Sensors: Field-Gathering Wireless Sensor Networks

摘要0329715大卫Neuhoff密歇根大学，安娜堡现场收集无线传感器网络这个项目开发的理论和实践的无线传感器网络测量二维领域，并将其传输到一个收集器网站，在那里它可以显示解释或用于执行自动目标识别，目标跟踪或其他形式的估计或检测。 这项研究的意义在于在传感器和传感器网络这一新兴领域建立理论和发展技术，并将这一努力应用于广泛的科学研究和社会问题。 该项目还有助于培训研究生和本科生，以及招聘妇女和代表性不足的少数民族学生。这项研究建立在调查人员先前的工作基础上，探讨了基本的限制和实际的方法问题。 基本限制主要与网络效率有关，如通过现场重建的准确性与所需的昂贵资源（如能量和带宽）的数量以及效率如何与传感器的数量和密度成比例来衡量。实际问题与发展接近或达到基本极限的具体方法有关。 待测量和传输的典型场在相邻位置处高度相关。 本项目探讨了两种利用这种相关性来大幅提高效率的方法。 在一种方法中，数据压缩和网络路由被联合设计以彼此互补。 在第二 方法，使用Slepian-Wolf分布式编码。 这使得数据压缩和数据传播功能可以独立设计和操作。 它还考虑了平面和集群或分层网络组织。

ABSTRACT0329715David NeuhoffU of Michigan, Ann ArborField-Gathering Wireless Sensor NetworksThis project develops the theory and practice of wireless sensor networks for measuring a two-dimensional field and transporting it to a collector site, where it can be displayed for interpretation or used to perform automatic object recognition, object tracking or some other form of estimation or detection. The significance of this research lies in the establishment of theory and the development of technology in the emerging area of sensors and sensor networks, and the applicability of this effort to a wide range of scientific studies and social issues. This project also contributes to the training of graduate students, undergraduate students, and the recruitment of women and underrepresented minority students. This research, which builds on the prior work of the investigators, explores both fundamental limits and practical methodological issues. The fundamental limits have principally to do with network efficiency, as measured by the accuracy of the field reconstruction vs. the required quantity of costly resources such as energy and bandwidth, and how the efficiency scales with the number and density of the sensors. The practical issues have to do with the development of concrete methods that approach or attain the fundamental limits. The typical field to be measured and transported is highly correlated at neighboring locations. This project explores two approaches to exploiting this correlation to substantially increase efficiency. In one approach, the data compression and network routing are jointly designed to complement one another. In the second approach, Slepian-Wolf distributed coding is used. This permits the data compression and data dissemination functions to be independently designed and operated. It also considers both flat and clustered or hierarchical network organizations.