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Robust Distributed Parameter Estimation in Wireless Sensor Networks


Abstract 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.

Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs c... (more)
Created Date 2017
Contributor Lee, Jongmin (Author) / Tepedelenlioglu, Cihan (Advisor) / Spanias, Andreas (Advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Subject Engineering / consensus / distributed sensor network / measure of central tendency / wireless sensor network
Type Doctoral Dissertation
Extent 130 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis