Message Transmission Scheme Based on the Detection of Interest Community in Mobile Social Networks

  • Ying Cai
  • Linqing Hou
  • Yanfang Fan
  • Ruoyu Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


The storage-carrying-forwarding of messages of the node is a way of short-distance communication in the mobile social networks, and the transmission performance is the key factor that affects the user interaction experience. If the user can transmit the message according to the interest or the community, the transmission performance can be improved. For the short-distance communication in the mobile social networks, the existing research is mainly either interest-based or community-based transmission. In order to make users to have a better interactive experience, we proposed InComT (Interest Community based Transmission) which combines the user interest with the community. We measure the interest value of a node in the mobile social networks, and the community is divided according to its interest value to determine the whole community interest value. Then the relay community and the relay node are selected by the interest value to realize the transmission of the message. The simulation results show that the scheme can get a higher transmission success rate with low transmission overhead and low average delay.


Interest community Detection Mobile Social Networks (MSNs) 



This work was supported by the National Natural Science Foundation of China under Grant 61672106 and by Governmental Special Funds to Promote Regional Development of Science and Technology under Grant Z171100004717002.


  1. 1.
    Xiao, Y., Rayi, V., Sun, B., Du, X., Hu, F., Galloway, M.: A survey of key management schemes in wireless sensor networks. J. Comput. Commun. 30(11–12), 2314–2341 (2007)CrossRefGoogle Scholar
  2. 2.
    Du, X., Xiao, Y., Guizani, M., Chen, H.H.: An effective key management scheme for heterogeneous sensor networks ad hoc networks. Elsevier 5(1), 24–34 (2007)Google Scholar
  3. 3.
    Du, X., Guizani, M., Xiao, Y., Chen, H.H.: A routing-driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks. IEEE Trans. Wirel. Commun. 8(3), 1223–1229 (2009)CrossRefGoogle Scholar
  4. 4.
    Index, Cisco Visual Networking. Global Mobile Data Traffic Forecast Update 2015–2020 White Paper, February 2016.
  5. 5.
    Hu, X., Chu, T.H.S., Leung, V.C.M., et al.: A survey on mobile social networks: applications, platforms, system architectures, and future research directions. IEEE Commun. Surv. Tutor. 17, 1557–1581 (2015)CrossRefGoogle Scholar
  6. 6.
    Vastardis, N., Yang, K.: Mobile social networks: architectures, social properties, and key research challenges. IEEE Commun. Surv. Tutor. 15, 1355–1371 (2013)CrossRefGoogle Scholar
  7. 7.
    Zhu, Y., Xu, B., Shi, X., et al.: A survey of social based routing in delay tolerant networks: positive and negative social effects. IEEE Commun. Surv. Tutor. 15, 387–401 (2013)CrossRefGoogle Scholar
  8. 8.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)CrossRefGoogle Scholar
  9. 9.
    Hui, P., Yoneki, E., Chan, S.Y., Crowcroft, J.: Distributed community detection in delay tolerant networks. In: Workshops -2nd ACM International Workshop on Mobility in the Evolving Internet Architecture, pp. 1–8 (2007)Google Scholar
  10. 10.
    Nguyen, N.P., Dinh, T.N., Tokala, S., et al.: Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, pp. 85–96. ACM (2011)Google Scholar
  11. 11.
    Fortunato, S., Castellano, C.: Community structure in graphs. In: Meyers, R. (ed.) Computational Complexity, pp. 490–512. Springer, New York (2012). Scholar
  12. 12.
    Macropol, K., Singh, A.: Scalable discovery of best clusters on large graphs. Proc. VLDB Endow. 3, 693–702 (2010)CrossRefGoogle Scholar
  13. 13.
    Xie, J., Szymanski, B.K., Liu, X.; SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 344–349 (2011)Google Scholar
  14. 14.
    Williams, M.J., Whitaker, R.M., Allen, S.M.: Decentralised detection of periodic encounter communities in opportunistic networks. Ad Hoc Netw. 10, 1544–1556 (2012)CrossRefGoogle Scholar
  15. 15.
    Chen, Q., Wu, T.T., Fang, M.: Detecting local community structures in complex networks based on local degree central nodes. Phys. Stat. Mech. Appl. 392, 529–537 (2013)CrossRefGoogle Scholar
  16. 16.
    Rhouma, D., Ben Romdhane, L.: An efficient algorithm for community mining with overlap in social networks. Expert Syst. 41, 4309–4321 (2014)CrossRefGoogle Scholar
  17. 17.
    Wei, K., Liang, X., Xu, K.: A survey of social-aware routing protocols in delay tolerant networks: applications, taxonomy and design-related issues. IEEE Commun. Surv. Tutor. 16, 556–578 (2014)CrossRefGoogle Scholar
  18. 18.
    Du, X., Chen, H.H.: Security in wireless sensor networks. IEEE Wirel. Commun. Mag. 15(4), 60–66 (2008)CrossRefGoogle Scholar
  19. 19.
    Du, X., Guizani, M., Xiao, Y., Chen, H.H.: Secure and efficient time synchronization in heterogeneous sensor networks. IEEE Trans. Veh. Technol. 57(4), 2387–2394 (2008)CrossRefGoogle Scholar
  20. 20.
    Du, X., Xiao, Y., Chen, H.H., Wu, Q.: Secure cell relay routing protocol for sensor networks. Wirel. Commun. Mob. Comput. 6(3), 375–391 (2006)CrossRefGoogle Scholar
  21. 21.
    Hei, X., Du, X., Wu, J., Hu, F.: Defending resource depletion attacks on implantable medical devices. In: Proceedings of IEEE GLOBECOM 2010, Miami, Florida, USA, December 2010Google Scholar
  22. 22.
    Hei, X., Du, X.: Biometric-based two-level secure access control for implantable medical devices during emergency. In: Proceedings of IEEE INFOCOM 2011, Shanghai, China, April 2011Google Scholar
  23. 23.
    Vahdat, A., Becker, D.: Epidemic routing for partially connected ad hoc networks (2000)Google Scholar
  24. 24.
    Lindgren, A., Doria, A., Schelén, O.: Probabilistic routing in intermittently connected networks. In: Dini, P., Lorenz, P., de Souza, J.N. (eds.) SAPIR 2004. LNCS, vol. 3126, pp. 239–254. Springer, Heidelberg (2004). Scholar
  25. 25.
    Spyropoulos, T., Psounis, K., Raghavendra, C.S.: Spray and wait: an efficient routing scheme for inter-mittently connected mobile networks. In: Proceedings of the ACM SIGCOMM Workshop, pp. 252–259 (2005)Google Scholar
  26. 26.
    Daly, E.M., Haahr, M.: Social network analysis for routing in disconnected delay-tolerant manets. In: Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 32–40. ACM (2007)Google Scholar
  27. 27.
    Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: social-based forwarding in delay tolerant networks. ACM MobiHoc (2008)Google Scholar
  28. 28.
    Gao, W., Li, Q., Zhao, B., et al.: Multicasting in delay tolerant networks: a social network perspective. In: Proceedings of the Tenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 299–308. ACM (2009)Google Scholar
  29. 29.
    Ioannidis, S., Chaintreau, A., Massoulié, L.: Optimal and scalable distribution of content updates over a mobile social network. In: INFOCOM 2009, pp. 1422–1430 (2009)Google Scholar
  30. 30.
    Moghadam, A., Schulzrinne, H.: Interest-aware content distribution protocol for mobile disruption-tolerant networks. In: IEEE International Symposium, pp. 1–7 (2009)Google Scholar
  31. 31.
    Mtibaa, A., May, M., Diot, C., et al.: Peoplerank: social opportunistic forwarding. In: Infocom Proceedings IEEE, pp. 1–5 (2010)Google Scholar
  32. 32.
    Hui, P., Crowcroft, J., Yoneki, E.: Bubble-rap: social-based forwarding in delay-tolerant networks. IEEE Trans. Mob. Comput. 10(11), 1576–1589 (2011)CrossRefGoogle Scholar
  33. 33.
    Gao, W., Cao, G.: User-centric data dissemination in disruption tolerant networks. In: INFOCOM IEEE, pp. 3119–3127 (2011)Google Scholar
  34. 34.
    Lin, K.C.J., Chen, C.W., Chou, C.F.: Preference aware content dissemination in opportunistic mobile social networks. In: INFOCOM Proceedings IEEE, pp. 1960–1968 (2012)Google Scholar
  35. 35.
    Wu, J., Wang, Y.: Social feature-based multi-path routing in delay tolerant networks. In: INFOCOM, Proceedings IEEE, pp. 1368–1376 (2012)Google Scholar
  36. 36.
    Xu, Y., Chen, X.: Social-similarity-based multicast algorithm in impromptu mobile social networks. In: Global Communications Conference (GLOBECOM), pp. 346–351. IEEE (2014)Google Scholar
  37. 37.
    Didwania, A., Narmawala, Z.: A comparative study of various community detection algorithms in the mobile social network. In: Engineering Nui-CONE 5th Nirma University International Conference, pp. 1–6. IEEE (2015)Google Scholar
  38. 38.
    Mao, Z., Jiang, Y., Min, G., et al.: Mobile social networks: design requirements, architecture, and state-of-the-art technology. Comput. Commun. 100, 1–19 (2017)CrossRefGoogle Scholar
  39. 39.
    Keränen, A., Ott, J., Kärkkäinen, T.: The one simulator for dtn protocol evaluation. In: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, p. 55. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ying Cai
    • 1
  • Linqing Hou
    • 1
  • Yanfang Fan
    • 1
  • Ruoyu Chen
    • 1
  1. 1.School of Computer Beijing Information Science and Technology UniversityBeijingChina

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