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Performance Analysis for Content Distribution in Crowdsourced Content-Centric Mobile Networking

  • Chengming Li
  • Xiaojie Wang
  • Shimin Gong
  • Zhi-Hui Wang
  • Qingshan Jiang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)

Abstract

Content-Centric Networking emerges as a promising paradigm which has a better content distribution efficiency and mobility via named data and in-network caching compared with the IP-based network. However, providing a high quality of experience in content distribution of Content-Centric Mobile Networking (CCMN) is challenging due to the heterogeneous networks, varying wireless channel conditions and incentive strategies to mobile users. In this work, we propose a novel crowdsourced content distribution framework for CCMN. This framework enables the nearby mobile users to crowdsource their caching resources and radio links for cooperative content distribution. We formulate the problem as the maximization of the payoff of all users which considers content retrieve time and energy cost. Further, we analysis the upper bound and lower bound of the proposed system in term of user payoff, which can be a benchmark for the future scheduling algorithms and incentive mechanisms design.

Keywords

Information-Centric Networks Content-Centric Networking Mobile crowdsourcing In-network caching 

Notes

Acknowledgment

This work is supported in part by National Nature Science Foundation of China under grant No. 61602462 and No. 61601449, and supported in part by Shenzhen Science and Technology Foundation under grant No. JCYJ20150630114942277 and No. JSGG20160229123657040.

References

  1. 1.
    Jiang, X., Jun, B.I., Nan, G., Zhaogeng, L.I.: A survey on information-centric networking:rationales, designs and debates. China Commun. 12(7), 1–12 (2015)CrossRefGoogle Scholar
  2. 2.
    Ning, Z., Hu, X., Chen, Z., Zhou, M., Hu, B., Cheng, J., Obaidat, M.: A cooperative quality-aware service access system for social internet of vehicles. IEEE Internet Things J. (2017).  https://doi.org/10.1109/JIOT.2017.2764259
  3. 3.
    Ning, Z., Xia, F., Ullah, N., Kong, X., Hu, X.: Vehicular social networks: enabling smart mobility. IEEE Commun. Mag. 55(5), 49–55 (2017)CrossRefGoogle Scholar
  4. 4.
    Cisco: Cisco visual networking index: global mobile data traffic forecast update, 2016–2021. CISCO White Paper (2017)Google Scholar
  5. 5.
    Jacobson, V., Smetters, D., Thornton, J., Plass, M., Briggs, N., Braynard, R.: Networking named content. In: Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, pp. 1–12. ACM (2009)Google Scholar
  6. 6.
    Su, Z., Xu, Q.: Content distribution over content centric mobile social networks in 5G. IEEE Commun. Mag. 53(6), 66–72 (2015)CrossRefGoogle Scholar
  7. 7.
    Tourani, R., Misra, S., Mick, T.: IC-MCN: an architecture for an information-centric mobile converged network. IEEE Commun. Mag. 54(9), 43–49 (2016)CrossRefGoogle Scholar
  8. 8.
    Lee, J., Jeon, S.: Low overhead smooth mobile content sharing using content centric networking (CCN). IEICE Trans. Commun. 94(10), 2751–2754 (2011)CrossRefGoogle Scholar
  9. 9.
    Han, B., Wang, X., Choi, N., Kwon, T., Choi, Y.: AMVS-NDN: adaptive mobile video streaming and sharing in wireless named data networking. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 375–380. IEEE (2013)Google Scholar
  10. 10.
    Detti, A., Ricci, B., Blefari-Melazzi, N.: Mobile peer-to-peer video streaming over information-centric networks. Comput. Netw. 81, 272–288 (2015)CrossRefGoogle Scholar
  11. 11.
    Gao, L., Tang, M., Pang, H., Huang, J.: Performance bound analysis for crowdsourced mobile video streaming. In: Conference on Information Science and Systems, pp. 366–371 (2016)Google Scholar
  12. 12.
    Tang, M., Wang, S., Gao, L., Huang, J., Sun, L.: MOMD: a multi-object multi-dimensional auction for crowdsourced mobile video streaming. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications (2017)Google Scholar
  13. 13.
    Joseph, V., de Veciana, G.: NOVA: QoE-driven optimization of dash-based video delivery in networks. In: 2014 Proceedings of INFOCOM, pp. 82–90. IEEE (2014)Google Scholar
  14. 14.
    Balasubramanian, N., Balasubramanian, A., Venkataramani, A.: Energy consumption in mobile phones: a measurement study and implications for network applications. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 280–293. ACM (2009)Google Scholar
  15. 15.
    Manaris, B., Vaughan, D., Wagner, C., Romero, J., Davis, R.B.: Evolutionary music and the Zipf-Mandelbrot law: developing fitness functions for pleasant music. In: Cagnoni, S., Johnson, C.G., Cardalda, J.J.R., Marchiori, E., Corne, D.W., Meyer, J.-A., Gottlieb, J., Middendorf, M., Guillot, A., Raidl, G.R., Hart, E. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 522–534. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-36605-9_48CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Chengming Li
    • 1
  • Xiaojie Wang
    • 2
  • Shimin Gong
    • 1
  • Zhi-Hui Wang
    • 2
  • Qingshan Jiang
    • 1
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of ScienceShenzhenChina
  2. 2.School of SoftwareDalian University of TechnologyDalianChina

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