LEADER 04027nam 22005775 450 001 9910734847903321 005 20230629144102.0 010 $a3-031-26712-5 024 7 $a10.1007/978-3-031-26712-3 035 $a(MiAaPQ)EBC30612951 035 $a(Au-PeEL)EBL30612951 035 $a(DE-He213)978-3-031-26712-3 035 $a(PPN)272270768 035 $a(EXLCZ)9927285997700041 100 $a20230629d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Indoor Localization and Navigation$b[electronic resource] /$fedited by Saideep Tiku, Sudeep Pasricha 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (563 pages) 311 08$aPrint version: Tiku, Saideep Machine Learning for Indoor Localization and Navigation Cham : Springer International Publishing AG,c2023 9783031267116 327 $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. 330 $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. 606 $aEmbedded computer systems 606 $aCooperating objects (Computer systems) 606 $aMicroprocessors 606 $aComputer architecture 606 $aEmbedded Systems 606 $aCyber-Physical Systems 606 $aProcessor Architectures 615 0$aEmbedded computer systems. 615 0$aCooperating objects (Computer systems). 615 0$aMicroprocessors. 615 0$aComputer architecture. 615 14$aEmbedded Systems. 615 24$aCyber-Physical Systems. 615 24$aProcessor Architectures. 676 $a621.384191 700 $aTiku$b Saideep$01372956 701 $aPasricha$b Sudeep$01372957 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734847903321 996 $aMachine Learning for Indoor Localization and Navigation$93403873 997 $aUNINA