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Understanding Mobile Data Demand regarding Mobility: The report for mid-term thesis evaluation

Guangshuo Chen 1 
1 INFINE - INFormation NEtworks
Inria Saclay - Ile de France
Abstract : Smartphones are supposedly the fastest-spreading technology in human history. Global mobile data traffic has a growth of 74% in 2015, and is predicted to have an eightfold increase in 2020. Hence the understanding of subscriber’s mobile data demand is of great significance for solutions managing the increasing data traffic as well as improving quality of communication service. A core problem in understanding mobile data demand is to what degree is mobile data traffic predictable? We explore the predictability of data volume for individuals. Specifically, our goal is to determine the maximum probability of forecasting data volume for each subscriber. To this end, we mine a large-scale mobile dataset with both voice traffic and data traffic, construct a dataset of time series of data volume and explore the upper bound of predictability hidden in the time series. We find a overall > 90% of predictability hidden in individual’s time series of data volume.
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Submitted on : Tuesday, May 31, 2016 - 1:24:34 PM
Last modification on : Wednesday, February 2, 2022 - 3:57:55 PM
Long-term archiving on: : Thursday, September 1, 2016 - 11:40:05 AM


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  • HAL Id : hal-01323916, version 1


Guangshuo Chen. Understanding Mobile Data Demand regarding Mobility: The report for mid-term thesis evaluation. [Technical Report] INRIA Saclay. 2016. ⟨hal-01323916⟩



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