Resource Allocation for Fog Enhanced Vehicular Services

  • Ashok V. Sutagundar
  • Ameenabegum H. Attar
  • Daneshwari I. HattiEmail author


Vehicular Ad hoc Networks mainly depends on the cloud computing for the services like storage, computing and networking. With the increase in the number of vehicles connected to the cloud, the problems like network congestion and increased delay arises. Thus, to solve such issues new technology is proposed i.e. fog computing. Fog Enhanced Vehicular Services (FEVS) provides the resources for computing, storage etc. for the vehicles but at the edge of the network. The proposed algorithms are simulated using the cloudsim and cloud reports tools, results are analyzed and compared using the cloud analysts tool. Better resource utilization is achieved for the proposed algorithm compared to other algorithms. FEVS model proves to be better than a cloud-only model. Proposed algorithm produces less latency, less response time, low Virtual Machine cost and percentage of utilization of resources is more compared to the cloud-only model.


Cloud computing Fog computing VANET Fog enhanced vehicular services Resource allocation and game theory 



The authors thank AICTE for the support and the college for doing the work. The work is funded by AICTE grant for carrying out the project “Resource Management in Internet of Things” Ref. No. File No. 8-40/RIFD/RPS/POLICY-1/2017-18 dated August 02, 2017.


  1. 1.
    Peter, N. (2015). FOG computing and its real time applications. International Journal of Emerging Technology and Advanced Engineering, 5(6), 266–269.Google Scholar
  2. 2.
    Deshmukh, U. A., & More, S. A. (2016). Fog computing: New approach in the world of cloud computing. International Journal of Innovative Research in Computer and Communication Engineering, 4(9), 16310–16316.Google Scholar
  3. 3.
    Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications and issues. In Mobidata ‘15 Proceedings of the 2015 workshop on mobile big data (pp. 37–42).Google Scholar
  4. 4.
    Li, J., Natalino, C., Van, D. P., Wosinska, L., & Chen, J. (2017). Resource management in fog-enhanced radio access network to support real-time vehicular services. In IEEE 1st international conference on fog and edge computing (ICFEC) (Vol. 17, pp. 68–74).Google Scholar
  5. 5.
    Wang, N., Varghese, B., Matthaiou, M., & Nikolopoulos, D. S. (2017). ENORM: A framework for edge node resource management. IEEE Transactions on Services Computing. CrossRefGoogle Scholar
  6. 6.
    Gu, L., Zeng, D., Guo, S., Barnawi, A., & Xiang, Y. (2015). Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing, 5(1), 108–119.CrossRefGoogle Scholar
  7. 7.
    Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014). Fog computing: A platform for internet of things and analytics. In Big data and internet of things: A roadmap for smart environments (pp. 169–186). SpringerGoogle Scholar
  8. 8.
    Xu, J., Palanisamy, B., Ludwig, H., & Wang, Q. (2017). Zenith: Utility-aware resource allocation for edge computing. In IEEE 1st International conference on edge computing (Vol. 2, pp. 47–54).Google Scholar
  9. 9.
    Ni, L., Zhang, J., Jiang, C., Yan, C., & Yu, K. (2017). Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet of Things Journal, 4(5), 1216–1228.CrossRefGoogle Scholar
  10. 10.
    Bhanu, K. N., Reddy, T. B., & Hanumanthappa, M. (2018). Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks. Egyptian Informatics Journal. CrossRefGoogle Scholar
  11. 11.
    Menon, V. G. (2017). Moving from vehicular cloud computing to vehicular fog computing: Issues and challenges. International Journal on Computer Science and Engineering (IJCSE), 9, 14–18.Google Scholar
  12. 12.
    Kopetz, H., Poledna, S. (2016). In-vehicle real-time fog computing. In 46th Annual IEEE/IFIP international conference on dependable systems and networks workshops (pp. 162–167).Google Scholar
  13. 13.
    Huang, C., Lu, R., & Choo, K. (2017). Vehicular fog computing: Architecture, use case, and security and forensic challenges. IEEE Communications Magazine, 55, 105–111.CrossRefGoogle Scholar
  14. 14.
    Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860–3872.CrossRefGoogle Scholar
  15. 15.
    Hu, Q. (2016). Reactive prediction model for cloud resource estimation. Ottawa-Carleton Institute for Electrical and Computer Engineering Department of Systems and Computer Engineering Carleton University (pp. 30–40).Google Scholar
  16. 16.
    Zhang, H., Zhang, Y., Gu, Y., Niyato, D., & Han, Z. (2017). A hierarchical game framework for resource management in fog computing. IEEE Communications Magazine, 55(8), 52–57.CrossRefGoogle Scholar
  17. 17.
    Sun, Y., & Zhang, N. (2017). A resource-sharing model based on a repeated game in fog computing. Saudi Journal of Biological Sciences, 24, 687–694.CrossRefGoogle Scholar
  18. 18.
    Yu, R., Zhang, Y., Gjessing, S., Xia, W., & Yang, K. (2013). Toward cloud-based vehicular networks with efficient resource management. arXiv:1308.6208v1

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ashok V. Sutagundar
    • 1
  • Ameenabegum H. Attar
    • 1
  • Daneshwari I. Hatti
    • 2
    Email author
  1. 1.Department of Electronics and CommunicationBasaveshwar Engineering CollegeBagalkotIndia
  2. 2.Department of Electronics and CommunicationBLDEA’s V.P. Dr. P.G. Halakatti College of Engineering and TechnologyVijayapurIndia

Personalised recommendations