Buffer Occupation in Wireless Social Networks

  • Tuo Yu
  • Xiaohua Tian
  • Feng Yang
  • Xinbing Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7992)


In this paper, we investigate the buffer occupation in wireless social networks. n source nodes are randomly distributed in the networks, and each of them chooses several friend nodes, whose number follows power-law distribution. Two different strategies are proposed to achieve optimal buffer occupation or throughput, and the upper bound and lower bound for buffer size and throughput are also investigated. The results show that there exists trade-off between buffer occupation and throughput, and we also find that the buffer occupation for each node will not increase when the size of network becomes larger, which is opposite to our intuitive understanding.


Wireless Network Time Slot Span Tree Source Node Destination Node 
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  1. 1.
    Li, J., Blake, C., De Couto, D.S.J., Lee, H.I., Morris, R.: Capacity of ad hoc wireless networks. In: International Conference on Mobile Computing and Networking, pp. 61–69 (2001)Google Scholar
  2. 2.
    Azimdoost, B., Sadjadpour, H.R., Garcia Luna-Aceves, J.J.: Capacity of composite networks: combining social and wireless ad hoc networks. In: Proc. 2011 IEEE Wireless Communications and Networking Conference, pp. 464–468 (2011)Google Scholar
  3. 3.
    Ning, T., Yang, Z., Wu, H., Han, Z.: Self-Interest-Drive Incentives for Ad Dissemination in Autonomous Mobile Social Networks. In: IEEE International Conference on Computer Communications, INFOCOM, Turin, Italy (April 2013)Google Scholar
  4. 4.
    Niyato, D., Han, Z., Saad, W., Hjorungnes, A.: A Controlled Coalitional Game for Wireless Connection Sharing and Bandwidth Allocation in Mobile Social Networks. In: IEEE Globe Communication Conference, Miami, FL (November-December 2010)Google Scholar
  5. 5.
    Zhang, B., Xing, K., Cheng, X., Huang, L., Bie, R.: Traffic Clustering and Online Traffic Prediction in Vehicle Networks: A Social Influence Perspective. In: IEEE INFOCOM, March 25-30, pp. 495–503 (2012)Google Scholar
  6. 6.
    Quercia, D., Lathia, N., Calabrese, F., Di Lorenzo, G., Crowcroft, J.: Recommending social events from mobile phone location data. In: IEEE 10th Int. Data Mining (ICDM) Conf., 2010, pp. 971–976 (2010)Google Scholar
  7. 7.
    Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the social web. In: Proc. 2nd Int. Cognitive Information Processing (CIP) Workshop, pp. 411–416 (2010)Google Scholar
  8. 8.
    Wen, H., Liu, J., Lin, C., Li, P., Fang, Y., Ren, F.: A Storage-friendly Routing Scheme in Intermittently Connected Mobile Network. IEEE Transactions on Vehicular Technology 60(3), 1138–1149 (2011)CrossRefGoogle Scholar
  9. 9.
    Herdtner, J.D., Chong, E.: Throughput-Storage Tradeoff in Ad Hoc Networks. In: IEEE INFOCOM (2005)Google Scholar
  10. 10.
    Bodas, S., Shakkottai, S., Ying, L., Srikant, R.: Scheduling in Multi-Channel Wireless Networks: Rate Function Optimality in the Small-Buffer Regime. In: Proceedings of the ACM SIGMETRICS/Performance Conference (June 2009)Google Scholar
  11. 11.
    Wang, X., Yu, T., Xu, Y.: Lower Bound for Node Buffer Size in Intermittently Connected Wireless Networks. IEEE Transactions on Parallel and Distributed Systems 99(1) (2012)Google Scholar
  12. 12.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: ACM IMC, New York, NY, USA, pp. 29–42 (2007)Google Scholar
  13. 13.
    Ahn, Y.-Y., Han, S., Kwak, H., Moon, S., Jeong, H.: Analysis of topological characteristics of huge online social networking services. In: Proc. of ACM WWW, New York, NY, USA, pp. 835–844 (2007)Google Scholar
  14. 14.
    Yan, Y., Huang, J., Wang, J.: Dynamic Bargaining for Relay-Based Cooperative Spectrum Sharing. IEEE Journal on Selected Areas in Communications 31(8), 1–14 (2013)Google Scholar
  15. 15.
    Wu, C., Mohsenian-Rad, A.H., Huang, J.: Vehicle-to-Aggregator Interaction Game. IEEE Transactions on Smart Grid 3(1), 434–442 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhu, X., Li, P., Fang, Y., Wang, Y.: Throughput and Delay in Cooperative Wireless Networks With Partial Infrastructure. IEEE Transactions on Vehicular Technology 58(8), 4620–4627 (2009)CrossRefGoogle Scholar
  17. 17.
    Zhang, B., Cheng, X., Bie, R., Chen, D.: A Community Based Vaccination Strategy Over Mobile Phone Records. In: ACM MHealthSys. (2012)Google Scholar
  18. 18.
    Xu, Y., Wang, X.: Fundamental Lower Bound for Node Buffer Size in Intermittently Connected Wireless Networks. In: IEEE INFOCOM, Shanghai, China, pp. 972–980 (2011)Google Scholar
  19. 19.
    Stuijk, S., Geilen, M., Basten, T.: Exploring trade-offs in buffer requirements and throughput constraints for synchronous dataflow graphs. In: Proceedings of the 43rd Annual Design Automation Conference, pp. 899–904 (2006)Google Scholar
  20. 20.
    Wang, C., Li, X., Jiang, C., Tang, S., Liu, Y., Zhao, J.: Scaling Laws on Multicast Capacity of Large Scale Wireless Networks. In: IEEE INFOCOM, pp. 1863–1871 (2009)Google Scholar
  21. 21.
    Franceschetti, M., Dousse, O., Tse, D.N.C., Thiran, P.: Closing the Gap in the Capacity of Wireless Networks Via Percolation Theory. IEEE Transactions on Information Theory 53, 1009–1018 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Grimmett, G.R.: Percolaiton. Springer (1999)Google Scholar
  23. 23.
    Penrose, M.: Random Geometric Graphs. Oxford University Press, New York (2003)zbMATHCrossRefGoogle Scholar
  24. 24.
    El Gammal, A., Mammen, J., Prabhakar, B., Shah, D.: Throughput-Delay Trade-off in Wireless Networks. In: Proc. IEEE INFOCOM, Hong Kong, pp. 464–475 (2004)Google Scholar
  25. 25.
    Xue, F., Kumar, P.R.: Scaling laws for ad hoc wireless networks: an information theoretic approach. Found. Trends Netw. 1(2), 145–270 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tuo Yu
    • 1
  • Xiaohua Tian
    • 1
  • Feng Yang
    • 1
  • Xinbing Wang
    • 1
  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina

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