Problems in distributed signal processing in wireless sensor networks.

K-REx Repository

Show simple item record

dc.contributor.author Krishnan, Rajet
dc.date.accessioned 2009-04-30T21:49:49Z
dc.date.available 2009-04-30T21:49:49Z
dc.date.issued 2009-04-30T21:49:49Z
dc.identifier.uri http://hdl.handle.net/2097/1351
dc.description.abstract In this thesis, we first consider the problem of distributed estimation in an energy and rate-constrained wireless sensor network. To this end, we study three estimators namely - (1) Best Linear Unbiased Estimator (BLUE-1) that accounts for the variance of noise in measurement, uniform quantization and channel, and derive its variance and its lower bound; (2) Best Linear Unbiased Estimator (BLUE-2) that accounts for the variance of noise in measurement and uniform quantization, and derive lower and upper bounds for its variance; (3) Best Linear Unbiased Estima- tor (BLUE-3) that incorporates the effects of probabilistic quantization noise and measurement noise, and derive an upper bound for its variance. Then using BLUE-1, we analyze the tradeoff between estimation error (BLUE variance) at the fusion center and the total amount of resources utilized (power and rate) using three different system design approaches or optimization formulations. For all the formulations, we determine optimum quantization bits and transmission power per bit (or optimum actions) for all sensors jointly. Unlike prior efforts, we in- corporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. We study the e®ect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit. In the sequel, we consider a problem in distributed detection and signal processing in the context of biomedical wireless sensors and more specifically pulse- oximeter devices that record photoplethysmographic data. We propose an automated, two-stage PPG data processing method to minimize the effect of motion artifact. Regarding stage one, we present novel and consistent techniques to detect the presence of motion artifact in photoplethysmograms given higher order statistical information present in the data.For stage two, we propose an effective motion artifact reduction method that involves enhanced PPG data preprocessing followed by frequency domain Independent Component Analysis (FD-ICA). Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method. Finally, we analyze a wireless ad hoc/sensor network where nodes are connected via random channels and information is transported in the network in a cooperative multihop fashion using amplify and forward relay strategy. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject Wireless Sensor Networks en
dc.subject Distributed Estimation en
dc.subject Distributed Detection en
dc.subject Optimization en
dc.subject Best Linear Unbiased Estimator en
dc.subject Amplify and Forward en
dc.title Problems in distributed signal processing in wireless sensor networks. en
dc.type Thesis en
dc.description.degree Master of Science en
dc.description.level Masters en
dc.description.department Department of Electrical and Computer Engineering en
dc.description.advisor Balasubramaniam Natarajan en
dc.subject.umi Engineering, Biomedical (0541) en
dc.subject.umi Engineering, Electronics and Electrical (0544) en
dc.date.published 2009 en
dc.date.graduationmonth May en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search K-REx


Advanced Search

Browse

My Account

Statistics








Center for the

Advancement of Digital

Scholarship

cads@k-state.edu