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High-Quality Learning Resource Dissemination Based on Opportunistic Networks in Campus Collaborative Learning Context

  • Peng Li
  • Hong Liu
  • Longjiang GuoEmail author
  • Lichen Zhang
  • Xiaoming Wang
  • Xiaojun Wu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1101)

Abstract

In the campus scenario, a basic community of collaborative teams is formed among the nodes participating in the collaborative learning interaction in the mobile opportunistic network. Due to the existing research does not consider the weak connection, node influence and the contact characteristics between nodes. In this paper, a routing method using a collaborative group as a communication unit is proposed. The route mainly counts the contact characteristics among the groups according to the characteristics of the node movement and predicts the subsequent contact situation. Combined with the weak connection relationship and the node’s influence, the optimal node to be transmitted is selected. It has been experimentally verified that the routing method can greatly improve the speed of message dissemination and avoid unnecessary message redundancy and waste of contact opportunities.

Keywords

Opportunistic networks Collaborative learning Weak connection Community influence 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61877037) and the National Natural Science Foundation of China (No. 61977044).

References

  1. 1.
    Fall, K.: A delay-tolerant network architecture for challenged internets. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, p. 27. ACM, Karlsruhe (2003)Google Scholar
  2. 2.
    Vahdat, A., Becker, D.: Epidemic Routing for Partially-Connected Ad Hoc Networks. Handbook of Systemic Autoimmune Diseases (2000)Google Scholar
  3. 3.
    Pelusi, L.: Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Commun. Mag. 44(11), 134–141 (2006)CrossRefGoogle Scholar
  4. 4.
    Spyropoulos, T., Psounis, K., Raghavendra, C.S.: Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In: ACM SIGCOMM Workshop on Delay-tolerant Networking, pp. 252–259. ACM, New York (2005)Google Scholar
  5. 5.
    Sushant, J., Kevin, R., Rabin, K.: Routing in a delay tolerant network. In: Technologies, Architectures, and Protocols for Computer Communication, pp. 145–158. ACM, Portland (2004)Google Scholar
  6. 6.
    Lindgren, A., Doria, A., Schelén, O.: Probabilistic routing in intermittently connected networks. In: ACM SIGMOBILE Mobile Computing and Communications Review, vol. 7, no. 3, pp. 19–20 (2003)CrossRefGoogle Scholar
  7. 7.
    Zhao, R., Zhang, L., et al.: A novel energy-efficient probabilistic routing method for mobile opportunistic networks. EURASIP J. Wirel. Commun. Netw. 2018(1), 263 (2018)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Chen, X., Shang, C., Wong, B., et al.: Efficient multicast algorithms in opportunistic mobile social networks using community and social features. Comput. Netw. 111, 71–81 (2016)CrossRefGoogle Scholar
  10. 10.
    Petroczi, A., Bazsó, F., et al.: Measuring tie-strength in virtual social networks. Connections 91(1), 39–52 (2006)Google Scholar
  11. 11.
    Friedkin, N.: A test of structural features of Granovetter’s strength of weak ties theory. Soc. Netw. 2(4), 411–422 (1980)CrossRefGoogle Scholar
  12. 12.
    Rogers, E.M.: Perspectives on social network research. In: Network Analysis of the Diffusion of Innovations, pp. 137–164 (1979)CrossRefGoogle Scholar
  13. 13.
    Fine, G.A., Kleinman, S.: Rethinking subculture: an interactionist analysis. Am. J. Sociol. 85(1), 1–20 (1979)CrossRefGoogle Scholar
  14. 14.
    Kristiansson, S.: Enriching and simplifying communication by social prioritization. In: International Conference on Advances in Social Networks Analysis and Mining, Odense, Denmark, pp. 336–340 (2010)Google Scholar
  15. 15.
    Haythornthwaite, W.C., Garton, L.: Studying online social networks. J. Comput.-Mediated Commun. 3(1), 1–5 (1997)Google Scholar
  16. 16.
    May, R.M., Anderson, R.M.: Population biology of infectious disease: part II. Nature 280, 455–461 (1979)CrossRefGoogle Scholar
  17. 17.
    Azar, S., Machado, J.C., et al.: Motivations to interact with brands on Facebook - towards a typology of consumer - brand interactions. J. Brand Manag. 23(2), 153–178 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Peng Li
    • 1
    • 2
    • 3
  • Hong Liu
    • 1
    • 2
    • 3
  • Longjiang Guo
    • 1
    • 2
    • 3
    Email author
  • Lichen Zhang
    • 1
    • 2
    • 3
  • Xiaoming Wang
    • 1
    • 2
    • 3
  • Xiaojun Wu
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of Modern Teaching TechnologyMinistry of EducationXi’anChina
  2. 2.Engineering Laboratory of Teaching Information Technology of Shaanxi ProvinceXi’anChina
  3. 3.School of Computer ScienceShaanxi Normal UniversityXi’anChina

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