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

[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