The Journal of Supercomputing

, Volume 71, Issue 4, pp 1401–1426 | Cite as

A cluster-based vehicular cloud architecture with learning-based resource management

  • Hamid Reza Arkian
  • Reza Ebrahimi Atani
  • Abolfazl Diyanat
  • Atefe Pourkhalili


Recent advances in wireless communication technologies have made it possible to implement Intelligent Transportation Systems (ITS) to have more safety in roads and eliminating the excessive cost of traffic collisions. However, there are some resource limitations in mobile vehicles, which is a significant technical challenge in the deployment of new applications and advancement of ITS services. In this paper, a new vehicular cloud architecture is proposed which uses a clustering technique to solve the resource limitation problem by grouping the vehicles and cooperatively providing the resources. To be more specific, the clustering structure is made flexible using the fuzzy logic in the cluster head selection procedure. Resource management of the proposed architecture is improved by employing the Q-learning technique to select a service provider among participant vehicles as well as introducing three different queuing strategies to solve resource allocation problem. Finally, the performance of proposed architecture is evaluated using extensive simulation and its efficiency is demonstrated through comparison with other existing approaches.


Vehicular cloud Vehicular ad-hoc network (VANET) Clustering Fuzzy logic Q-Learning 


  1. 1.
    Al-Sultan S, Al-Doori MM, Al-Bayatti AH, Zedan H (2014) A comprehensive survey on vehicular Ad Hoc network. J Netw Comput Appl 37:380–392Google Scholar
  2. 2.
    Anda J, LeBrun J, Ghosal D, Chuah CN, Zhang M (2005) Vgrid: vehicular adhoc networking and computing grid for intelligent traffic control. In: IEEE 61st Vehicular Technology Conference, vol 5, pp 2905–2909Google Scholar
  3. 3.
    Arbabi H, Weigle M (2011) Monitoring free flow traffic using vehicular networks. In: IEEE Consumer Communications and Networking Conference (CCNC), pp 272–2760Google Scholar
  4. 4.
    Arkian HR, Atani RE, Kamali S (2014) Fcvca: A fuzzy clustering-based vehicular cloud architecture. In: 7th International Workshop on Communication Technologies for Vehicles (Nets4Cars-Fall), pp 24–28Google Scholar
  5. 5.
    Arkian HR, Atani RE, Pourkhalili A, Kamali S (2014) Cluster-based traffic information generalization in vehicular ad-hoc networks. Vehicular Communications
  6. 6.
    Boushaba M, Hafid A, Belbekkouche A (2011) Reinforcement learning-based best path to best gateway scheme for wireless mesh networks. In: IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp 373–379Google Scholar
  7. 7.
    Chen J, Cao X, Zhang Y, Xu W, Sun Y (2011) Measuring the performance of movement-assisted certificate revocation list distribution in VANET. Wirel Commun Mob Comput 11(7):888–898CrossRefGoogle Scholar
  8. 8.
    Das SK (2010) Mobile handset design. Wiley, Singapore. doi: 10.1002/9780470824696
  9. 9.
    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611CrossRefGoogle Scholar
  10. 10.
    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gener Comput Syst 29(1):84–106CrossRefGoogle Scholar
  11. 11.
    Gerla M, Tzu-Chieh Tsai J (1995) Multicluster, mobile, multimedia radio network. Wirel Netw 1(3):255–265CrossRefGoogle Scholar
  12. 12.
    Hosseininezhad S, Shirazi G, Leung VCM (2011) RLAB: Reinforcement learning-based adaptive broadcasting for Vehicular Ad-Hoc Networks. In: IEEE 73rd Vehicular Technology Conference (VTC Spring), pp 1–5Google Scholar
  13. 13.
    Huerta-Canepa G, Lee D (2010) A Virtual Cloud Computing Provider for Mobile Devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & #38; Services: Social Networks and Beyond, ACM, MCS ’10, pp 6:1–6:5Google Scholar
  14. 14.
    Hussain R, Son J, Eun H, Kim S, Oh H (2012) Rethinking vehicular communications: Merging VANET with cloud computing. In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp 606–609Google Scholar
  15. 15.
    Karagiannis G, Altintas O, Ekici E, Heijenk G, Jarupan B, Lin K, Weil T (2011) Vehicular networking: a survey and tutorial on requirements, architectures, challenges, standards and solutions. Commun Surv Tutor, IEEE 13(4):584–616Google Scholar
  16. 16.
    Klir GJ, St Clair U (1997) Fuzzy set theory: foundations and applications. Prentice-Hall Inc, Upper Saddle RiverzbMATHGoogle Scholar
  17. 17.
    Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. Comput, IEEE Trans C–26(12):1182–1191CrossRefGoogle Scholar
  18. 18.
    May A (1990) Traffic flow fundamentals. Prentice Hall, Englewood Cliffs, NJ, USAGoogle Scholar
  19. 19.
    Mendel J (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377CrossRefGoogle Scholar
  20. 20.
    Mershad K, Artail H (2013) Finding a STAR in a Vehicular Cloud. Intel Transp Syst Mag, IEEE 5(2):55–68CrossRefGoogle Scholar
  21. 21.
    Michael Behrisch JEDK, Bieker L (2011) SUMO-simulation of urban mobility: an overview. The Third International Conference on Advances in System Simulation, SIMUL pp 63–68Google Scholar
  22. 22.
    Mousannif H, Khalil I, Moatassime HA (2011) Cooperation as a service in VANETs. J Univers Comput Sci 17(8):1202–1218Google Scholar
  23. 23.
    Olariu S, Weigle MC (2009) Vehicular networks: from theory to practice, 1st edn. Chapman & Hall/CRC, Boca Raton, USAGoogle Scholar
  24. 24.
    Olariu S, Khalil I, Abuelela M (2011) Taking VANET to the clouds. Int J Pervasive Comput Commun 7(1):7–21CrossRefGoogle Scholar
  25. 25.
    Olariu S, Hristov T, Yan G (2013) The next paradigm shift: from vehicular networks to vehicular clouds. WileyGoogle Scholar
  26. 26.
    Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11(3):387–434CrossRefGoogle Scholar
  27. 27.
    Rawashdeh Z, Mahmud S (2012) A novel algorithm to form stable clusters in vehicular ad hoc networks on highways. EURASIP J Wirel Commun Netw 1:1–13CrossRefGoogle Scholar
  28. 28.
    Yu R, Zhang Y, Gjessing S, Xia W, Yang K (2013) Toward cloud-based vehicular networks with efficient resource management. IEEE Netw 27(5):48–55. doi: 10.1109/MNET.2013.6616115
  29. 29.
    Russel S, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, USAGoogle Scholar
  30. 30.
    Satyanarayanan M (1996) Fundamental challenges in mobile computing. In: Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing, ACM, PODC ’96, pp 1–7Google Scholar
  31. 31.
    Sommer C (2012) Vehicles in network simulation (VEINS).
  32. 32.
    Tal I, Muntean GM (2013) User-oriented fuzzy logic-based clustering scheme for vehicular ad-hoc networks. In: IEEE 77th Vehicular Technology Conference (VTC Spring), pp 1–5Google Scholar
  33. 33.
    Uzcategui R, Acosta-Marum G (2009) WAVE: a tutorial. IEEE Commun Mag 47(5):126–133CrossRefGoogle Scholar
  34. 34.
    Varga A (2011) OMNET++ discrete event simulation system user manual. 4.2.2Google Scholar
  35. 35.
    Venkataraman H, Delcelier R, Muntean GM (2013) A moving cluster architecture and an intelligent resource reuse protocol for vehicular networks. Wirel Netw 19(8):1881–1900CrossRefGoogle Scholar
  36. 36.
    Vodopivec S, Bester J, Kos A (2012) A survey on clustering algorithms for vehicular ad-hoc networks. In: 35th International Conference on Telecommunications and Signal Processing (TSP), pp 52–56Google Scholar
  37. 37.
    Wahab OA, Otrok H, Mourad A (2013) VANET QoS-OLSR: QoS-based clustering protocol for Vehicular Ad hoc Networks. Comput Commun 36(13):1422–1435CrossRefGoogle Scholar
  38. 38.
    Watkins C, Dayan P (1992) Technical note: Q-Learning. Mach Learn 8:279–292zbMATHGoogle Scholar
  39. 39.
    Whaiduzzaman M, Sookhak M, Gani A, Buyya R (2014) A survey on vehicular cloud computing. J Netw Comput Appl 40:325–344Google Scholar
  40. 40.
    Wisitpongphan N, Bai F, Mudalige P, Sadekar V, Tonguz O (2007) Routing in sparse vehicular ad hoc wireless networks. IEEE J Sel Areas Commun 25(8):1538–1556Google Scholar
  41. 41.
    Wu C, Ohzahata S, Kato T (2013) Flexible, portable, and practicable solution for routing in VANETs: a fuzzy constraint Q-learning approach. Veh Technol, IEEE Trans 62(9):4251–4263CrossRefGoogle Scholar
  42. 42.
    Yan G, Olariu S, Weigle M (2010) Cross-layer location verification enhancement in vehicular networks. In: IEEE Intelligent Vehicles Symposium (IV), pp 95–100Google Scholar
  43. 43.
    Zeadally S, Hunt R, Chen YS, Irwin A, Hassan A (2012) Vehicular ad hoc networks (VANETS): status, results, and challenges. Telecommun Syst 50(4):217–241CrossRefGoogle Scholar
  44. 44.
    Zrar Ghafoor K, Abu- Bakar K (2013) A fuzzy logic approach to beaconing for vehicular ad hoc networks. Telecommun Syst 52(1):139–149Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Hamid Reza Arkian
    • 1
  • Reza Ebrahimi Atani
    • 1
  • Abolfazl Diyanat
    • 2
  • Atefe Pourkhalili
    • 1
  1. 1.Department of Computer EngineeringUniversity of GuilanRashtIran
  2. 2.Department of Computer EngineeringUniversity of TehranTehranIran

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