An Overlay Approach for Optimising Small-World Properties in VANETs

  • Julien Schleich
  • Grégoire Danoy
  • Bernabé Dorronsoro
  • Pascal Bouvry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)


Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. In order to face this new multi-objective optimisation problem, the NSGAII algorithm was used on realistic instances in the city-centre of Luxembourg. The accurate tradeoff solutions found by NSGAII (assuming global knowledge of the network) will permit to better know and understand the problem. This will later ease the design of decentralised solutions to be used in real environments, as well as their future validation.


Multi-objective optimisation VANETs small-world 


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  1. 1.
    Hotcity network website,
  2. 2.
    Banerjee, A., Agarwal, R., Gauthier, V., Yeo, C.K., Afifi, H., Lee, B.-S.: A self-organization framework for wireless ad hoc networks as small worlds. CoRR, abs/1203.1185 (2012)Google Scholar
  3. 3.
    Brust, M.R., Frey, H., Rothkugel, S.: Dynamic multi-hop clustering for mobile hybrid wireless networks. In: Int. Conf. on Ubiquitous Information Management and Communication (ICUIMC), pp. 130–135. ACM (2008)Google Scholar
  4. 4.
    Brust, M.R., Ribeiro, C.H.C., Turgut, D., Rothkugel, S.: LSWTC: A local small-world topology control algorithm for backbone-assisted mobile ad hoc networks. In: IEEE Con. on Local Computer Networks (LCN), pp. 144–151 (2010)Google Scholar
  5. 5.
    Brust, M.R., Turgut, D., Riberio, C.H.C., Kaiser, M.: Is the clustering coefficient a measure for fault tolerance in wireless sensor networks? In: IEEE Int. Conf. on Communications—Ad-hoc and Sensor Networking Symposium (ICC) (2012)Google Scholar
  6. 6.
    Cavalcanti, D., Agrawal, D., Kelner, J., Sadok, D.: Exploiting the small-world effect to increase connectivity in wireless ad hoc networks. In: IEEE Int. Conf. on Telecommunications (2004)Google Scholar
  7. 7.
    Coello Coello, C.A., Lamont, G.B., Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer (2007)Google Scholar
  8. 8.
    Danoy, G., Alba, E., Bouvry, P.: Optimal interconnection of ad hoc injection networks. Journal of Interconnection Networks (JOIN) 9(3), 277–297 (2008)CrossRefGoogle Scholar
  9. 9.
    Danoy, G., Alba, E., Bouvry, P., Brust, M.R.: Optimal design of ad hoc injection networks by using genetic algorithms. In: Conf. on Genetic and Evolutionary Computation, GECCO, pp. 2256–2256. ACM (2007)Google Scholar
  10. 10.
    Danoy, G., Bouvry, P., Hogie, L.: Coevolutionary genetic algorithms for ad hoc injection networks design optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 4273–4280 (2007)Google Scholar
  11. 11.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Comp. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Dorronsoro, B., Ruiz, P., Danoy, G., Pigné, Y., Bouvry, P.: Evolutionary Algorithms for Mobile Networks. Wiley (in press, 2013)Google Scholar
  13. 13.
    Filiposka, S., Trajanov, D., Grnarov, A.: Analysis of small world phenomena and group mobility in ad hoc networks. In: Innovative Algs. and Techns. in Automation, Industrial Electronics and Telecommunications, pp. 425–430. Springer (2007)Google Scholar
  14. 14.
    Helmy, A.: Small worlds in wireless networks. IEEE Communications Letters 7(10), 490–492 (2003)CrossRefGoogle Scholar
  15. 15.
    Pallis, G., Katsaros, D., Dikaiakos, M.D., Loulloudes, N., Tassiulas, L.: On the structure and evolution of vehicular networks. In: Int. Symp. on Modeling, Analysis & Simulation of Computer and Telecom. Systems, pp. 1–10. IEEE (2009)Google Scholar
  16. 16.
    Pigné, Y., Danoy, G., Bouvry, P.: A Vehicular Mobility Model Based on Real Traffic Counting Data. In: Strang, T., Festag, A., Vinel, A., Mehmood, R., Rico Garcia, C., Röckl, M. (eds.) Nets4Trains/Nets4Cars 2011. LNCS, vol. 6596, pp. 131–142. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Rezende, C., Boukerche, A., Pazzi, R.W., Rocha, B.P.S., Loureiro, A.A.F.: The impact of mobility on mobile ad hoc networks through the perspective of complex networks. J. Parallel Distrib. Comput. 71(9), 1189–1200 (2011)CrossRefGoogle Scholar
  18. 18.
    Stai, E., Karyotis, V., Papavassiliou, S.: Socially-inspired topology improvements in wireless multi-hop networks. In: IEEE Int. Conf. on Communications (ICC), pp. 1–6 (2010)Google Scholar
  19. 19.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Julien Schleich
    • 1
  • Grégoire Danoy
    • 1
  • Bernabé Dorronsoro
    • 2
  • Pascal Bouvry
    • 1
  1. 1.Computer Science and Communications Research UnitUniversity of LuxembourgLuxembourg
  2. 2.Laboratoire d’Informatique Fondamentale de LilleUniversity of Lille 1France

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