Spatial signal processing in wireless sensor networks
Zhang, Benhong (2006) Spatial signal processing in wireless sensor networks. PhD thesis, Iowa State University.
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Wireless sensor networks (WSNs) are gaining attention in recent years. Considering the potential low cost of a single sensor-processor unit in the near future, it is envisioned that there will be large-scale deployments of sensor networks for various applications: environmental, medical, inventory control, energy management, structural health monitoring, etc. A WSN comprises of a large number of nodes that individually have limited energy and computational power; however, by cooperating with each other, they can jointly perform tasks that are difficult to handle by traditional centralized sensing systems. In this dissertation, spatial and spatio-temporal signal processing methods are developed for WSNs: • distributed estimation and detection using hidden Markov random fields: We derive ICM algorithms for distributed estimation of the hidden random field from the noisy measurements and consider both parametric and nonparametric measurement-error models. • parametric signal estimation in the presence of node localization errors: We propose a Bayesian framework that accounts for the inherent uncertainties in the node locations (caused by the node localization errors) and develop an estimation method that is robust to these uncertainties. • event-region estimation under the Poisson regime: We propose a parametric model for the location and shape of the event region and develop a Bayesian method for event-region estimation in large-scale sensor networks. • sequential mean-field estimation and detection in spatially correlated Gaussian fields: We propose distributed methods for estimating and detecting the mean of a correlated Gaussian random field observed by a sensor network. We consider estimation and detection of both localized and global phenomena and practically important nonparametric scenarios where the distribution of the measurements is unknown.
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