CAREER: Distributed Space-Time Processing for Sensor Networks

能够以高空间和时间分辨率近距离监测环境的大规模传感器网络预计将在各种应用中发挥重要作用，例如评估机器、航空航天器和土木工程结构的“健康”;环境、医疗、食品安全和生境监测;能量管理、库存控制、家庭和建筑物自动化等。网络中的每个节点将具有有限的感测、信号处理和通信能力，但通过相互合作，它们将完成传统集中式传感系统难以完成的任务。本研究的重点是传感器网络设计中突出信号处理问题的新解决方案：通过分布式（基于邻域的）处理有效地提取信息，减轻诸如节点定位误差和空间相关测量的实际困难，以及通过主动节点选择节省能量。 分布式贝叶斯算法正在开发用于估计存在节点位置不确定性的物理现象，忽略这些不确定性可能会导致估计和检测性能差。 研究人员还研究了非参数分布式信号处理方法下的实际重要的情况下，参数模型的响应函数和噪声分布是未知的。在这里，我们的目标是提供可靠的推断所观察到的现象，这是使用精确的参数模型所实现的。该计划的教育目标包括将现代教学技术和统计信号处理应用纳入本科工程课程，并将最先进的信号处理融入爱荷华州州立大学的研究生工程课程。该项目的研究成果和开发的教学工具通过互联网和在科学期刊上发表向广大科学界提供。

Large-scale sensor networks that can monitor an environment at close range with high spatial and temporal resolutions are expected to play an important role in various applications, e.g. assessing ``health'' of machines, aerospace vehicles, and civil-engineering structures; environmental, medical, food-safety, and habitat monitoring; energy management, inventory control, home and building automation, etc. Each node in the network will have limited sensing, signal processing, and communication capabilities, but by cooperating with each other they will accomplish tasks that are difficult to perform with conventional centralized sensing systems.This research focuses on novel solutions for prominent signal processing problems in sensor network design: efficiently extracting information through distributed (neighborhood-based) processing, mitigating practical difficulties such as node localization errors and spatially correlated measurements, and conserving energy through active node selection. Distributed Bayesian algorithms are being developed for estimating physical phenomena in the presence of node location uncertainties; ignoring these uncertainties may lead to poor estimation and detection performance. The investigators also study nonparametric distributed signal processing approaches under the practically important scenario where parametric models for the response function and noise distribution are unknown. Here, the goal is to provide reliable inference about the observed phenomenon that is comparable to that achieved using exact parametric models. Educational goals of the program include incorporating modern teaching techniques and statistical signal processing applications into the undergraduate engineering curriculum and integrating state-of-the-art signal processing into the graduate engineering curriculum at the Iowa State University. The research results and teaching tools developed in this project are made available to a broad scientific community through the Internet and publication in scientific journals.