Advertisement

Game Theoretic Optimal User Association in Emergency Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11803)

Abstract

The availability of effective communications in post-disaster scenarios is key to implement emergency networks that enable the sharing of critical information and support the coordination of the emergency response. To deliver those levels of QoS suitable to these applications, it is vital to exploit the multiple communication opportunities made available by the progressive deployment of the 5G and Smart City paradigms, ranging from ad-hoc networks among smartphones and surviving IoT devices, to cellular networks but also drone-based and vehicle-based wireless access networks. Therefore, the user device should be able to opportunistically select the most convenient among them to satisfy the demands for QoS imposed by the applications and also minimize the power consumption. The driving idea of this paper is to leverage non-cooperative game theory to design such an opportunistic user association strategy in a post-disaster scenario using UAV ad-hoc networks. The adaptive game-theoretic scheme allows increasing of the QoS of the communication means by lowering the loss rate and also keeps moderate the energy consumption.

Keywords

Game theory Disaster resilient networking Emergency networks Vehicular crowdcell 

Notes

Acknowledgment

This work is supported by CAPES, CNPQ, the EU COST Action CA15127 RECODIS and the Hasler MOBNET project.

References

  1. 1.
    Mauthe, A., et al.: Disaster-resilient communication networks: principles and best practices. In: Proceedings of the 8th International Workshop on Resilient Networks Design and Modeling (RNDM 2016), pp. 1–10 (2016)Google Scholar
  2. 2.
    Furdek, M., et al.: An overview of security challenges in communication networks. In: Proceedings of the 8th International Workshop on Resilient Networks Design and Modeling (RNDM 2016), pp. 1–8 (2016)Google Scholar
  3. 3.
    Gomes, T., et al.: A survey of strategies for communication networks to protect against large-scale natural disasters. In: Proceedings of the 8th International Workshop on Resilient Networks Design and Modeling (RNDM 2016), pp. 1–12, September 2016Google Scholar
  4. 4.
    Merwaday, A., Guvenc, I.: UAV assisted heterogeneous networks for public safety communications. In: Proceedings of the IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 329–334, March 2015Google Scholar
  5. 5.
    Fischer, M., Lynch, N., Paterson, M.: Impossibility of distributed consensus with one faulty process. J. ACM 32(2), 374–382 (1985)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Balani, R.: Energy consumption analysis for Bluetooth, WiFi and cellular networks. Technical report, Electrical Engineering University of California at Los Angeles (2007). http://www.nesl.ucla.edu/uploads/document/paperupload/254/PowerAnalysis.pdf
  7. 7.
    Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Commun. Lett. 20(8), 1647–1650 (2016) Google Scholar
  8. 8.
    Cochran, J.K., Horng, S.-M., Fowler, J.W.: A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comput. Oper. Res. 30(7), 1087–1102 (2003)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Tembine, H.: Distributed Strategic Learning for Wireless Engineers. CRC Press, Boca Raton (2012)zbMATHGoogle Scholar
  10. 10.
    Sutton, S.: Learning to predict by the method of temporal differences. Mach. Learn. 3, 9–44 (1989)Google Scholar
  11. 11.
    Das, D., Das, D.: Efficient UE mobility in multi-RAT cellular networks using SDN. Wirel. Netw. 25(1), 255–267 (2019)Google Scholar
  12. 12.
    Raschellá, A., Bouhafs, F., Deepak, G.C., Mackay, M.: QoS aware radio access technology selection framework in heterogeneous networks using SDN. J. Commun. Netw. 19(6), 577–586 (2017)Google Scholar
  13. 13.
    Yang, W.: Conceptual verification of integrated heterogeneous network based on 5G millimeter wave use in gymnasium. Symmetry 11(3), 376 (2019)Google Scholar
  14. 14.
    Giust, F., Bernardos, C.J., de la Oliva, A.: Analytic evaluation and experimental validation of a network-based IPv6 distributed mobility management solution. IEEE Trans. Mob. Comput. 13(11), 2484–2497 (2014)Google Scholar
  15. 15.
    Zhang, H., Chu, X., Guo, W., Wang, S.: Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum. IEEE Commun. Mag. 53(3), 158–164 (2015)Google Scholar
  16. 16.
    Kumar, A., Mallik, R.K., Schober, R.: A probabilistic approach to modeling users’ network selection in the presence of heterogeneous wireless networks. IEEE Trans. Veh. Technol. 63(7), 3331–3341 (2014)Google Scholar
  17. 17.
    Sui, N., Zhang, D., Zhong, W., Wang, C.: Network selection for heterogeneous wireless networks based on multiple attribute decision making and Evolutionary Game Theory. In: Proceedings of the 25th Wireless and Optical Communication Conference (WOCC), pp. 1–5, May 2016Google Scholar
  18. 18.
    Liou, Y.-S., Gau, R.-H., Chang, C.-J.: A bargaining game based access network selection scheme for HetNet. In: Proceedings of the 1st IEEE International Conference on Communications (ICC), pp. 4888–4893, June 2014Google Scholar
  19. 19.
    Nguyen-Vuong, Q.-T., Agoulmine, N., Cherkaoui, E.H., Toni, L.: Multicriteria optimization of access selection to improve the quality of experience in heterogeneous wireless access networks. IEEE Trans. Veh. Technol. 62(4), 1785–1800 (2013)Google Scholar
  20. 20.
    Nicholson, A.J., Noble, B.D.: Breadcrumbs: forecasting mobile connectivity. In: Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, pp. 46–57 (2008)Google Scholar
  21. 21.
    Robles, T., Bordel, B., Alcarria, R., Martín, D.: Mobile wireless sensor networks: modeling and analysis of three-dimensional scenarios and neighbor discovery in mobile data collection. Ad Hoc Sens. Wirel. Netw. 35(1), 67–104 (2017)Google Scholar
  22. 22.
    Nguyen, D.D., Nguyen, H.X., White, L.B.: Evaluating performance of RAT selection algorithms for 5G Hetnets. IEEE Access 6, 61212–61222 (2018)Google Scholar
  23. 23.
    Yu, G., Jiang, Y., Xu, L., Li, G.Y.: Multi-objective energy-efficient resource allocation for multi-RAT heterogeneous networks. IEEE J. Sel. Areas Commun. 33(10), 2118–2127 (2015)Google Scholar
  24. 24.
    Ghatak, G., De Domenico, A., Coupechoux, M.: Coverage analysis and load balancing in hetnets with millimeter wave Multi-RAT small cells. IEEE Trans. Wirel. Commun. 17(5), 3154–3169 (2018)Google Scholar
  25. 25.
    Iwasawa, H., Tokunaga, K., Takaya, N.: Available-bandwidth information based TCP congestion control algorithm on multi-RAT networks. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2017)Google Scholar
  26. 26.
    Shen, K., Liu, Y., Ding, D.Y., Yu, W.: Flexible multiple base station association and activation for downlink heterogeneous networks. IEEE Signal Process. Lett. 24(10), 1498–1502 (2017)Google Scholar
  27. 27.
    Hayajneh, A.M., Zaidi, S.A.R., McLernon, D.C., Di Renzo, M., Ghogho, M.: Performance analysis of UAV enabled disaster recovery networks: a stochastic geometric framework based on cluster processes. IEEE Access 6, 26215–26230 (2018)Google Scholar
  28. 28.
    Singhal, C., De, S.: Resource Allocation in Next-generation Broadband Wireless Access Networks. IGI Global, Pennsylvania (2017)Google Scholar
  29. 29.
    Baltrunas, D., Elmokashfi, A., Kvalbein, A.: Measuring the reliability of mobile broadband networks. In: Proceedings of the 2014 Conference on Internet Measurement (2014)Google Scholar
  30. 30.
    Tsiropoulou, E., Koukas, K., Papavassiliou, S.: A socio-physical and mobility-aware coalition formation mechanism in public safety networks. EAI Endorsed Trans. Future Internet 4, 154176 (2018)Google Scholar
  31. 31.
    Liu, D., et al.: User association in 5G networks: a survey and an outlook. IEEE Commun. Surv. Tutor. 18(2), 1018–1044 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information Technology (DIETI)University of Napoli “Federico II”NapoliItaly
  2. 2.Institute of Computer ScienceUniversity of BernBernSwitzerland
  3. 3.Universidad Politécnica de MadridMadridSpain
  4. 4.University of Applied Sciences of Western Switzerland (HES-SO)SierreSwitzerland

Personalised recommendations