A Generalized Cellular Automata Approach to Modelling Contagion and Monitoring for Emergent Events in Sensor Networks

  • Ru HuangEmail author
  • Hongyuan Yang
  • Haochen Yang
  • Lei Ma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)


In order to improve the invulnerability and adaptability in sensor networks, we propose a cellular automata (CA) based propagation control mechanism (CACM) to inhibit and monitor emergent-event contagion. The cellular evolving rules of CACM are figured in multi-dimension convolution operations and cell state transform, which can be utilized to model the complex behavior of sensor nodes by separating the intrinsic and extrinsic states for each network cell. Furthermore, inspired by burning pain for Wireworld based monitoring model, network entropy theory is introduced into layered states on CACM to construct particle-based information communication process by efficient distribution of event-related messages on network routers, thus an invulnerable and energy-efficient diffusion and monitoring being achieved. Experiment results prove that CACM can outperform traditional propagation models in adaptive invulnerability and self-recovery scalability on sensor networks for propagation control on malicious events.


Sensor network Cellular automata Invulnerability Contagion 


  1. 1.
    Ren, F.Y.: Wireless sensor networks. J. Softw. 14(14), 1513–1525 (2003) Google Scholar
  2. 2.
    Liu, X., Han, J., Ni, G., Zhang, C., Liu, Y.: A multipath redundant transmission algorithm for MANET. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds.) CSPS 2017. LNEE, vol. 463, pp. 518–524. Springer, Singapore (2019). Scholar
  3. 3.
    Libi, F., Song, W., Wei, L., Lo, S.: Simulation of emotional contagion using modified sir model: a cellular automaton approach. Phys. A Stat. Mech. Appl. 405, 380–391 (2014)CrossRefGoogle Scholar
  4. 4.
    Shaw, A.K., Tsvetkova, M., Daneshvar, R.: The effect of gossip on social networks. Complexity 16(4), 39–47 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tang, S., Myers, D., Yuan, J.: Modified SIS epidemic model for analysis of virus spread in wireless sensor networks. Int. J. Wirel. Mob. Comput. 6(2), 99–108 (2013)CrossRefGoogle Scholar
  6. 6.
    Fresnadillo, M.J., García, E., García, J.E., Martín, Á., Rodríguez, G.: A SIS epidemiological model based on cellular automata on graphs. In: Omatu, S., et al. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 1055–1062. Springer, Heidelberg (2009). Scholar
  7. 7.
    Peng, S., Wang, G., Shui, Y.: Modeling the dynamics of worm propagation using two-dimensional cellular automata in smartphones. J. Comput. Syst. Sci. 79(5), 586–595 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Choudhury, S.: Cellular automata and wireless sensor networks. In: Adamatzky, A. (ed.) Emergent Computation. ECC, vol. 24, pp. 321–335. Springer, Cham (2017). Scholar
  9. 9.
    Baryshnikov, Y.M., Coffman, E., Kwak, K.J.: High performance sleep-wake sensor systems based on cyclic cellular automata. In: 2008 International Conference on Information Processing in Sensor Networks (IPSN 2008), pp. 517–526. IEEE (2008)Google Scholar
  10. 10.
    Athanassopoulos, S., Kaklamanis, C., Katsikouli, P., Papaioannou, E.: Cellular automata for topology control in wireless sensor networks. In: 2012 16th IEEE Mediterranean Electrotechnical Conference, pp. 212–215. IEEE (2012)Google Scholar
  11. 11.
    Mansilla, R., Gutierrez, J.L.: Deterministic site exchange cellular automata model for the spread of diseases in human settlements (2000)Google Scholar
  12. 12.
    He, Y., Zhang, W., Jiang, N., Luo, X.: The research of scale-free sensor network topology evolution based on the energy efficient. In: 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 221–226. IEEE (2014)Google Scholar
  13. 13.
    Hennebert, C., Hossayni, H., Lauradoux, C.: The entropy of wireless statistics. In: 2014 European Conference on Networks and Communications (EuCNC), pp. 1–5. IEEE (2014)Google Scholar
  14. 14.
    Harris, D., Harris, S.: Digital Design and Computer Architecture. Morgan Kaufmann, Burlington (2010)Google Scholar
  15. 15.
    Wu, T.L., Lai, Y.H., Fung, R.F.: Comparisons of fitness functions in identifying an electromagnetic energy harvester. J. Vib. Eng. Technol. 7(2), 167–177 (2019)CrossRefGoogle Scholar
  16. 16.
    Lopez, L., Burguerner, G., Giovanini, L.: Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach. BMC Res. Notes 7(1), 1–11 (2014)CrossRefGoogle Scholar
  17. 17.
    Panwar, H., Gupta, S.: Optimized large margin classier based on perceptron. In: Wyld, D., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications. AINSC, vol. 166, pp. 385–392. Springer, Heidelberg (2012). Scholar
  18. 18.
    Akram, H., Khalid, S., et al.: Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition. J. Electr. Syst. Inf. Technol. 4(3), 387–396 (2017)CrossRefGoogle Scholar
  19. 19.
    Mata, J., Cohn, M.: Cellular automata-based modeling program: synthetic immune system. Immunol. Rev. 216(1), 198–212 (2010)CrossRefGoogle Scholar
  20. 20.
    Pun-Cheng, L.S.C., Chan, A.W.F.: Optimal route computation for circular public transport routes with differential fare structure. Travel Behav. Soc. 3(4), 71–77 (2016)CrossRefGoogle Scholar
  21. 21.
    Motter, A.E., Timme, M.: Antagonistic phenomena in network dynamics. Annu. Rev. Condens. Matter Phys. 9(1), 463–484 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information Science and EngineeringEast China University of Science and TechnologyShanghaiChina

Personalised recommendations