Geographical And Fingerprinting Data For Positioning And Navigation Systems
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Geographical and Fingerprinting Data for Positioning and Navigation Systems
Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap explores the state-of-the -art software tools and innovative strategies to provide better understanding of positioning and navigation in indoor environments using fingerprinting techniques. The book provides the different problems and challenges of indoor positioning and navigation services and shows how fingerprinting can be used to address such necessities. This advanced publication provides the useful references educational institutions, industry, academic researchers, professionals, developers and practitioners need to apply, evaluate and reproduce this book's contributions. The readers will learn how to apply the necessary infrastructure to provide fingerprinting services and scalable environments to deal with fingerprint data. - Provides the current state of fingerprinting for indoor positioning and navigation, along with its challenges and achievements - Presents solutions for using WIFI signals to position and navigate in indoor environments - Covers solutions for using the magnetic field to position and navigate in indoor environments - Contains solutions of a modular positioning system as a solution for seamless positioning - Analyzes geographical and fingerprint data in order to provide indoor/outdoor location and navigation systems
Machine Learning for Indoor Localization and Navigation
While 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.
Mobile Computing, Applications, and Services
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Mobile Computing, Applications, and Services (MobiCASE 2011) held in Los Angeles, CA, USA, during October 24-27, 2010. The 18 revised full papers presented together with 12 revised poster papers were carefully reviewed and selected from numerous submissions. The conference papers are organized in seven technical sessions, covering the topics of mobile pervasive applications, system issues, location-aware services, mobile phone based systems, mobile Web and services, tools for mobile environments, and mobile application development issues.