CAREER: QoS Maps for Large-Scale, High-mobility Wireless Networks

预计未来10年的无线网络将由大量嵌入衣服、鞋子、汽车、公交车以及传统便携式手持设备和笔记本电脑的微处理器传感器设备组成。由于预计信息本身将通过这些网络中的数千至数百万个无线设备(而不是几个网关)流动，因此必须建立并提供移动网络基本特性的综合视图。服务质量(Qos)决策(关于能量消耗、延迟和吞吐量)将在这些大规模网络中的智能决策中发挥重要作用。该项目的主要研究贡献是通过在相关时间尺度上在节点的联合存储器中构建Qos地图来开发在移动、无线微处理器-传感器设备的网络中跟踪和传播Qos度量的方法。Qos势函数由节点通过路径集成的方法在空间上构造，并通过节点相遇时的信息交换来分配。新的概念，如服务质量指标的相干时间，使信号处理方法能够应用于服务质量地图的细化。该项目为在民用环境中建立由小型、移动、微处理器-传感器设备组成的第一代大规模网络开辟了道路，这将改变我们社会的运行方式，利用过多的传感器和支持将这些信息转化为有用动作的语义设备。该项目将传统上分离的信号处理和网络技术整合到研究生课程中，并为少数民族本科生开发了一个早期职业重点课程。

The wireless networks of the next 10 years are expected to consist of a plethora of microprocessor-sensor devices embedded in clothes, shoes, cars, buses, as well as the traditional portable handhelds, and laptops. Since information is expected to flow through thousands to millions of wireless devices themselves in these networks (rather than a few gateways), it is essential that an aggregate view of the essential qualities of the mobile network be built and made available. Quality-of-Service (QoS) decisions (regarding energy consumption, delay and throughput) will play a prominent role in making intelligent decisions in these large-scale networks.The major research contribution of this project is the development of methodologies for tracking and disseminating QoS metrics in mobile, wireless networks of microprocessor-sensor devices by building QoS maps in the joint memories of the nodes over relevant time scales. QoS potential functions are constructed over space by the nodes via path integration methods, and distributed via information exchange during node encounters. Novel concepts, such as the coherence time of QoS metrics, enable the application of signal processing methods to the refinement of QoS maps.This project opens the way to building the first-generation large-scale networks of small, mobile, microprocessor-sensor devices in civilian settings, which will transform the way our society operates, utilizing a plethora of sensors and supporting semantic devices that translate this information to useful actions. This project integrates the traditionally separate signal processing and networking techniques in graduate coursework, and develops an early career focus program for minority undergraduate students.