Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  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 37:380–392

  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 2905–2909

  3. Arbabi H, Weigle M (2011) Monitoring free flow traffic using vehicular networks. In: IEEE Consumer Communications and Networking Conference (CCNC), pp 272–2760

  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 24–28

  5. Arkian HR, Atani RE, Pourkhalili A, Kamali S (2014) Cluster-based traffic information generalization in vehicular ad-hoc networks. Vehicular Communications http://dx.doi.org/10.1016/j.vehcom.2014.08.003

  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 373–379

  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):888–898

    Article  Google Scholar 

  8. Das SK (2010) Mobile handset design. Wiley, Singapore. doi:10.1002/9780470824696

  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):1587–1611

    Article  Google Scholar 

  10. Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gener Comput Syst 29(1):84–106

    Article  Google Scholar 

  11. Gerla M, Tzu-Chieh Tsai J (1995) Multicluster, mobile, multimedia radio network. Wirel Netw 1(3):255–265

    Article  Google Scholar 

  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 1–5

  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 6:1–6:5

  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 606–609

  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):584–616

  16. Klir GJ, St Clair U (1997) Fuzzy set theory: foundations and applications. Prentice-Hall Inc, Upper Saddle River

    MATH  Google Scholar 

  17. Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. Comput, IEEE Trans C–26(12):1182–1191

    Article  Google Scholar 

  18. May A (1990) Traffic flow fundamentals. Prentice Hall, Englewood Cliffs, NJ, USA

  19. Mendel J (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377

    Article  Google Scholar 

  20. Mershad K, Artail H (2013) Finding a STAR in a Vehicular Cloud. Intel Transp Syst Mag, IEEE 5(2):55–68

    Article  Google Scholar 

  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 63–68

  22. Mousannif H, Khalil I, Moatassime HA (2011) Cooperation as a service in VANETs. J Univers Comput Sci 17(8):1202–1218

    Google Scholar 

  23. Olariu S, Weigle MC (2009) Vehicular networks: from theory to practice, 1st edn. Chapman & Hall/CRC, Boca Raton, USA

  24. Olariu S, Khalil I, Abuelela M (2011) Taking VANET to the clouds. Int J Pervasive Comput Commun 7(1):7–21

    Article  Google Scholar 

  25. Olariu S, Hristov T, Yan G (2013) The next paradigm shift: from vehicular networks to vehicular clouds. Wiley

  26. Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11(3):387–434

    Article  Google Scholar 

  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 1:1–13

    Article  Google Scholar 

  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. Russel S, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, USA

  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 1–7

  31. Sommer C (2012) Vehicles in network simulation (VEINS). http://veins.car2x.org

  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 1–5

  33. Uzcategui R, Acosta-Marum G (2009) WAVE: a tutorial. IEEE Commun Mag 47(5):126–133

    Article  Google Scholar 

  34. Varga A (2011) OMNET++ discrete event simulation system user manual. 4.2.2

  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):1881–1900

    Article  Google Scholar 

  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 52–56

  37. Wahab OA, Otrok H, Mourad A (2013) VANET QoS-OLSR: QoS-based clustering protocol for Vehicular Ad hoc Networks. Comput Commun 36(13):1422–1435

    Article  Google Scholar 

  38. Watkins C, Dayan P (1992) Technical note: Q-Learning. Mach Learn 8:279–292

    MATH  Google Scholar 

  39. Whaiduzzaman M, Sookhak M, Gani A, Buyya R (2014) A survey on vehicular cloud computing. J Netw Comput Appl 40:325–344

  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):1538–1556

  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):4251–4263

    Article  Google Scholar 

  42. Yan G, Olariu S, Weigle M (2010) Cross-layer location verification enhancement in vehicular networks. In: IEEE Intelligent Vehicles Symposium (IV), pp 95–100

  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):217–241

    Article  Google Scholar 

  44. Zrar Ghafoor K, Abu- Bakar K (2013) A fuzzy logic approach to beaconing for vehicular ad hoc networks. Telecommun Syst 52(1):139–149

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Guilan, Rasht, Iran

    Hamid Reza Arkian, Reza Ebrahimi Atani & Atefe Pourkhalili

  2. Department of Computer Engineering, University of Tehran, Tehran, Iran

    Abolfazl Diyanat

Authors
  1. Hamid Reza Arkian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Ebrahimi Atani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abolfazl Diyanat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atefe Pourkhalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Reza Arkian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arkian, H.R., Atani, R.E., Diyanat, A. et al. A cluster-based vehicular cloud architecture with learning-based resource management. J Supercomput 71, 1401–1426 (2015). https://doi.org/10.1007/s11227-014-1370-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1370-z

Keywords

Search

Navigation