Cluster Computing

, Volume 22, Supplement 6, pp 13547–13558 | Cite as

An enhanced communication mechanism for partitioned social overlay networks using modified multi-dimensional routing

  • Ahsan HussainEmail author
  • Bettahally N. Keshavamurthy


P2P social overlay networks provide large-scale data-sharing over Internet. The government action or natural calamities can cause social network partitions (SNP) between different geographical areas. The single attribute based routing inside Chord networks over such SNPs becomes ineffective. Thus, we propose a social interest overlay (SIO) network built over the Chord network architecture, which uses modified multi-dimensional routing (mMDR) algorithm. The weight-function of multiple-dimensions, i.e., geographical-location, social-interest and time-zones, is computed in order to obtain best paths inside SNPs. Resultant topological and routing communication probabilities prove that the mMDR algorithm applied for the proposed SIO network, greatly improves communication inside SNPs.


Chord Network Multidimensional routing Network partitioning P2P overlay network Social interest overlay network Social networks 


  1. 1.
    Li, M., Lee, W.C., Sivasubramaniam, A., Lee, D.: Semantic small world: an overlay network for peer-to-peer search. In: Proceedings of the 12th IEEE International Conference on Network Protocols, ICNP, 2010, pp. 228–238Google Scholar
  2. 2.
    Ding, D., Conti, M., Figueiredo, R.: Impact of country-scale Internetdisconnection on structured and social P2P overlays. In: Proceedings of 16th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM, 2015, pp. 1–9Google Scholar
  3. 3.
    Sun, W.J., Qui, H.M.: A social network analysis on blogspheres. In: Proceedings of the 15th IEEE International Conference on Management Science and Engineering, Long Beach, USA, 2008, pp. 1769–1773Google Scholar
  4. 4.
    Zhang, M.: Social network analysis: history, concepts, and research. In: Furht, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 3–21. Springer, Boca Raton (2010)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Li, M., Lee, W.C., Sivasubramaniam. A., Lee, D.: A small world overlay network for semantic based search in P2P systems. In: Proceedings of the 2nd Workshop on Semantics in Peer-to-Peer and Grid Computing, New York, USA, 2004, pp. 71–90Google Scholar
  7. 7.
    Doval, D., O’Mahony, D.: Overlay networks: a scalable alternative for P2P. IEEE Internet Comput. 7(4), 79–82 (2003)CrossRefGoogle Scholar
  8. 8.
    Xie, J., Li, Z., Chen, G.A.: A semantic overlay network for unstructured peer-to-peer protocols. In: Proceedings of the 13th IEEE International Conference on Parallel and Distributed Systems, 2007, vol. 2, pp. 1–8Google Scholar
  9. 9.
    Lua, E.K., Crowcroft, J., Pias, M., Sharma, R., Lim, S.: A survey and comparison of peer-to-peer overlay network schemes. IEEE Commun. Surv. Tutor. 7(2), 72–93 (2005)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Yun, X., Li, Y.: Analyzing the characteristics of gnutella overlays. In: Proceedings of the 4th IEEE International Conference on Information Technology, ITNG, 2007, pp. 1095–1100Google Scholar
  11. 11.
    Rowstron, A., Druschel, P.: Pastry: Scalable, decentralized object location, and routing for large- scale peer-to-peer systems. In: Proceedings of the 18th IFIP/ACM International Conference on Distributed Systems Platforms and Open Distributed Processing, 2001, pp. 329–350CrossRefGoogle Scholar
  12. 12.
    Zhao, B.Y., Huang, L., Stribling, J., Rhea, S.C., Joseph, A.D., Kubiatowicz, J.D.: Tapestry: a resilient global-scale overlay for service deployment. IEEE J. Sel. Areas Commun. 22(1), 41–53 (2004)CrossRefGoogle Scholar
  13. 13.
    Kutzner, K., Fuhrmann, T.: Measuring large overlay networks—The Overnet example. In: Müller, P., Gotzhein, R., Schmitt, J.B. (eds.) Kommunikation in Verteilten Systemen, KiVS, pp. 193–204. Springer, Berlin (2005)CrossRefGoogle Scholar
  14. 14.
    Marti, S., Ganesan, P., Garcia-Molina, H.: SPROUT: P2P routing with social networks. In: Proceedings of the 9th International Conference on Current Trends in Database Technology, EDBT, Springer, 2004, pp. 425–435Google Scholar
  15. 15.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: a scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Comput. Commun. Rev. 31(4), 149–160 (2001)CrossRefGoogle Scholar
  16. 16.
    Stoica, I., Morris, R., Liben-Nowell, D., Karger, D.R., Kaashoek, M.F., Dabek, F., Balakrishnan, H.: Chord: a scalable peer-to-peer lookup protocol for internet applications. IEEE/ACM Trans. Netw. 11(1), 17–32 (2003)CrossRefGoogle Scholar
  17. 17.
    Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., Tomkins, A.: Geographic routing in social networks. Proc. Natl. Acad. Sci. USA 102(33), 11623–11628 (2005)CrossRefGoogle Scholar
  18. 18.
    Leskovec, J., Horvitz, E.: Geospatial structure of a planetary-scale social network. IEEE Trans. Comput. Soc. Syst. 1(3), 156–163 (2014)CrossRefGoogle Scholar
  19. 19.
    Jia, S., Pierre St, J., Figueiredo, R.J.: A multidimensional heuristic for social routing in peer-to-peer networks. In: Proceedings of 10th IEEE Consumer Communications and Networking Conference, CCNC, 2013, pp. 329–335Google Scholar
  20. 20.
    Li, R.H., Liu, J., Yu, J.X., Chen, H., Kitagawa, H.: Co-occurrence prediction in a large location-based social network. Front. Comput. Sci. 7(2), 185–194 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Gao, L., Li, M., Bonti, A., Zhou, W., Yu, S.: Multidimensional routing protocol in human-associated delay-tolerant networks. IEEE Trans. Mob. Comput. 12(11), 2132–2144 (2013)CrossRefGoogle Scholar
  22. 22.
    Dainotti, A., Squarcella, C., Aben, E., Claffy, K. C., Chiesa, M., Russo, M., Pescapé, A.: Analysis of country-wide internet outages caused by censorship. In: Proceedings of the 11th ACM SIGCOMM Conference on Internet Measurement Conference, 2011, pp. 1–18Google Scholar
  23. 23.
    Mell, P., Harang, R., Gueye, A.: The resilience of the internet to colluding country induced connectivity disruptions. In: Proceedings of 1st Workshop on Security of Emerging Networking Technologies, 2015, pp. 1–10Google Scholar
  24. 24.
    Farrag, M., Abo-Zahhad, M., Doss, M.M., Fayez, J.V.: A new localization technique for wireless sensor networks using social network analysis. Arabian J. Sci. Eng. 42(7), 2817–2827 (2017)CrossRefGoogle Scholar
  25. 25.
    Cutillo, L.A., Molva, R., Strufe, T.: Safebook: a privacy-preserving online social network leveraging on real-life trust. IEEE Commun. Mag. 47(12), 94–101 (2009)CrossRefGoogle Scholar
  26. 26.
    Graffi, K., Mukherjee, P., Menges, B., Hartung, D., Kovacevic, A., Steinmetz, R:. A distributed platform for multimedia communities. In: 2008 10th IEEE International Symposium on Multimedia. ISM 2008. pp. 208–213. IEEE.Google Scholar
  27. 27.
    Graffi, K., Mukherjee, P., Menges, B., Hartung, D., Kovacevic, A., Steinmetz, R.: Practical security in p2p-based social networks. In: IEEE 34th Conference on Local Computer Networks, 2009. LCN 2009, pp. 269–272. IEEE, 2009.Google Scholar
  28. 28.
    Onus, M., Richa, A.W.: Minimum maximum degree publish-subscribe overlay network design. IEEE/ACM Trans. Netw. 19(5), 1331–1343 (2011)CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F.: Lars: a location-aware recommender system. In: Proceedings of 28th IEEE International Conference on Data Engineering, ICDE, 2012, pp. 450–461Google Scholar
  31. 31.
    Mohamed, S., Justin, J.L., Eldawy, A., Mohamed, F.M.: LARS*: a scalable and efficient location-aware recommender system. IEEE Trans. Knowl. Data Eng. 26(6), 1384–1399 (2014)CrossRefGoogle Scholar
  32. 32.
    Yang, D., Zhang, D., Bingqing, Q.: Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans. Intell. Syst. Technol. 7(3), 30 (2016)CrossRefGoogle Scholar
  33. 33.
    Yang, D., Zhang, D., Chen, L., Bingqing, Q.: NationTelescope: monitoring and visualizing large-scale collective behavior in LBSNs. J. Netw. Comput. Appl. 55(1), 170–180 (2015)CrossRefGoogle Scholar
  34. 34.
  35. 35.
    Sala, A., Cao, L., Wilson, C., Zablit, R., Zheng, H., Zhao, B.Y: Measurement-calibrated graph models for social network experiments. In: Proceedings of 19th ACM International Conference on World Wide Web, 2010, pp. 861–870, 2015Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology GoaGoaIndia

Personalised recommendations