Multi-hop Clustering Solution Based on Beacon Delay for Vehicular Ad-Hoc Networks

  • Soufiane Ouahou
  • Slimane Bah
  • Zohra Bakkoury
  • Abdelhakim Hafid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10542)


Vehicular ad hoc networks (VANET) are a specific type of networks, wherein nodes are vehicles equipped with wireless receivers. The vehicles can exchange data by using wireless communication either in ad-hoc mode or infrastructure mode through equipments installed on the road side. In vehicular networks, clustering is one of the main dissemination methods, as it enhances the communication reliability and performance. However, a good clustering solution for VANETs has to address the highly dynamic and complex aspects (e.g. real time) of such environments. Of this fact, we propose a novel multi-hop clustering model including: cluster construction/destruction mechanism, multi-hop links establishment and cluster head election algorithm based on a new metric. Indeed, the proposed metric captures real environment parameters. The simulation results show that our clustering solution performs better than existing solutions especially when the obstacle shadowing model is used which is the most likely scenario in real situations.


Vehicular ad hoc networks Clustering Multi-hop communication 


  1. 1.
    Sommer, C., Eckhoff, D., German, R., Dressler, F.: A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments. In: Proceedings of 8th IEEE/IFIP Conference on Wireless On demand Network Systems and Services (WONS 2011), Bardonecchia, Italy, pp. 84–90, January 2011Google Scholar
  2. 2.
    Sommer, C., Eckhoff, D., Dressler, F.: IVC in cities: signal attenuation by buildings and how parked cars can improve the situation. IEEE Trans. Mob. Comput. 13(8), 1733–1745 (2014)CrossRefGoogle Scholar
  3. 3.
    IEEE standard for information technology-telecommunications and information exchange between systems-local and metropolitan area networks-specific requirements - part 11: wireless lan medium access control (MAC) and physical layer (PHY) specifications (2007).
  4. 4.
    IEEE standard for local and metropolitan area networks part 16: air interface for fixed and mobile broadband wireless access systems amendment for physical and medium access control layers for combined fixed and mobile operation in licensed bands (2005).
  5. 5.
    Yu, J., Chong, P.: A survey of clustering schemes for mobile ad hoc networks. IEEE Commun. Surv. Tutor. 7(1), 32–48 (2005)CrossRefGoogle Scholar
  6. 6.
    Gerla, M., Tsai, J.T.C.: Multicluster, mobile, multimedia radio network. Wirel. Netw. 1, 255–265 (1995). doi: 10.1007/BF01200845 CrossRefGoogle Scholar
  7. 7.
    Basu, P., Khan, N., Little, T.D.: A mobility based metric for clustering in mobile ad hoc networks. In: International Workshop on Wireless Networks and Mobile Computing (WNMC2001), pp. 413–418, April 2001Google Scholar
  8. 8.
    Zhang, Z.: A novel multi-hop clustering scheme for vehicular ad-hoc networks. In: Proceedings of the 9th ACM International Symposium on Mobility Management and Wireless Access (2011)Google Scholar
  9. 9.
    Azizian, M., Cherkaoui, S., Hafid, A.: A distributed D-hop cluster formation for VANET. In: IEEE Wireless Communications and Networking Conference (WCNC 2016), Doha, Qatar (2016)Google Scholar
  10. 10.
    Ucar, S., Ergen, S., Ozkasap, O.: VMaSC: vehicular multi-hop algorithm for stable clustering in vehicular ad hoc networks. In: Wireless Communications and Networking Conference (WCNC), Shanghai (2013)Google Scholar
  11. 11.
    Vodopivec, S., Bester, J., Kos, A.: A survey on clustering algorithms for vehicular ad-hoc networks. In: 35th International Conference on Telecommunications and Signal Processing, Prague (2012)Google Scholar
  12. 12.
    Varga, A.: OMNET ++ discrete event simulation system user manual, version 4.2.2 (2011)Google Scholar
  13. 13.
    Sommer, C., German, R., Dressler, F.: Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mob. Comput. 10(1), 3–15 (2011)CrossRefGoogle Scholar
  14. 14.
    Behrisch, M., Erdmann, J., Krajzewicz, D., Bieker, L.: SUMO—simulation of urban mobility: an overview. In: The Third International Conference on Advances in System Simulation, SIMUL, pp. 63– 68 (2011)Google Scholar
  15. 15.
    Fan, W., Shi, Y., Chen, S., Zou, L.: A mobility metrics based dynamic clustering algorithm for VANETs. In: ICCTA, Beijing (2011)Google Scholar
  16. 16.
    Ouahou, S., Bah, S., Bakkoury, Z., Hafid, A.: Dynamic clustering algorithm based on beacon delay. In: The 11th International Conference on Intelligent Systems: Theories and Applications SITA (2016)Google Scholar
  17. 17.
    Ahizoune, A., Hafid, A.: A new stability based clustering algorithm (SBCA) for VANETs. In: IEEE 37th Conference on Local Computer Networks Workshops (LCN Workshops), Clearwater, FL (2012)Google Scholar
  18. 18.
    Azizian, M., Cherkaoui, S., Hafid, A.S.: A distributed cluster based transmission scheduling in VANET. In: 2016 IEEE International Conference on Communications (ICC). IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Soufiane Ouahou
    • 1
  • Slimane Bah
    • 1
  • Zohra Bakkoury
    • 1
  • Abdelhakim Hafid
    • 2
  1. 1.AMIPS Research Group, Mohammadia School of EngineersMohammed V UniversityRabatMorocco
  2. 2.Department of Computer Science and Operation ResearchUniversity of MontrealMontrealCanada

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