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Distributed Network Structure Estimation Using Consensus Methods
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Bester Preis: € 52,05 (vom 30.06.2022)DISTRIBUTED NETWORK STRUCTURE ESTIMATION USING CONSENSUS METHODS (2018)
ISBN: 9781681732909 bzw. 1681732904, in Englisch, Morgan & Claypool Publishers, Taschenbuch, neu, Nachdruck.
9781681732909 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Print on Demand title, produced to the highest standard, and there would be a delay in dispatch of around 10 working days. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal.
Distributed Network Structure Estimation Using Consensus Methods (2018)
ISBN: 9781681732909 bzw. 1681732904, in Englisch, MORGAN and CLAYPOOL, neu, Nachdruck.
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications) (2018)
ISBN: 9781681732923 bzw. 1681732920, in Englisch, 90 Seiten, Morgan & Claypool Publishers, gebundenes Buch, gebraucht.
Von Händler/Antiquariat, californiabooks.
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 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. Hardcover, Label: Morgan & Claypool Publishers, Morgan & Claypool Publishers, Product group: Book, Published: 2018-03-02, Studio: Morgan & Claypool Publishers.
Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications) (2018)
ISBN: 9781681732923 bzw. 1681732920, in Englisch, 90 Seiten, Morgan & Claypool Publishers, gebundenes Buch, neu.
Von Händler/Antiquariat, Amazon.com.
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 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. Hardcover, Label: Morgan & Claypool Publishers, Morgan & Claypool Publishers, Product group: Book, Published: 2018-03-02, Studio: Morgan & Claypool Publishers.
Distributed Network Structure Estimation Using Consensus Methods (Synthesis Lectures on Communications) (2018)
ISBN: 9781681732909 bzw. 1681732904, in Englisch, 90 Seiten, Morgan & Claypool Publishers, Taschenbuch, gebraucht.
Von Händler/Antiquariat, californiabooks.
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 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. Paperback, Label: Morgan & Claypool Publishers, Morgan & Claypool Publishers, Product group: Book, Published: 2018-03-02, Studio: Morgan & Claypool Publishers, Sales rank: 1880687.
Distributed Network Structure Estimation Using Consensus Methods
ISBN: 9781681732916 bzw. 1681732912, in Englisch, Morgan & Claypool Publishers, neu, E-Book.
Technology, 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 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. eBook.
Distributed Network Structure Estimation Using Consensus Methods
ISBN: 9783031016844 bzw. 303101684X, in Englisch, neu, E-Book, elektronischer Download.
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 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.
Distributed Network Structure Estimation Using Consensus Methods
ISBN: 9781681732916 bzw. 1681732912, in Englisch, Morgan & Claypool Publishers, neu, E-Book, elektronischer Download.
Distributed Network Structure Estimation Using Consensus Methods: 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 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. Englisch, Ebook.
Distributed Network Structure Estimation Using Consensus Methods
ISBN: 9781681732916 bzw. 1681732912, in Englisch, Morgan & Claypool Publishers, neu, E-Book, elektronischer Download.