CAREER: Distributed Space-Time Processing for Sensor Networks

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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.