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Data Forwarding Based on Node Moving Trajectory in Mobile Social Networks

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Abstract

With the popularization of wireless mobile device, mobile social networks (MSNets) have begun to attract more and more attention. In MSNets, mobile social users can communicate with each other by intermittent connectivity. The wireless device moving trajectory reflects the social attribute of the device carrier. In this paper, the relay selection is addressed in terms of both the absolute character and the relative character of node moving trajectory. First, the hot degree of node movement trajectory is defined based on the steady-state node distribution using a semi-markov chain. Moreover, another semi-markov chain is used to predict the future locations of a mobile user, with the predictive location nodes as basis, and the similarity of node movement trajectories is presented. Furthermore, a data forwarding based on node moving trajectory is proposed. Its main idea is to choose a node with higher hot degree of node moving trajectory and lower similarity of movement trajectories between it and a packet carrier to propagate the data packets. The simulation results show that, compared with the Spray and Wait routing and the social groups-based routing, our algorithm can outperform them in the delivery ratio and delivery delay, and apparently reduce network overhead compared with the Epidemic algorithm. Additionally, our algorithm nears the maximum delivery ratio and minimum delivery delay obtained by the Epidemic algorithm in a realistic trace data.

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References

  1. Zhu, Y., Xu, B., Shi, X., & Wang, Y. (2013). A survey of social-based routing in delay tolerant networks: Positive and negative social effects. IEEE Communications Surveys & Tutorials, 15(1), 387–401.

    Article  Google Scholar 

  2. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially connected ad hoc networks. Duke Technical Report CS-200006.

  3. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008). Efficient routing in intermittently connected mobile networks: The multiple-copy case. IEEE/ACM Transactions on Networking, 16(1), 77–90.

    Article  Google Scholar 

  4. Lindgren, A., Doria, A., & Schelen, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review, 7(3), 19–20.

    Article  Google Scholar 

  5. Hui, P., Crowcroft, J., & Yoeki, E. (2011). Bubble Rap: Social-based forwarding in delay tolerant networks. IEEE Transactions on Mobile Computing., 10(11), 1576–1589.

    Article  Google Scholar 

  6. Gao, W., Cao, G., Porta, T., & Han, J. (2013). On exploiting transient social contact patterns for data forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 12(1), 151–165.

    Article  Google Scholar 

  7. Wang, Q. S., & Wang, Q. (2015). Restricted epidemic routing in multi-community delay tolerant networks. IEEE Transactions on Mobile Computing, 14(8), 1686–1697.

    Article  Google Scholar 

  8. Abdelkader, T., Naik, K., Nayak, A., Goel, N., & Srivastava, V. (2013). SGBR: A routing protocol for delay tolerant networks using social grouping. IEEE Transactions on Parallel and Distributed Systems, 24(12), 2472–2481.

    Article  Google Scholar 

  9. Wu, J., & Wang, Y. (2014). Hypercube-based multi-path social feature routing in human contact networks. IEEE Transactions on Computers, 63(2), 383–396.

    Article  Google Scholar 

  10. Yuan, Q., Cardei, I., & Wu, J. (2012). An efficient prediction-based routing in disruption-tolerant networks. IEEE Transactions on Parallel and Distributed System, 23(1), 19–31.

    Article  Google Scholar 

  11. Lee, J. K., & Hou, J. C. (2006). Modeling steady-state and transient behaviors of user mobility: Formulation, analysis, and application. In Proceedings of the ACM international symposium on mobile ad hoc networking and computing (pp. 85–96).

  12. Corradi, G., Janssen, J., & Manca, R. (2004). Numerical treatment of homogeneous semi-Markov processes in transient case—A straightforward approach. Methodology and Computing in Applied Probability, 6(2), 233–246.

    Article  MathSciNet  MATH  Google Scholar 

  13. Tan, P. N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Boston: Addison-Wesley.

    Google Scholar 

  14. Fan, J., Chen, J., Du, Y., Wang, P., & Sun, Y. (2011). Delque: A socially aware delegation query scheme in delay-tolerant networks. IEEE Transactions on Vehicular Technology, 60(5), 2181–2193.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their insightful comments which have helped improve the quality of this paper. This work was partly supported by Natural Science Foundation of China [61401144, 61571179], Anhui Natural Science Foundation, China [1308085MF87], Open Fund of State Key Lab. for Novel Software Technology, Nanjing University, China [KFKT2014B22], the Specialized Research Fund for the Doctoral Program of Higher Education [20130111120018], and the Fundamental Research Funds for the Central Universities [2013HGXJ0232, 2015HGZX0019].

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Authors and Affiliations

  1. School of Mathematics, Hefei University of Technology, Hefei, 230009, China

    Qi Wang & Qingshan Wang

  2. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, 19122, USA

    Qi Wang

  3. Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Qi Wang & Qingshan Wang

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  1. Qi Wang
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  2. Qingshan Wang
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Correspondence to Qi Wang.

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Wang, Q., Wang, Q. Data Forwarding Based on Node Moving Trajectory in Mobile Social Networks. Wireless Pers Commun 87, 1285–1297 (2016). https://doi.org/10.1007/s11277-015-3053-3

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  • DOI: https://doi.org/10.1007/s11277-015-3053-3

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