Probabilistic Localization of Underwater Sensor Networks

  • Author / Creator
    Abougamila, Salwa
  • 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.

  • Subjects / Keywords
  • Graduation date
    2016-06:Fall 2016
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. 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.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Elmallah, Ehab (Computing Science)
  • Examining committee members and their departments
    • Stewart, Lorna (Computing Science)
    • Harms, Janelle (Computing Science)