skip to main content
10.1145/2757513.2757518acmconferencesArticle/Chapter ViewAbstractPublication PagesmobihocConference Proceedingsconference-collections
research-article

Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-scale Cellular Data Networks

Published: 22 June 2015 Publication History

Abstract

As the volume of mobile traffic has been growing quickly in recent years, reducing the congestion of mobile networks has become an important problem of networking research. Researchers found out that the inhomogeneity in the spatio-temporal distribution of the data traffic leads to extremely insufficient utilization of network resources. Thus, it is important to fundamentally understand this distribution to help us make better resource planning or introduce new management tools such as time-dependent pricing to reduce the congestion. However, due to the requirement of a large dataset, a detailed, radical and credible network-wide study for the spatio-temporal distribution of mobile traffic is still lacking. In this work, we conduct such a measurement study. Base on a large-scale data set obtained from 380,000 base stations in Shanghai spanning over one month, we quantitatively characterize the spatio-temporal distribution of mobile traffic and present a detailed visualized analysis. Furthermore, on the basis of quantitative analysis, we find that the mobile traffic loads uniformly follow a trimodal distribution, which is the combination of compound-exponential, power-law and exponential distributions, in terms of both spatial and temporal dimension. Extensive results show that our model is with accuracy over 99%, which provides fundamental and credible guidelines for the practical solutions of the issues in mobile traffic operations.

References

[1]
Cisco Visual Networking Index, White Paper, Feb. 2015. available online at http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vn/white paper c11-520862.pdf
[2]
D. Lee, S. Zhou, X. Zhong, Z. Niu, X. Zhou, and H. Zhang, "Spatial modeling of the traffic density in cellular networks," Wireless Communications, IEEE, vol. 21, no. 1, pp. 80--88, 2014.
[3]
U. Gotzner, and R. Rathgeber, "Spatial Traffic Distribution in Cellular Networks," in Proc. IEEE VTC, 1998, pp. 1994--1998.
[4]
M. Michalopoulou, J. Riihijarvi, and P. Mahonen, "Towards characterizing primary usage in cellular networks: A traffic-based study," In Proc. DySPAN, 2011, pp. 652--655
[5]
S. Sen, C. Joe-Wong, S. Ha, and M. Chiang,"Incentivizing timeshifting of data: a survey of time-dependent pricing for internet access," Communications Magazine, IEEE, vol. 50, no. 11, pp. 91--99, 2012.
[6]
M. Laner, P. Svoboda, S. Schwarz, "Users in cells: a data traffic analysis," Proc. IEEE WCNC, 2012, pp. 3063--3068.
[7]
C. Peng, S. B. Lee, S. Lu, "Traffic-driven power saving in operational 3G cellular networks," in Proc. ACM MOBICOM, 2011, pp. 121--132.
[8]
E. Nan, X. Chu, W. Guo, "User data traffic analysis for 3G cellular networks," in Proc. IEEE CHINACOM, 2013, pp. 468--472.

Cited By

View all
  • (2024)Multi-Feature Traffic Prediction Based on Signaling Information for Cellular NetworkIEEE Transactions on Vehicular Technology10.1109/TVT.2023.331935173:2(2280-2291)Online publication date: Feb-2024
  • (2024)Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2022.322644823:1(469-482)Online publication date: Jan-2024
  • (2024)Optimizing UE Power Efficiency: AI/ML Approach for Upgrade Time DeterminationIEEE Access10.1109/ACCESS.2024.343900812(122878-122887)Online publication date: 2024
  • Show More Cited By

Index Terms

  1. Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-scale Cellular Data Networks

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking
        June 2015
        62 pages
        ISBN:9781450335171
        DOI:10.1145/2757513
        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]

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 22 June 2015

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. measurement study
        2. mobile data traffic
        3. spatio-temporal inhomogeneity
        4. trimodal distribution.

        Qualifiers

        • Research-article

        Conference

        MobiHoc'15
        Sponsor:

        Acceptance Rates

        HOTPOST '15 Paper Acceptance Rate 5 of 10 submissions, 50%;
        Overall Acceptance Rate 5 of 10 submissions, 50%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)10
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 13 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)Multi-Feature Traffic Prediction Based on Signaling Information for Cellular NetworkIEEE Transactions on Vehicular Technology10.1109/TVT.2023.331935173:2(2280-2291)Online publication date: Feb-2024
        • (2024)Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2022.322644823:1(469-482)Online publication date: Jan-2024
        • (2024)Optimizing UE Power Efficiency: AI/ML Approach for Upgrade Time DeterminationIEEE Access10.1109/ACCESS.2024.343900812(122878-122887)Online publication date: 2024
        • (2023)Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure EventsTechnometrics10.1080/00401706.2023.223149166:1(77-89)Online publication date: 24-Jul-2023
        • (2023)Identify spatio-temporal properties of network traffic by model checkingThe Journal of Supercomputing10.1007/s11227-023-05388-979:16(18886-18909)Online publication date: 20-May-2023
        • (2022)Integrating Stochastic Geometry and ON/OFF Traffic Models: Toward Spatio-Temporal Analysis of Wireless Networks With Heterogeneous ServicesIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.31493889:3(1668-1679)Online publication date: 1-May-2022
        • (2022)Data-Efficient Communication Traffic Prediction With Deep Transfer LearningICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9838413(3190-3195)Online publication date: 16-May-2022
        • (2021)Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data AnalyticsIEEE Transactions on Mobile Computing10.1109/TMC.2021.3099979(1-1)Online publication date: 2021
        • (2021)STEP: A Spatio-Temporal Fine-Granular User Traffic Prediction System for Cellular NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2020.300122520:12(3453-3466)Online publication date: 1-Dec-2021
        • (2021)Time-Wise Attention Aided Convolutional Neural Network for Data-Driven Cellular Traffic PredictionIEEE Wireless Communications Letters10.1109/LWC.2021.307874510:8(1747-1751)Online publication date: Aug-2021
        • Show More Cited By

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media