Machine learning for indoor localization and navigation / Saideep Tiku, Sudeep Pasricha, editors.
Saideep, Tiku, editor.; Sudeep Pasricha, editor.
2023
TK5103.48323
Available Online

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Title
Machine learning for indoor localization and navigation / Saideep Tiku, Sudeep Pasricha, editors.
ISBN
9783031267123 (electronic bk.)
3031267125 (electronic bk.)
3031267117
9783031267116
DOI
https://doi.org/10.1007/978-3-031-26712-3
Published
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource (567 pages)
Item Number
10.1007/978-3-031-26712-3 doi
Call Number
TK5103.48323
Dewey Decimal Classification
621.3841/91
Summary
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 several novel 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Added Author
Saideep, Tiku, editor.
Sudeep Pasricha, editor.
Available in Other Form
Print version: 9783031267116
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Online Access
Record Appears in
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All Resources
Part 1. Introduction to indoor localization and navigation
An overview of indoor localization techniques / Saideep Tiku and Sudeep Pasricha
Smart device-based PDR methods for indoor localization / Siya Bao and Nozomu Togawa
Geometric indoor radiolocation: history, trends and open issues / Antonello Florio, Gianfranco Avitabile, and Guiseppe Coviello
Indoor localization using trilateration and location fingerprinting methods / Lu Bai, Maurice D. Mulvenna, and Raymond R. Bond
Localization with Wi-Fi ranging and built-in sensors: self-learning techniques / Jeongsik Choi, Yang-Seok Choi, and Shilpa Talwar
Part II. Advanced pattern-matching techniques for indoor localization and navigation
Fusion Wi-Fi and IMU using swarm optimization for indoor localization / He Huang, Jianfei Yang, Xu Fang, Hao Jiang, and Lihua Xie
A scalable framework for indoor localization using convolutional neural networks / Saideep Tiku, Ayush Mittal, and Sudeep Pasricha
Learning indoor area localization: the trade-off between expressiveness and reliability / Marius Laska and Jörg Blankenbach
Exploiting fingerprint correlation for fingerprint-based indoor localization: a deep learning-based approach / Yang Zheng, Junyu Liu, Min Sheng, and Chengyi Zhou
On the application of graph neural networks for indoor positioning systems / Facundo Lezama, Frederico Larroca, and Germán Capdehourat
Part III. Machine learning approaches for resilience to device heterogeneity
Overview of approaches for device heterogeneity management during indoor localization / Cunyi Yin, Hao Jiang, and Jing Chen
Deep learning for resilience to device heterogeneity in cellular-based localization / Hamada Rizk
A portable indoor localization framework for smartphone heterogeneity resilience / Saideep Tiku and Sudeep Pasricha
Smartphone invariant indoor localization using multi-head attention neural network / Saideep Tiku, Danish Gufran, and Sudeep Pasricha
Heterogeneous device resilient indoor localization using vision transformer neural networks / Danish Gufran, Saideep Tiku, and Sudeep Pasricha
Part IV. Enabling temporal variation resilience for ML-based indoor localization and navigation
Enabling temporal variation resilience for ML-based indoor localization / Nobuhiko Nishio, Kota Tsubouchi, Masato Sugasaki, and Masamichi Shimosaka
A few-shot contrastive learning framework for long-term indoor localization / Saideep Tiku and Sudeep Pasricha
A manifold-based method for long-term indoor positioning using WiFi fingerprinting / Yifan Yang, Saideep Tiku, Mahmood R. Azimi-Sadjadi, and Sudeep Pasricha
Part V. Deploying indoor localization and navigation frameworks for resource constrained devices
Exploring model compression for deep machine learning-based indoor localization / Saideep Tiku, Liping Wang, and Sudeep Pasricha
Resource-aware deep learning for wireless fingerprinting localization / Gregor Cerar, Blaž Bertalaniĉ, and Carolina Fortuna
Toward real-time indoor localization with smartphones with conditional deep learning / Saideep Tiku, Prathmesh Kale, and Saideep Pasricha
Part VI. Securing indoor localization and navigation frameworks
Enabling security for fingerprinting-based indoor localization on mobile devices / Saideep Tiku and Sudeep Pasricha.

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