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Permanent link (DOI): https://doi.org/10.7939/R3RV0DB2Q

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Probabilistic Localization of Underwater Sensor Networks Open Access

Descriptions

Other title
Subject/Keyword
Underwater sensor networks
Localization
Mobility
Probabilistic graph theory
Partial k-tree
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Abougamila, Salwa
Supervisor and department
Elmallah, Ehab (Computing Science)
Examining committee member and department
Harms, Janelle (Computing Science)
Stewart, Lorna (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2016-09-22T16:02:07Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
In recent years, Underwater Sensor Networks (UWSNs) have attracted attention for their potential use in many applications. To name a few, UWSNs have been considered in studying marine life, oceanographic data collection, monitoring underwater oil pipelines, and a variety of military and homeland security applications. UWSN deployments can be either static, semi-mobile, or mobile. For such networks, localization of nodes and observed events arise as a fundamental task where the obtained location information can be used in data tagging, routing, and node tracking. Analyzing the ability of an UWSN to perform a localization task is most challenging for networks with uncontrollable mobile nodes. In this thesis, we approach the above class of problems by adopting a probabilistic graph model to describe node location uncertainty in semi-mobile and mobile deployments. Using the above model, we formalize a probabilistic node localization performance measure, and a corresponding problem to compute the measure. Using the theory of partial k-trees, we develop an exact algorithm that runs in polynomial time, for any fixed k. We next consider the structure of network configurations that contribute to solving the formalized problem. We call such structures pathsets. We then devise an iterative algorithm that aims to generating a most probable pathset in each iteration. In addition, we present numerical results that illustrate the quality of the obtained solutions, and the use of the devised algorithms in UWSNs design.
Language
English
DOI
doi:10.7939/R3RV0DB2Q
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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