Description

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors

Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.

Reuse Permissions
  • 1.87 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2017
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: Ph.D., Arizona State University, 2017
      Note type
      thesis
    • Includes bibliographical references (pages 103-111)
      Note type
      bibliography
    • Field of study: Electrical engineering

    Citation and reuse

    Statement of Responsibility

    by Jongmin Lee

    Machine-readable links