NOSS: Ultra-Wideband Sensor Networks for Automotive Vehicles

NOSS：汽车超宽带传感器网络

• 批准号: 0721813
• 负责人: Jia Li
• 金额: $ 27.5万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721813&HistoricalAwards=false
• 关键词: NOSS; Ultra; Wideband; Sensor; Networks

Sensor communications has been a bottleneck in automotive vehicles. It is extremely important for the automotive industry to improve vehicle safety, reliability, fuel economy, reduce emissions and cost. Powerful electronic control units and the associated distributed sensors and actuators have been introduced. Revolutionary changes from the conventional wired sensors to wireless sensors are required to improve vehicle performance, reduce vehicle weight and cost. This research project undertakes a multidisciplinary and cross-layer approach to develop UWB intra-vehicle sensor network theory and methods. Packet detection theory will be established considering channel propagation statistics and multiple access interference for an intra-vehicle sensor network consisting of 50 mission critical sensors and 50 non-mission critical sensors. Methods will be developed to achieve the packet loss rate of in the detection stage. Reliable MAC protocols will be developed for multi-channel direct-sequence spread spectrum ALOHA UWB sensor networks to guarantee that the maximum delay is ms and the probability of message failure is for message delivery through non-line of sight intra-vehicle channels. Network performance will be analyzed including message throughput, packet loss rate and BER in the presence of multiple access interference considering UWB channel propagation effects and transmission power uncertainty. A UWB intra-vehicle sensor network testbed will be designed and built using cross-layer approaches for real time control and computing. This project can help to reduce the parts cost for intra-vehicle sensor communications and improve fuel economy, reliability and safety. Students will be trained in communications, networks, real-time operating systems and programming in embedded systems.

传感器通信一直是汽车中的瓶颈。 提高汽车的安全性、可靠性、燃油经济性、降低排放和成本对汽车工业来说是极其重要的。 强大的电子控制单元和相关的分布式传感器和执行器已经推出。 传统的有线传感器到无线传感器的革命性变化，需要提高车辆的性能，减少车辆的重量和成本。 本研究计画以跨学科、跨层级的方式，发展超宽频车内感测网路的理论与方法。 针对由50个使命关键传感器和50个非使命关键传感器组成的车内传感器网络，考虑信道传播统计和多址干扰，建立了数据包检测理论。 将开发方法以实现 在检测阶段。 可靠的MAC协议将被开发用于多信道直接序列扩频ALOHA超宽带传感器网络，以保证最大延迟是 ms，并且消息失败的概率是针对通过非视线交通工具内信道的消息传递。 在考虑UWB信道传播效应和发射功率不确定性的情况下，分析了多址干扰下的网络性能，包括消息吞吐量、分组丢失率和误码率。 利用跨层的方法设计并搭建了一个超宽带车载传感器网络实验平台，用于真实的时间控制和计算。 该项目有助于降低车内传感器通信的部件成本，提高燃油经济性、可靠性和安全性。 学生将接受通信，网络，实时操作系统和嵌入式系统编程的培训。