Distributed Network Structure Estimation by Zhang Sai (9 results)
Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications)
Zhang, Sai; Tepedelenlioglu, Cihan; Spanias, Andreas; Banavar, Mahesh
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Language: English
Published by Springer, Berlin, Springer International Publishing, Morgan & Claypool, Springer, 2018
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor… network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm forestimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.
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Taschenbuch. Condition: Neu. Distributed Network Structure Estimation Using Consensus Methods | Sai Zhang (u. a.) | Taschenbuch | Synthesis Lectures on Communications | xi | Englisch | 2018 | Springer | EAN 9783031005565 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]h…artmann[at]springer[dot]com | Anbieter: preigu.
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Language: English
Published by Berlin Springer International Publishing Springer Mrz 2018, 2018
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired ce…ntralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region. 76 pp. Englisch.
Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications)
Zhang, Sai; Tepedelenlioglu, Cihan; Spanias, Andreas; Banavar, Mahesh
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Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications)
Zhang, Sai; Tepedelenlioglu, Cihan; Spanias, Andreas; Banavar, Mahesh
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Distributed Network Structure Estimation Using Consensus Methods
Sai Zhang|Cihan Tepedelenlioglu|Andreas Spanias|Mahesh Banavar
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Sai Zhang received a B.S. degree in electrical and information engineering from Huazhong University of Science and Technology, Wuhan, China, in 2012 and an M.S. degree in electrical engineering from Arizona State Univ…ersity, Tempe, AZ, in 2014. From 2014 to.
Language: English
Published by Springer, Springer International Publishing Mär 2018, 2018
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centra…lized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm forestimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 92 pp. Englisch.
