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Improving the Performance of Challenged Networks with Controlled Mobility

  • Laurent ReynaudEmail author
  • Isabelle Guérin-Lassous
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 184)

Abstract

In this work, we investigate the application of an adapted controlled mobility strategy on self-propelling nodes, which could efficiently provide network resource to users scattered on a designated area. We design a virtual force-based controlled mobility scheme, named VFPc, and evaluate its ability to be jointly used with a dual packet-forwarding and epidemic routing protocol. In particular, we study the possibility for end-users to achieve synchronous communications at given times of the considered scenarios. On this basis, we study the delay distribution for such user traffic and show the advantages of VFPc compared to other packet-forwarding and packet-replication schemes, and highlight that VFPc-enabled applications could take benefit of both schemes to yield a better user experience, despite challenging network conditions.

Keywords

Controlled mobility Virtual forces MANET Challenged networks DTN Unmanned aerial vehicles Disaster communications 

References

  1. 1.
    Sanou, B.: ICT Facts and Figures 2015. International Telecommunication Union (ITU) Fact Sheet (2015)Google Scholar
  2. 2.
    Nelson, C.B., Steckler, B.D., Stamberger, J.A.: The evolution of hastily formed networks for disaster response: technologies, case studies, and future trends. In: IEEE Global Humanitarian Technology Conference, Seattle, USA, pp. 467–475 (2011)Google Scholar
  3. 3.
    Corson, S., Macker, J.: Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations. RFC 2501 (1999)Google Scholar
  4. 4.
    Reina, D.G., et al.: A survey on multihop ad hoc networks for disaster response scenarios. Int. J. Distrib. Sens. Netw. 2015 (2015)Google Scholar
  5. 5.
    Sassatelli, L., et al.: Reliable transport in delay-tolerant networks with opportunistic routing. IEEE Trans. Wireless Commun. 13(10), 5546–5557 (2014)CrossRefGoogle Scholar
  6. 6.
    Fall, K.: A delay-tolerant network architecture for challenged internets. In: Proceedings of ACM SIGCOMM 2003, Karlsruhe, Germany (2003)Google Scholar
  7. 7.
    Li, Y., Hui, P., Jin, D., Chen, S.: Delay-tolerant network protocol testing and evaluation. IEEE Com. Mag. 53(1), 258 (2015)CrossRefGoogle Scholar
  8. 8.
    Spyropoulos, T., Psounis, K., Raghavendra, C.S.: Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In: Proceedings of the ACM Workshop on Delay-Tolerant Networking (2005)Google Scholar
  9. 9.
    Burgess, J., Gallagher, B., Jensen, D., Levine, B.N.: MaxProp: routing for vehicle-based disruption-tolerant networks. In: IEEE INFOCOM, Barcelona, Spain (2006)Google Scholar
  10. 10.
    Balasubramanian, A., Levine, B.N., Venkataramani, A.: DTN routing as a resource allocation problem. In: Proceedings of the ACM SIGCOMM (2007)Google Scholar
  11. 11.
    Clausen, T., Jacquet, P.: RFC3626, Optimized Link State Routing Protocol (OLSR). Experimental. http://www.ietf.org/rfc/rfc3626.txt
  12. 12.
    Alenazi, M.J.F., Cheng, Y., Zhang, D., Sterbenz, J.P.G.: Epidemic routing protocol implementation in ns-3. In: Workshop on ns-3, Barcelona, Spain (2015)Google Scholar
  13. 13.
    Spears, W.M., Spears, D.F., Hamann, J.C., Heil, R.: Distributed, physics-based control of swarms of vehicles. Auton. Robots 17(2/3), 137–162 (2004)CrossRefGoogle Scholar
  14. 14.
    Reynaud, L., Guérin Lassous, I.: Design of a force-based controlled mobility on aerial vehicles for pest management. Ad Hoc Net. J. 53, 41–52 (2016). ElsevierCrossRefGoogle Scholar
  15. 15.
    Reynaud, L., Guérin Lassous, I.: Physics-based swarm intelligence for disaster relief communications. In: International Conference on Ad Hoc Networks and Wireless, Lille, France (2016)Google Scholar
  16. 16.
    Zhao, W., Ammar, M., Zegura, E.: Controlling the mobility of multiple data transport ferries in a delay-tolerant network. In: IEEE INFOCOM, Miami, USA (2005)Google Scholar
  17. 17.
    Basagni, S., et al.: Controlled sink mobility for prolonging wireless sensor networks lifetime. Wireless Netw. J. 14(6), 831–858 (2008)CrossRefGoogle Scholar
  18. 18.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  19. 19.
    Nouyan, S., Campo, A., Dorigo, M.: Path formation in a robot swarm: self-organized strategies to find your way home. Swarm Intell. 2(1), 1–23 (2008)CrossRefGoogle Scholar
  20. 20.
    Sperati, V., Trianni, V., Nolfi, S.: Self-organised path formation in a swarm of robots. Swarm Intell. 5, 97–119 (2011)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.Orange LabsLannionFrance
  2. 2.Université de Lyon/LIP (ENS Lyon, CNRS, UCBL, Inria)LyonFrance

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