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A Modified SI Epidemic Model for Combating Virus Spread in Wireless Sensor Networks

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Abstract

We study the dynamics of virus spread in wireless sensor networks (WSNs). We first analyze the susceptible-infective (SI) epidemic model for WSNs. In the SI model, once a sensor node is attacked by a virus, the infective node then, using normal communications, spreads the virus to its neighboring nodes, which further spread the virus to their neighbors, the process continues until the whole network fails. To combat this drawback, we propose a modified SI model by leveraging the sleep mode of WSNs to perform system maintenance. The modified SI model can improve the network anti-virus capability and flexibly adapt to different types of virus, without causing any additional hardware effort and signaling overhead. We derive the explicit analytical solutions for the modified SI model, which can capture both the spatial and temporal dynamics of the virus spread process. Extensive numerical results are presented to validate our analysis. The proposed model and analysis method are expected to be used for analysis and design of information (including virus) propagation mechanisms in distributed wireless or computer networks.

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References

  1. S. Tang and W. Li, Qos supporting and optimal energy allocation for a cluster-based wireless sensor network, Elsevier Computer Communications, Vol. 29, pp. 2569–2577, 2006.

    Google Scholar 

  2. D. Yupho and J. Kabara, The effect of physical topology on wireless sensor network lifetime, Journal of Networks, Vol. 2, pp. 14–23, 2007.

    Article  Google Scholar 

  3. N. A. Pantazisa, D. J. Vergadosb, D. D. Vergadosa, and C. Douligeris, Energy efficiency in wireless sensor networks using sleep mode TDMA scheduling, Ad Hoc Networks, Vol. 7, pp. 322–343, 2009.

    Article  Google Scholar 

  4. P. Ferrie, P. Szor, R. Stanev, and R. Mouritzen, Security responses: Symbos.cabir, tech. rep., Symantec Corporation, 2004.

  5. E. Chien, Security response: Symbos.mabir, tech. rep., Symantec Corporation, 2005.

  6. J.-L. Sanders, Quantitative guidelines for communicable disease control programs, Biometrics, Vol. 27, pp. 883–893, 1971.

    Article  Google Scholar 

  7. H. W. Hethcote, An immunization model for a heterogeneous population, Theoretical Population Biology, Vol. 14, pp. 338–349, 1978.

    Article  MathSciNet  Google Scholar 

  8. R. Pastor-Satorras and A. Vespignani, Epidemic spreading in scale-free networks, Physical Review Letters, Vol. 86, pp. 3200–3203, 2001.

    Article  Google Scholar 

  9. M. E. J. Newman, Spread of epidemic disease on networks, Physical Review E, Vol. 66, pp. 1–11, 2002.

    Google Scholar 

  10. Y. Moreno, M. Nekovee, and A. Vespignani, EfficiLency and reliability of epidemic data dissemination in complex networks, Physical Review E, Vol. 69, pp. 1–4, 2004.

    Google Scholar 

  11. A. Khelil, C. Becker, J. Tian, and K. Rothermel, Directed-graph epidemiological models of computer viruses. In Proc. 5th ACM Int’l Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems, pp. 54–60, 2002.

  12. J. W. Mickens and B. D. Noble, Modeling epidemic spreading in mobile environments. In Proc. 4th ACM Workshop on Wireless Security (WiSe’05), Cologne, Germany, pp. 77–86, 2005.

  13. S. A. Khayam and H. Radha, A topologically-aware worm propagation model for wireless sensor networks. In Proc. 2nd Int’l Workshop on Security in Distributed Computing Systems, pp. 210–216, 2005.

  14. H. Zheng, D. Li, and Z. Gao, An epidemic model of mobile phone virus. In 1st Int’l Symposium on Pervasive Computing and Applications, pp. 1–5, Aug. 2006.

  15. P. De, Y. Liu, and S. K. Das, An epidemic theoretic framework for evaluating broadcast protocols in wireless sensor networks. In Proc. IEEE Int’l Conf. on Mobile Adhoc and Sensor Systems (MASS), Pisa, Italy, Oct. 2007.

  16. S. K. Tan and A. Munro, Adaptive probabilistic epidemic protocol for wireless sensor networks in an urban environment. In Proc. 16th International Conference on Computer Communications and Networks (ICCCN 2007), pp. 1105–1110, Aug. 2007.

  17. R. M. Anderson and R. M. May, Infectious Diseases of Human: Dynamics and Control. Oxford University Press, Oxford, 1991.

    Google Scholar 

  18. J. W. Hui and D. Culler, The dynamic behavior of a data dissemination protocol for network programming at scale. In Proc. 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, MD, pp. 81–94, Nov. 2004.

  19. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Gossip algorithms: design, analysis and applications. In Proc. IEEE INFOCOM’05, Miami, FL, Mar. 2005.

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  1. Department of Engineering Technology, Missouri Western State University, 4525 Downs Dr., St. Joseph, MO, 64507, USA

    Shensheng Tang

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  1. Shensheng Tang
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Correspondence to Shensheng Tang.

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Tang, S. A Modified SI Epidemic Model for Combating Virus Spread in Wireless Sensor Networks. Int J Wireless Inf Networks 18, 319–326 (2011). https://doi.org/10.1007/s10776-011-0147-z

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  • DOI: https://doi.org/10.1007/s10776-011-0147-z

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