Automatic Parameter Tuning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-Hoc Networks

  • José García-Nieto
  • Enrique Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)


Communication protocol tuning can yield significant gains in energy efficiency, resource requirements, and the overall network performance, all of which is of particular importance in vehicular ad-hoc networks (VANETs). In this kind of networks, the lack of a predefined infrastructure as well as the high level of dynamism usually provoke problems such as the congestion of intermediate nodes, the appearance of jitters, and the disconnection of links. Therefore, it is crucial to make an optimal configuration of the routing protocols previously to the network deployment. In this work, we address the optimal automatic parameter tuning of a well-known routing protocol: Ad Hoc On Demand Distance Vector (AODV). For this task, we have used and compared five optimization techniques: PSO, DE, GA, ES, and SA. For our tests, a urban VANET scenario has been defined by following realistic mobility and data flow models. The experiments reveal that the produced configurations of AODV significantly improve their performance over using default parameters, as well as compared against other well-known routing protocols. Additionally, we found that PSO outperforms all the compared algorithms in efficiency and accuracy.


Vehicular Ad Hoc Networks On Demand Distance Vector Routing Protocol Metaheuristics ns-2 Simulator 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Härri, J., Filali, F., Bonnet, C.: Mobility Models for Vehicular Ad Hoc Networks: A Survey and Taxonomy. Research Report RR-06-168 (March 2007)Google Scholar
  2. 2.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  3. 3.
    Vanhatupa, T., Hännikäinen, M., Hämäläinen, T.: Optimization of mesh WLAN channel assignment with a configurable genetic algorithm. In: WiMeshNets 2006 (2006)Google Scholar
  4. 4.
    Alba, E., et al.: A Cellular MOGA for Optimal Broadcasting Strategy in Metropolitan MANETs. Computer Communications 30(4), 685–697 (2007)CrossRefGoogle Scholar
  5. 5.
    Di Caro, G.A., Ducatelle, F., Gambardella, L.M.: AntHocNet: An Adaptive Nature-Inspired Algorithm for Routing in Mobile Ad Hoc Networks. European Transactions on Telecommunications 16(5), 443–455 (2005)CrossRefGoogle Scholar
  6. 6.
    Chiang, F., Chaczko, Z., Agbinya, J., Braun, R.: Ant-based topology convergence algorithms for resource management in VANETs. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 992–1000. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Huang, C., Chuang, Y., Hu, K.: Using particle swarm optimization for QoS in ad-hoc multicast. Eng. Appl. of Artificial Intelligence (2009) (in Press)Google Scholar
  8. 8.
    Perkins, C.E., Belding-Royer, E.M., Das, S.: Ad Hoc on Demand Distance Vector (AODV) Routing. IETF RFC 3561 (2003),
  9. 9.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, November 1995, vol. 4, pp. 1942–1948 (1995)Google Scholar
  10. 10.
    Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A practical Approach to Global Optimization. Springer, London (2005)zbMATHGoogle Scholar
  11. 11.
    The Network Simulator Project - Ns-2,
  12. 12.
    Toh, C.: Ad Hoc Wireless Networks: Protocols and Systems. Prentice Hall PTR, Upper Saddle River (2001)Google Scholar
  13. 13.
    Perkins, C.E., Royer, E.M.: Adhoc On Demand Distance Vector Routing. In: 2nd IEEE Workshop on MCSA, Metz, France, pp. 90–100 (1999)Google Scholar
  14. 14.
    Perkins, C.E., Bhagwat, P.: Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers. In: ACM SIGCOMM 1994, London, UK, pp. 234–244 (1994)Google Scholar
  15. 15.
    Johnson, D.B., Maltz, D.A., Broch, J.: DSR: the dynamic source routing protocol for multihop wireless ad hoc networks. In: Ad hoc Networking. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)Google Scholar
  16. 16.
    Naumov, V., Baumann, R., Gross, T.: An evaluation of inter-vehicle ad hoc networks based on realistic vehicular traces. In: Proceedings of the 7th ACM MobiHoc, pp. 108–119. ACM, New York (2006)Google Scholar
  17. 17.
    Krajzewicz, D., Bonert, M., Wagner, P.: The open source traffic simulation package SUMO. In: RoboCup 2006, Bremen, Germany, pp. 1–10 (2006)Google Scholar
  18. 18.
    Alba, E., Luque, G., García-Nieto, J., Ordonez, G., Leguizamón, G.: MALLBA: A software library to design efficient optimisation algorithms. Int. Journal of Innovative Computing and Applications (IJICA) 1(1), 74–85 (2007)CrossRefGoogle Scholar
  19. 19.
    Wilcox, R.: New statistical procedures for the social sciences, Hillsdale (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • José García-Nieto
    • 1
  • Enrique Alba
    • 1
  1. 1.Dept. de Lenguajes y Ciencias de la ComputaciónUniversity of MálagaMálagaSpain

Personalised recommendations