Semester

Spring

Date of Graduation

2013

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Matthew C. Valenti

Committee Co-Chair

Daryl S. Reynolds

Committee Member

Natalia A. Schmid

Abstract

Wireless Sensor Networks (WSNs) are traditionally employed to collect spatial and temporal data characterizing various events. These data are then used to solve inference problems such as object detection, counting, classification, estimation and tracking. Distributed solutions provided by WSNs are often cost effective and characterized by high performance indices.;In this work, we model and simulate a distributed sensor network composed of radiation detectors and analyze its ability to make inferences. Radiation detectors are deployed over a known area. A radiological point source is positioned in the interior of the area. Detectors take measurements of the field generated by the point source and transmit them (without any interaction with one another) to a remotely installed super computer (called here Fusion Center) for a joint processing. To minimize consumption of resources such as power in the network and transmission bandwidth, the measurements are locally preprocessed prior to transmission. Our model assumes two Gaussian channels, observation and transmission. The first channel distorts data at the receiver end of each sensor during data acquisition. The second channel distorts data during transmission. Sensor measurements are modeled as an inhomogeneous spatial counting random process (Poisson process). The location of the radiological point source in the area and the strength of the field generated by the substance are unknown parameters. The goal of the FC is to estimate these parameters from the distributed measurements provided by the WSN. To find the distributed estimates, we adopt the Maximum Likelihood approach. This approach requires knowledge of the joint probability density function of the distributed measurements observed by the FC. Since the joint probability density of the data observed at the FC is nonlinear in unknown parameters, we propose an iterative approach to solve for the maximum likelihood estimates of these parameters. The solution is a combination of the Bisection and Secant approaches adjusted to seek solution in a multidimensional parameter space. The performance of the distributed estimator is measured in terms of the mean square error. It is analyzed with respect to various parameters of the WSN. We vary the following parameters of the network: (1) the number of sensors in the WSN, (2) signal to noise ratio in observation and transmission channels, (3) the strength of the original field, and (4) the number of quantization levels used by a sensor to convert an analog measurement into a digital signal. We also propose a distributed tracking algorithm for monitoring position of the object in real time.

Download

Share

COinS