| 000 | 03964nam a22005535i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01684-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240730165139.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2018 sz | s |||| 0|eng d | ||
| 020 |
_a9783031016844 _9978-3-031-01684-4 |
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| 024 | 7 |
_a10.1007/978-3-031-01684-4 _2doi |
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| 050 | 4 | _aT1-995 | |
| 072 | 7 |
_aTBC _2bicssc |
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_a620 _223 |
| 100 | 1 |
_aZhang, Sai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987616 |
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| 245 | 1 | 0 |
_aDistributed Network Structure Estimation Using Consensus Methods _h[electronic resource] / _cby Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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| 300 |
_aXI, 76 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSynthesis Lectures on Communications, _x1932-1708 |
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| 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 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. | ||
| 650 | 0 |
_aEngineering. _99405 |
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| 650 | 0 |
_aElectrical engineering. _987618 |
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| 650 | 0 |
_aTelecommunication. _910437 |
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| 650 | 1 | 4 |
_aTechnology and Engineering. _987620 |
| 650 | 2 | 4 |
_aElectrical and Electronic Engineering. _987621 |
| 650 | 2 | 4 |
_aCommunications Engineering, Networks. _931570 |
| 700 | 1 |
_aTepedelenlioglu, Cihan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987623 |
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| 700 | 1 |
_aSpanias, Andreas. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987625 |
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| 700 | 1 |
_aBanavar, Mahesh. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987626 |
|
| 710 | 2 |
_aSpringerLink (Online service) _987629 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031000508 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031005565 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031028120 |
| 830 | 0 |
_aSynthesis Lectures on Communications, _x1932-1708 _987631 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01684-4 |
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