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Graduate Exam Abstract


Luoyang Fang

Ph.D. Preliminary
May 8, 2018, 10:00 am - 12:00 pm
Civil Environmental Engineering Conference Room
Spatiotemporal Modeling in Mobile Big Data

Abstract: With the explosive growth of mobile devices, mobile big data attracts significant attention from various research communities. The spatiotemporal information revealed by large-scale mobile network subscribers is valuable in many fields, highlighted as one of the most distinct characteristics in mobile big data. In this thesis, the spatiotemporal modeling of mobile big data is studied in terms of individual mobility behaviors and aggregated crowd behaviors. The spatiotemporal modeling on individual mobility behavior is explored in the context of subscriber privacy evaluation via user identification across two data collection time periods. The user identification can be regarded as an identity deanonymization task based on the spatiotemporal patterns of subscriber’s mobility behaviors. The aggregated spatiotemporal modeling is investigated in the context of demand forecasting for cellular network management, which could effectively take advantage of relevancy information among cells and improve the forecasting performance.

Adviser: Dr. Liuqing Yang
Co-Adviser: N/A
Non-ECE Member: Dr. Haonan Wang, Statistics
Member 3: Dr. J. Rockey Luo, ECE
Addional Members: Dr. Anura P Jayasumana, ECE

Publications:
[1] L. Fang, H. Wang, X. Cheng and L. Yang, “Mobile Privacy: User Identification via Ensemble Matching on Spatiotemporal Features,” IEEE Transactions on Information Forensics and Security (Submitted)
[2] L. Fang, X. Cheng, L. Yang and H. Wang, “Location Privacy in Mobile Big Data: User Identifiability via Habitat Region Representation” Journal of Communication and Information Networks (Submitted)
[3] L. Fang, H.Wang, X. Cheng and L. Yang, “Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks,” IEEE Internet of Things Journal (Accepted)
[4] X. Cheng, L. Fang, L. Yang and S. Cui, "Mobile Big Data: The Fuel for Data-Driven Wireless," in IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1489-1516, Oct. 2017.
[5] X. Cheng, L. Fang, X. Hong and L. Yang, "Exploiting Mobile Big Data: Sources, Features, and Applications," in IEEE Network, vol. 31, no. 1, pp. 72-79, January/February 2017.


Program of Study:
CS 545
ECE 514
ECE 516
ECE 520
ECE 652
ECE 658
ECE 612
ECE 651