主要挑战是在存在噪声、干扰和移动引起的无线信道变化的情况下，利用有限的无线网络频谱资源实现实时、可靠的通信。该项目开发了一种新的跨层框架，可以在不增加额外频谱开销的情况下提高车载网络的性能。关键的观察结果是，由于协议特定的标题结构和应用数据的时空相关性，例如车辆位置、车载传感器读数以及到周围物体的距离，交通通常表现出高度的可预测性。该项目采用系统方法来利用这些交通特征来优化车辆网络性能。该方法基于自顶向下的跨层体系结构，该体系结构包括：1)流量特征提取算法在网络流量动态变化下保证提取精度；2)流量签名感知PHY算法，提高对信道变化和干扰的鲁棒性和弹性；3)综合链路层，利用流量签名对功率控制、速率适应和错误恢复进行细粒度优化。该项目对未来车载网络系统在频谱资源限制下要求高通信可靠性的多个应用领域具有广泛的意义。该解决方案框架对车载网络应用是透明的，并可与各种无线标准集成，包括802.11p、WiMAX和LTE，以便在车际和车对基础设施网络中提供更好的服务质量（QoS）。教育和推广活动包括将车辆网络系统引入新的研究生课程，并在该项目过程中开发软件、测试平台和实验室，以及招募妇女和少数民族学生参与该项目。