000 04150nam a22005655i 4500
001 978-3-031-26712-3
003 DE-He213
005 20240207153627.0
007 cr nn 008mamaa
008 230629s2023 sz | s |||| 0|eng d
020 _a9783031267123
_9978-3-031-26712-3
050 4 _aTK7895.E42
072 7 _aUKM
_2bicssc
072 7 _aTEC008010
_2bisacsh
072 7 _aUKM
_2thema
082 0 4 _a006.22
_223
245 1 0 _aMachine Learning for Indoor Localization and Navigation
_h[electronic resource] /
_cedited by Saideep Tiku, Sudeep Pasricha.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXV, 567 p. 247 illus., 233 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
505 0 _aIntroduction to Indoor Localization and its Challenges -- Advanced Pattern-Matching Techniques for Indoor Localization -- Machine Learning Approaches for Resilience to Device Heterogeneity -- Enabling Temporal Variation Resilience for ML based Indoor Localization -- Deploying Indoor Localization Frameworks for Resource Constrained Devices -- Securing Indoor Localization Frameworks.
520 _aWhile GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book: Provides comprehensive coverage of the application of machine learning to the domain of indoor localization; Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization; Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.
541 _fUABC ;
_cPerpetuidad
650 0 _aEmbedded computer systems.
650 0 _aCooperating objects (Computer systems).
650 0 _aMicroprocessors.
650 0 _aComputer architecture.
650 1 4 _aEmbedded Systems.
650 2 4 _aCyber-Physical Systems.
650 2 4 _aProcessor Architectures.
700 1 _aTiku, Saideep.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPasricha, Sudeep.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031267116
776 0 8 _iPrinted edition:
_z9783031267130
776 0 8 _iPrinted edition:
_z9783031267147
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-26712-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c261993
_d261992