Agent Technology Based Resource Allocation for Fog Enhanced Vehicular Services

  • Daneshwari I. HattiEmail author
  • Ashok V. Sutagundar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)


IoT comprising heterogenous devices with varied and constrained resources imposes challenge in managing the available network resources. The new technology raised to solve these challenges is fog computing. In this research work, Agent technology for Fog enhanced vehicular services model is proposed. For managing the resources at the edge of the network fog is used and cloud agency is used for providing services to the tasks that are not given by the fog. The proposed work is designed and simulated using cloudsim tool and analysed using cloud analyst tool. Performance measures such as resource utilization, allocation time and congestion rate is measured and resulted with better resource utilization, less allocation time and reduced congestion rate.


Agent Cloud agency Fog agency Game theory Resource allocation 



The authors are thankful for the college and AICTE for the support in 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-1/2016-17 dated August 02, 2017.


  1. 1.
    Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49(8), 112–116 (2016)CrossRefGoogle Scholar
  2. 2.
    Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, New York, NY, USA, pp. 37–42 (2015)Google Scholar
  3. 3.
    Ketel, M.: Fog-cloud services for IoT. In: Proceedings of the SouthEast Conference, New York, NY, USA, pp. 262–264 (2017)Google Scholar
  4. 4.
    Luan, T.H., Gao, L., Li, Z., Xiang, Y., Wei, G., Sun, L.: Fog computing: focusing on mobile users at the edge. arXiv:1502.01815 Cs, February 2015
  5. 5.
    Yannuzzi, M., Milito, R., Serral-Gracià, R., Montero, D., Nemirovsky, M.: Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 325–329 (2014)Google Scholar
  6. 6.
    Aazam, M., Huh, E.: Fog computing and smart gateway based communication for cloud of things. In: 2014 International Conference on Future Internet of Things and Cloud, pp. 464–470 (2014)Google Scholar
  7. 7.
    Bao, W., et al.: sFog: seamless fog computing environment for mobile IoT applications. In: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems - MSWIM 2018, Montreal, QC, Canada, pp. 127–136 (2018)Google Scholar
  8. 8.
    Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5(1), 108–119 (2017)CrossRefGoogle Scholar
  9. 9.
    Sutagundar, A.V., Manvi, S.S.: Wheel based event triggered data aggregation and routing in wireless sensor networks: agent based approach. Wirel. Pers. Commun. 71(1), 491–517 (2013)CrossRefGoogle Scholar
  10. 10.
    Aazam, M., Huh, E.: Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, pp. 687–694 (2015)Google Scholar
  11. 11.
    Xu, X., Yu, H.: A game theory approach to fair and efficient resource allocation in cloud computing. Math. Probl. Eng. (2014). Accessed 02 Nov 2018
  12. 12.
    Wang, Z., Xu, W., Yang, J., Peng, J.: A game theoretic approach for resource allocation based on ant colony optimization in emergency management. In: 2009 International Conference on Information Engineering and Computer Science, pp. 1–4 (2009)Google Scholar
  13. 13.
    Nezarat, A., Dastghaibifard, G.: Efficient Nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 1–5 (2015)Google Scholar
  14. 14.
    Sutagundar, A.V., Manvi, S.S.: Fish bone structure based data aggregation and routing in wireless sensor network: multi-agent based approach. Telecommun. Syst. 56(4), 493–508 (2014)CrossRefGoogle Scholar
  15. 15.
    Sutagundar, A.V., Attar, A.H., Hatti, D.I.: Resource allocation for fog enhanced vehicular services. Wireless Pers. Commun. 1–19 (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationBLDEA’s V.P. Dr. P.G.Halakatti College of Engineering and TehnologyVijayapurIndia
  2. 2.Department of Electronics and CommunicationBasveshwar Engineering CollegeBagalkotIndia

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