Machine Learning for Indoor Localization and Navigation, 1st ed. 2023

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. 

Saideep Tiku is a Walter Scott Jr. College of Engineering Ph.D. candidate in the Department of Electrical and Computer Engineering Department at Colorado State University, Fort Collins, Colorado, USA. He is a Research Assistant at the Embedded, High Performance, and Intelligent Computing (EPIC) Laboratory and his interests include indoor localization, and energy efficiency for fault tolerant embedded systems. His work in the domain of machine learning-based indoor localization has been published and recognized globally in conferences and journals including ACM GLSVLSI 2018, ACM TECS 2019, ACM/IEEE DAC 2019, ACM TCPS 2021, IEEE DATE 2021. He is the recipient of two best paper/poster awards and currently holds 10 (1 awarded, 9 filed) patents in the domain of machine learning-based indoor localization and other fields of applications for machine learning on embedded systems. Saideep Tiku received his B.E. degree in Electronics and Electrical Communication from Panjab University, India in 2013. During his time at CSU, he has worked on embedded projects with companies such as Fiat-Chrysler Automobiles, Mentor Graphics (now Siemens), and Micron Technology. He is the mentor for the undergraduate senior design program at CSU for teams in the domain of indoor localization which was also awarded funding from Keysight technologies. He has served as the INTO program tutor for CSU and the Teaching Assistant for the coursework Hardware/Software Design of Embedded Systems. Saideep Tiku has reviewed 13 publications for reputable conferences and journals and also served as the student volunteer for ACM/IEEE ESWEEK 2021. He is a Student Member of the IEEE.

 

Sudeep Pasricha is a Walter Scott Jr. College of Engineering Professor in the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Department of Systems Engineering at Colorado State University. He is Director of the Embedded, High Performance, and Intelligent Computing