Cellular Automata Based Evacuation Process Triggered by Indoors Wi-Fi and GPS Established Detection

  • N. Kartalidis
  • I. G. Georgoudas
  • G. Ch. SirakoulisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11115)


This study presents the principles of an application that is designed to facilitate customized evacuation from indoor spaces. The proposed approach combines in-doors detection using existing wireless networks based on trilateration technique and proper evacuation estimation based on cellular automata (CA). An efficient application has been developed that can be installed in smartphones under Android operation system and technically fulfills the scopes of the aforementioned evacuation model. More specifically, it offers the user the option to view her/his location at any time and to find the closest possible route to an exit in case of an emergency. The efficiency of the application to provide reliable guidance towards an exit is also evaluated. Preliminary results are reasonably encouraging; provided that the application is properly customized then a reliable, real-time evacuation guidance could be realized.


Cellular automata Evacuation Modelling Trilateration Smartphones Wireless Android 


  1. 1.
    Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., McCaughey, A.: An evaluation of indoor location determination technologies. J. Locat. Based Serv. 5(2), 61–78 (2011)CrossRefGoogle Scholar
  2. 2.
    Furey, E., Curran, K., Mc Kevitt, P.: HABITS: a Bayesian filter approach to indoor tracking and location. Int. J. Bio-Inspired Comput. (IJBIC) 4(2), 79–88 (2012)CrossRefGoogle Scholar
  3. 3.
    Mazuelas, S., et al.: Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE J. Sel. Topics Sig. Process. 3(5), 821–831 (2009)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Han, D., Jung, S., Lee, M., Yoon, G.: Building a practical Wi-Fi-based indoor navigation system. IEEE Pervasive Comput. 13(2), 72–79 (2013)Google Scholar
  5. 5.
    Nižetić Kosović, I., Jagušt, T.: Enhanced weighted centroid localization algorithm for indoor environments. Int. J. Electron. Commun. Eng. 8(7), 1219–1223 (2014)Google Scholar
  6. 6.
    Cheng, Y.-C., Chawathe, Y., LaMarca, A., Krumm, J.: Accuracy characterization for metropolitan-scale Wi-Fi localization. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, pp. 233–245. IEEE (2005)Google Scholar
  7. 7.
    Chrysikos, T., Georgopoulos, G., Kotsopoulos, S.: Empirical calculation of shadowing deviation for complex indoor propagation topologies at 2.4 GHz. In: Proceedings of 2009 International Conference on Ultra Modern Telecommunications & Workshops, pp. 1–6. IEEE (2009)Google Scholar
  8. 8.
    Zhuang, Y., Li, Y., Lan, H., Syed, Z., El-Sheimy, N.: Smartphone-based WiFi access point localisation and propagation parameter estimation using crowdsourcing. IET Electron. Lett. 51(17), 1380–1382 (2015)CrossRefGoogle Scholar
  9. 9.
    Chrysikos, T., Georgopoulos, G., Kotsopoulos, S.: Attenuation over distance for indoor propagation topologies at 2.4 GHz. In: Proceedings of 2011 IEEE Symposium on Computers and Communications, pp. 329–334. IEEE (2011)Google Scholar
  10. 10.
    Georgoudas, I.G., Sirakoulis, G.Ch., Andreadis, I.Th.: An anticipative crowd management system preventing clogging in exits during pedestrian evacuation processes. ΙΕΕΕ Syst. J. 5(1), 129–141 (2011)Google Scholar
  11. 11.
    Bandini, S., Mauri, G., Serra, R.: Cellular automata: from a theoretical parallel computational model to its application to complex systems. Parallel Comput. 27(5), 539–553 (2001)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lujak, M., Billhardt, H., Dunkel, J., Fernández, A., Hermoso, R., Ossowski, S.: A distributed architecture for real-time evacuation guidance in large smart buildings. Comput. Sci. Inf. Syst. 14(1), 257–282 (2017)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
    Georgoudas, I.G., Koltsidas, G., Sirakoulis, G.Ch., Andreadis, I.Th.: A cellular automaton model for crowd evacuation and its auto-defined obstacle avoidance attribute. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds.) ACRI 2010. LNCS, vol. 6350, pp. 455–464. Springer, Heidelberg (2010). Scholar
  16. 16.
    Lubaś, R., Wąs, J., Porzycki, J.: Cellular automata as the basis of effective and realistic agent-based models of crowd behavior. J. Supercomput. 72(6), 2170–2196 (2016)CrossRefGoogle Scholar
  17. 17.
    Tsompanas, M.-A.I., Sirakoulis, G.Ch., Adamatzky, A.I.: Evolving transport networks with cellular automata models inspired by slime mould. IEEE Trans. Cybern. 45(9), 1887–1899 (2015)CrossRefGoogle Scholar
  18. 18.
    Tsiftsis, A., Georgoudas, I.G., Sirakoulis, G.Ch.: Real data evaluation of a crowd supervising system for stadium evacuation and its hardware implementation. IEEE Syst. J. 10(2), 649–660 (2016)CrossRefGoogle Scholar
  19. 19.
    Crociani, L., Vizzari, G., Yanagisawa, D., Nishinari, K., Bandini, S.: Route choice in pedestrian simulation: design and evaluation of a model based on empirical observations. Intelligenza Artificiale 10(2), 163–182 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • N. Kartalidis
    • 1
  • I. G. Georgoudas
    • 1
  • G. Ch. Sirakoulis
    • 1
    Email author
  1. 1.Laboratory of Electronics, Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

Personalised recommendations