Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Saideep Tiku
Ph.D. Final
Feb 14, 2022, 11:30 am - 1:30 pm
Zoom - see ECE email for link
Abstract: The advent of the Global Positioning System (GPS) reformed the global transportation industry and allowed vehicles to not only localize themselves but also to navigate reliably and in a secure manner across the world at high speeds. Today, indoor localization is an emerging IoT domain that is poised to reinvent the way we navigate within buildings and subterranean locales, with many benefits, e.g., directing emergency response services after a 911 call to a precise location (with sub-meter accuracy) inside a building, accurate tracking of equipment and inventory in hospitals, factories, and warehouses, etc. 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, and inside buildings and other covered structures, where GPS signals are severely attenuated or totally blocked, and affected by multipath interference. Thus, very different solutions are needed to support localization in indoor locales.
Popular solutions for indoor positioning with high accuracy leverage wireless radio signals, such as WiFi, Bluetooth ultra-wideband (UWB), etc. Due to the existing widespread deployment of WiFi access points (WAPs) in most indoor locales, using WiFi for indoor localization can lead to low-cost solutions. Many localization algorithms that utilize these wireless signals have been proposed, e.g., based on the principles of proximity, trilateration, triangulation, and fingerprinting. Studies have shown that fingerprinting-based algorithms deliver higher accuracy, without stringent synchronization or line-of-sight requirements and enable greater error resilience in the presence of frequently encountered multipath signal interference effects, than other alternatives.
A fingerprinting-based approach for indoor localization has two phases. In an offline phase, location-tagged wireless signal signatures, i.e., fingerprints, at known indoor locations are captured along a path and stored in a database. Each fingerprint in the database consists of a location and wireless signal characteristics, e.g., received signal strength (RSSI; which varies as a function of distance from the WAP), from visible WAPs at that location. This phase requires great manual effort of collecting several fingerprints at each location and comes at considerable cost. In the online phase, the observed RSS on the user’s mobile device is used to query the fingerprint database and determine location (potentially after some interpolation). Such WiFi-based fingerprinting is a promising building block for low-cost indoor localization with mobile devices.
Unfortunately, there are many unaddressed challenges before a viable WiFi fingerprinting based solution can be realized: (i) the algorithms used for the matching of fingerprints in the online phase have a major impact on accuracy, however the limited CPU/memory/battery resources in mobile devices requires careful algorithm design and deployment that can trade-off accuracy, energy-efficiency, and performance (localization decision latency); (ii) the diversity of mobile devices poses another challenge as smartphones from different vendors may have varying device characteristics leading to different fingerprints being captured at the same location; (iii) security vulnerabilities due to unintentional or intentional WiFi jamming and spoofing attacks can create significant errors which must be overcome; and (iv) short-term and long-term variations in WAP power levels and the indoor environments (e.g., adding/moving furniture, equipment, changes in density of people) can also introduce errors during location estimation, that often corrected by the expensive collecting new fingerprints.
In this dissertation, we propose a new real-time machine learning based framework called SARTHI that addresses all of the abovementioned key challenges towards realizing a viable indoor localization solution with smart mobile devices. To enable energy-efficient enhancements in localization accuracy, SARTHI includes lightweight yet powerful machine learning algorithms with a focus on achieving a balance between battery life and response time. To enable device heterogeneity resilience, we analyzed and identified device diversity invariant pattern matching metrics that can be incorporated into a variety of machine learning based indoor localization frameworks. SARTHI also addresses the challenges associated with the security of fingerprinting-based indoor localization frameworks in the presence of spoofing and jamming attacks. This is achieved by devising a novel methodology for training and deploying deep-learning algorithms that are specifically designed to be resilient to the vulnerabilities associated with intentional power level variation-based attacks. Finally, SARTHI addresses the challenges associated with short-term and long-term variations in WiFi fingerprints using novel low-overhead relativistic learning-based deep-learning algorithms that can deliver high-accuracy while simultaneously minimizing the fingerprint collection effort in the offline phase.
Adviser: Sudeep Pasricha
Co-Adviser: NA
Non-ECE Member: Shrideep Pallickara, CS
Member 3: Anthony Maciejewski, ECE
Addional Members: H. J. Siegel, ECE
S. Tiku, S. Pasricha, “PortLoc: A Portable Data-driven Indoor Localization Framework for Smartphones,” IEEE Design and Test, vol. 36, no. 5, pp. 18-26, 2019.

S.Tiku, S. Pasricha, B. Notaros, Q. Han, “A Hidden Markov Model based Smartphone Heterogeneity Resilient Portable Indoor Localization Framework,” Journal of Systems Architecture, vol. 108, 2020.

S. Tiku, S. Pasricha, “Overcoming Security Vulnerabilities in Deep Learning Based Indoor Localization on Mobile Devices,” ACM Transactions on Embedded Computing Systems (TECS), vol. 18, no. 6, pp. 114, 2019.

L. Wang, S. Tiku, S. Pasricha, “CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning,” IEEE Embedded System Letters, 2021.

S. Tiku, P. Kale, S. Pasricha, “QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices,” ACM Transactions on Cyber-Physical Systems (TCPS), vol. 5, no. 4, pp. 1-30, 2021.

C. Langlois, S. Tiku, S. Pasricha, “Indoor localization with smartphones,” IEEE Consumer Electronics, vol. 6, no. 4, 2017.

S. Pasricha, J. Doppa, K. Chakrabarty, S. Tiku, D. Dauwe, S. Jin, P. Pande, “Data Analytics Enables Energy-Efficiency and Robustness: From Mobile to Manycores, Datacenters, and Networks,” ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), 2017.

S. Tiku, S. Pasricha, “Energy-Efficient and Robust Middleware Prototyping for Smart Mobile Computing,” IEEE International Symposium on Rapid System Prototyping (RSP), 2017.

A. Mittal, S. Tiku, S. Pasricha, “Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices,” ACM Great Lakes Symposium on VLSI (GLSVLSI), 2018.

S. Tiku, S. Pasricha, B. Notaros, Q. Han, “SHERPA: A Lightweight Smartphone Heterogeneity Resilient Portable Indoor Localization Framework,” IEEE International Conference on Embedded Software and Systems (ICESS), 2019.

S. Tiku, S. Pasricha, “Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices,” IEEE/ACM Design, Automation and Test in Europe (DATE) Conference and Exhibition, 2022.
Program of Study: