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On Distributed Estimation for Resource Constrained Wireless Sensor Networks
 Date Issued:
 2017
 Abstract/Description:
 We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression,dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly:1 Consider an inhomogeneous WSN where the sensors' observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidthconstrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multibit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE.2 Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multibit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closedform. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian CramerRao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound.As a dual problem we also answer the question: what is the minimum required bandwidth of thenetwork to satisfy a predetermined target MSE?3 Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as 'enhancement mode' of multiplicative noise. Through deriving different lower bounds, such as BCRLB,WeissWeinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. Wealso compare the performance of wellknown estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes.
Title:  On Distributed Estimation for Resource Constrained Wireless Sensor Networks. 
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Name(s): 
Sani, Alireza, Author Vosoughi, Azadeh, Committee Chair Rahnavard, Nazanin, Committee Member Wei, Lei, Committee Member Atia, George, Committee Member Chatterjee, Mainak, Committee Member University of Central Florida, Degree Grantor 

Type of Resource:  text  
Date Issued:  2017  
Publisher:  University of Central Florida  
Language(s):  English  
Abstract/Description:  We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression,dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly:1 Consider an inhomogeneous WSN where the sensors' observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidthconstrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multibit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE.2 Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multibit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closedform. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian CramerRao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound.As a dual problem we also answer the question: what is the minimum required bandwidth of thenetwork to satisfy a predetermined target MSE?3 Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as 'enhancement mode' of multiplicative noise. Through deriving different lower bounds, such as BCRLB,WeissWeinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. Wealso compare the performance of wellknown estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes.  
Identifier:  CFE0006913 (IID), ucf:51698 (fedora)  
Note(s): 
20171201 Ph.D. Engineering and Computer Science, Electrical Engineering and Computer Engineering Doctoral This record was generated from author submitted information. 

Subject(s):  Distributed estimation  bandwidthconstrained estimation  powerconstrained estimation  rate and power allocation  Quantization  linear estimators  Mean Square Error minimization  linear observation model  Gaussian vector  distributed optimization  numerical optimization  KKT conditions  MonteCarlo simulations  multiplicative noise  enhancement modes  noiseenhanced estimation  
Persistent Link to This Record:  http://purl.flvc.org/ucf/fd/CFE0006913  
Restrictions on Access:  public 20171215  
Host Institution:  UCF 