PhD Dissertation Defense by TRUONG Quang Hai | Improving The Performance of Wi-Fi Indoor Localization in Both Dense and Unknown Environments

ABOUT THE TALK

Indoor localization is important for various pervasive applications, garnering considerable research attention over recent decades. Despite numerous proposed solutions, the practical application of these methods in real-world environments with high applicability remains challenging. One compelling use case for building owners is the ability to track individuals as they navigate through the building, whether for security, customer analytics, space utilization planning, or other management purposes. However, this task becomes exceedingly difficult in environments with hundreds or thousands of people in motion. Conversely, the need to track oneself’s location is also meaningful from the perspective of individuals traversing in crowded spaces. These use cases are pertinent, such as meeting friends or reaching a preferred store in random malls or shopping centers. Nonetheless, addressing these use cases requires solutions that can be applied in unknown environments without pre-existing knowledge of those environments. Consequently, solutions should not necessitate the installation of complex devices, require extensive maintenance efforts, or rely on detailed environmental knowledge. 

This thesis will address two particularly challenging environments: dense environments with thousands of moving people in non-overlapping areas and unknown environments with no maps, fingerprints, or pre-existing knowledge. The proposed system for dense environment, named DenseTrack, connects devices reported by a Wi-Fi location system with specific video blobs obtained through computationally efficient video data analysis. The experiment results indicate that DenseTrack acquires an average match accuracy of 83% within a 2-person distance, with an average latency of 48 seconds in dense environments. For unknown environments, the thesis presents empirical findings derived from experiments utilizing one-sided RTT, aiming to assess the feasibility of this approach for indoor localization. By addressing the challenges posed by both dense and unknown environments, a comprehensive solution for indoor localization in practical scenarios is achieved.

ABOUT THE SPEAKER

TRUONG Quang Hai is a PhD candidate at the School of Computing and Information Systems at Singapore Management University. Under the supervision of Professor Rajesh Krishna BALAN, his research interests focus on indoor localization using multi-sensor fusion to compensate for the inherent limitations associated with individual sensors. His works mainly focus on dense environments with thousands of moving people and unknown environments where little to no prior knowledge is available.