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.
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- Partial requirement for: Ph.D., Arizona State University, 2017Note typethesis
- Includes bibliographical references (pages 103-111)Note typebibliography
- Field of study: Electrical engineering