research-article

Characterizing and Predicting Individual Traffic Usage of Mobile Application in Cellular Network

Authors:

Depeng JinAuthors Info & Claims

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Pages 852 - 861

https://doi.org/10.1145/3267305.3274173

Published: 08 October 2018 Publication History

Abstract

The proliferation of smart devices prompts the explosive usage of mobile applications, which increases network traffic load. Characterizing the application level traffic patterns from an individual perspective is valuable for operators and content providers to make technical and business strategies. In this paper, we identify several typical traffic patterns and predict per-user traffic demand utilizing application usage dataset in cellular network. Our primary contributions are twofold: First, we novelly designed a three-stage model combining factor analysis and machine learning to extract the traffic patterns of individuals. By detecting the latent temporal structure of their application usage, users in the network are grouped into six typical patterns. Second, we implement a Wavelet-ARMA based model to forecast per-user application level traffic demand. The evaluation on real-world dataset indicates the model improves the prediction accuracy by 7 to 8 times compared with the benchmark solutions.

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Cited By

Zanella ABazco-Nogueras AZiemlicki CFiore MMontpetit MLeivadeas AUhlig SJaved M(2023)Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale MeasurementsProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624825(696-709)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624825
Attanasio GFiandrino CFiore MWidmer JLudant NBloessl BKousias KAlay ÖJacquot LStanica R(2023)In-depth study of RNTI management in mobile networksComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109428219:COnline publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1016/j.comnet.2022.109428
Long PLi JLiu NPan ZYou X(2022)Antenna On/Off Strategy for Massive MIMO Based on User Behavior Prediction2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)10.1109/CNIOT55862.2022.00027(113-119)Online publication date: May-2022
https://doi.org/10.1109/CNIOT55862.2022.00027
Show More Cited By

Index Terms

Characterizing and Predicting Individual Traffic Usage of Mobile Application in Cellular Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing

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cover image ACM Conferences

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

October 2018

1881 pages

ISBN:9781450359665

DOI:10.1145/3267305

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2018

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UbiComp '18

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SIGCHI

UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Zanella ABazco-Nogueras AZiemlicki CFiore MMontpetit MLeivadeas AUhlig SJaved M(2023)Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale MeasurementsProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624825(696-709)Online publication date: 24-Oct-2023
https://dl.acm.org/doi/10.1145/3618257.3624825
Attanasio GFiandrino CFiore MWidmer JLudant NBloessl BKousias KAlay ÖJacquot LStanica R(2023)In-depth study of RNTI management in mobile networksComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109428219:COnline publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1016/j.comnet.2022.109428
Long PLi JLiu NPan ZYou X(2022)Antenna On/Off Strategy for Massive MIMO Based on User Behavior Prediction2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)10.1109/CNIOT55862.2022.00027(113-119)Online publication date: May-2022
https://doi.org/10.1109/CNIOT55862.2022.00027
Walelgne EAsrese AManner JBajpai VOtt J(2021)Understanding Data Usage Patterns of Geographically Diverse Mobile UsersIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303750318:3(3798-3812)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2020.3037503
Liu CWu TLi ZWang B(2021)Individual traffic prediction in cellular networks based on tensor completionInternational Journal of Communication Systems10.1002/dac.495234:16Online publication date: 9-Aug-2021
https://doi.org/10.1002/dac.4952
Qin ZCao FYang YWang SLiu YTan CZhang D(2020)CellPredProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33809824:1(1-24)Online publication date: 18-Mar-2020
https://dl.acm.org/doi/10.1145/3380982
Ting LKoketsu Rodrigues TKato NChuang K(2020)Prediction of Network Traffic Load on High Variability Data Based on Distance Correlation2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)10.1109/VTC2020-Fall49728.2020.9348769(1-5)Online publication date: Nov-2020
https://doi.org/10.1109/VTC2020-Fall49728.2020.9348769
Zhu QSun L(2020)Big Data Driven Anomaly Detection for Cellular NetworksIEEE Access10.1109/ACCESS.2020.29732148(31398-31408)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973214
Li YMa ZPan ZLiu NYou X(2020)Prophet model and Gaussian process regression based user traffic prediction in wireless networksScience China Information Sciences10.1007/s11432-019-2695-663:4Online publication date: 9-Mar-2020
https://doi.org/10.1007/s11432-019-2695-6
Malik SAkram HGill SPervaiz HMalik H(2020) EFFORT : Energy efficient framework for offload communication in mobile cloud computing Software: Practice and Experience10.1002/spe.285051:9(1896-1909)Online publication date: 31-May-2020
https://doi.org/10.1002/spe.2850
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