Mobile Data Traffic Modeling: Revealing Temporal Facets

Abstract : This paper presents a detailed measurement-driven model of mobile data traffic usage of smartphone subscribers, using a large-scale dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers' routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., " when " and " how much " traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in traffic demands and describe implications in network solution evaluation and privacy.
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Computer Networks, Elsevier, 2017, 112, pp.176-193
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Eduardo Mucelli Rezende Oliveira, Aline Carneiro Viana, Kolar Purushothama Naveen, Carlos Sarraute. Mobile Data Traffic Modeling: Revealing Temporal Facets. Computer Networks, Elsevier, 2017, 112, pp.176-193. 〈hal-01453379〉

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