Kalamkas' thesis has addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. Previous research works were primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones, which is power inefficient and privacy-invasive.
Today is a significant day for Kalamkas Zhagyparova, a master's student in Electrical and Computer Engineering, as she successfully defended her thesis titled "Transportation Mode Recognition based on Cellular Network Data." The master's defense took place at the Information Science Lab (ISL) in King Abdullah University of Science and Technology (KAUST), where Kalamkas presented her extensive research in the field of transportation mode recognition and the challenges related to utilizing cellular network data.
Therefore, in her study, she presents a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. The underlying concept is to correlate phone speed with features extracted from Channel State Information. She developed a system using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.
This remarkable achievement could not have been possible without the guidance and support of her supervisor, Prof. Tareq Y. Al-Naffouri, and the valuable contributions from Dr. Nour Kouzayha, Dr. Hesham ElSawy, and Dr. Ahmed Bader, who provided invaluable assistance as senior research staff members, enriching the quality of Kalamkas' work. We extend our heartfelt congratulations to Kalamkas for this accomplishment, and we eagerly anticipate her continued success in her future endeavors.