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Conference Paper User Mobility Synthesis based on Generative Adversarial Networks: A Survey
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Authors
Seungjae Shin, Hongseok Jeon, Chunglae Cho, Seunghyun Yoon, Taeyeon Kim
Issue Date
2020-02
Citation
International Conference on Advanced Communications Technology (ICACT) 2020, pp.94-103
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.23919/ICACT48636.2020.9061335
Abstract
User mobility characteristics are considered to significantly affect the performance and UX of modern IT systems such as mobile communications, context-aware services, location-based applications, smart mobility, and so on. Due to privacy concerns, legal issues, and expensive measurement costs for real user mobility traces, synthetic mobility traces are widely used for research and development purposes. Until now, most of the user mobility synthesis approaches have been categorized into two major paradigms: stochastic modeling and simulation. However, they naturally have a limitation in mimicking actual user movements because it is impossible to capture all temporal, spatial, and behavioral characteristics that are implicitly melted in real user mobility. Along with the recent advancements of deep learning technologies, there have been several challenging proposals that exploit the power of the deep learning, especially generative models, in mobility synthesis. This paper reviews and summarizes recently proposed user mobility synthesis schemes based on generative adversarial networks that have been one of the most leading deep learning technologies for the last few years.
KSP Keywords
Context-aware service, Development purposes, Generative models, IT systems, Learning Technology, Mobility Traces, Mobility characteristics, Privacy concerns, Smart Mobility, Stochastic modeling and simulation, User mobility