Indoor Localization Techniques Based On Wireless Sensor Networks
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Indoor Localization Techniques Based on Wireless Sensor Networks
In this chapter, we presented a set of classifications of indoor localization techniques. We generated categories according to measurement attribute, location algorithms, and communication protocols. The classifications presented in this chapter provide a compact form of overview on WSN-based indoor localizations. Then, based on the classifications, we introduced server-based and range-based localization systems that can be used for the indoor service robot. Specifically, we presented UWB, Wi-Fi, ZigBee, and CSS-based localization systems. From actual experimental tests, however we found that the existing WSN-based methods have their own disadvantage. That is, Ubisense system is expensive and needs heavy hardware equipment. The Wi-Fi system (Ekahau) has a low accuracy and is only useful for the room-level localization. The CSS-based system is too expensive. Thus, this chapter introduced a localization method based on received signal strength index (RSSI).
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.
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking
Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.