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008			220601s2018 sz | s |||| 0|eng d
020			_a9783031016844
024	7		_a10.1007/978-3-031-01684-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7872.D48 _b2018 EB
100	1		_aZhang, Sai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687875 _c(Electrical engineer)
245	1	0	_aDistributed Network Structure Estimation Using Consensus Methods _cby Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XI, 76 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Communications _x1932-1708
505	0		_aPreface -- Acknowledgments -- Introduction -- Review of Consensus and Network Structure Estimation -- Distributed Node Counting in WSNs -- Noncentralized Estimation of Degree Distribution -- Network Center and Coverage Region Estimation -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aThe 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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9441179 _aRedes de sensores inalámbricas
650		7	_2embne _9156434 _aProceso distribuido (Informática)
650		7	_2embne _9483083 _aInternet de los objetos
700	1		_aTepedelenlioğlu, Cihan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686553
700	1		_aSpanias, Andreas _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686104
700	1		_aBanavar, Mahesh K. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686142
776	0	8	_iPrinted edition: _z9783031000508
776	0	8	_iPrinted edition: _z9783031005565
776	0	8	_iPrinted edition: _z9783031028120
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01684-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI