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
024 7 _a10.1007/978-3-031-01684-4
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aZhang, Sai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987616
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.
300 _aXI, 76 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 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
650 0 _aElectrical engineering.
_987618
650 0 _aTelecommunication.
_910437
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
700 1 _aSpanias, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987625
700 1 _aBanavar, Mahesh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987626
710 2 _aSpringerLink (Online service)
_987629
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
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01684-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86126
_d86126