Name: Distributed Network Structure Estimation Using Consensus Methods (en Inglés)
Price: 70500.00 CLP
Availability: InStock
Author: Zhang, Sai ; Tepedelenlioglu, Cihan ; Spanias, Andreas
ISBN: 9783031005565

portada Distributed Network Structure Estimation Using Consensus Methods (en Inglés)

Formato

Libro Físico

Autor

Andreas Spanias

Cihan Tepedelenlioglu

Sai Zhang

Editorial

Springer

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 0.5 cm

Peso

0.17 kg.

ISBN13

9783031005565

N° edición

Categorías

Tecnología de la telefonía móvil
Tecnología wap y wireless
Sensores
Redes y comunicaciones informáticas

Distributed Network Structure Estimation Using Consensus Methods (en Inglés)

Andreas Spanias (Autor) · Cihan Tepedelenlioglu (Autor) · Sai Zhang (Autor) · Springer · Tapa Blanda

Libro Físico

$ 70.500

$ 141.000

Ahorras: $ 70.500

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Reseña del libro "Distributed Network Structure Estimation Using Consensus Methods (en Inglés)"

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.