VANET-based Secure Information Exchange for Smart Charging
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How to Cite

Chen, Joy Iong-Zong. 2021. “VANET-Based Secure Information Exchange for Smart Charging”. Journal of Electrical Engineering and Automation 2 (3): 141-45. https://doi.org/10.36548/jeea.2020.3.006.

Keywords

— Smart Charging
— Smart Grid
— Electric Vehicles
— Vehicular Ad-Hoc Network
— mobile edge computing
Published: 29-01-2021

Abstract

Smart inventions have led to smart grids, which have paved the way to smart charging means. This smart charging information about a vehicle needs to be properly maintained in order to use it to exchange information between charging stations and electric vehicles. In this work, we have introduced an efficient methodology for managing and disseminating energy from the charging station to the smart vehicle in an urban area where the number of electric vehicles is high. We have designed and implemented a novel concept involving mobile edge computing in VANET. Moreover, we have also used an effective mechanism for communicating the information on charging with the moving electric vehicles and servers. A local relay scheme is used for reducing redundant overheads, increase delivery efficiency of charging information. This scheme is implemented with delay wait model as the base. The output is recorded by means of simulation environment and based on the observations the proposed work is found to be optimal in maintaining, accessing and disseminating the charging information.

References

  1. Bautista, P. B., Cárdenas, L. L., Aguiar, L. U., & Igartua, M. A. (2019). A traffic-aware electric vehicle charging management system for smart cities. Vehicular Communications, 20, 100188.
  2. Li, G., Li, X., Sun, Q., Boukhatem, L., & Wu, J. (2020). An Effective MEC Sustained Charging Data Transmission Algorithm in VANET-Based Smart Grids. IEEE Access, 8, 101946-101962.
  3. Azam, F., Priyadarshi, N., Nagar, H., Kumar, S., & Bhoi, A. K. An Overview of Solar-Powered Electric Vehicle Charging in Vehicular Adhoc Network. Electric Vehicles, 95-102.
  4. Au, M. H., Liu, J. K., Zhang, Z., Susilo, W., Li, J., & Zhou, J. (2017). Anonymous announcement system (AAS) for electric vehicle in VANETs. The Computer Journal, 60(4), 588-599.
  5. Li, G., Boukhatem, L., Zhao, L., & Wu, J. (2018, February). Direct vehicle-to-vehicle charging strategy in vehicular ad-hoc networks. In 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1-5). IEEE.
  6. Cao, Y., Song, H., Kaiwartya, O., Zhou, B., Zhuang, Y., Cao, Y., & Zhang, X. (2018). Mobile edge computing for big-data-enabled electric vehicle charging. IEEE Communications Magazine, 56(3), 150-156.
  7. Khalid, A., Iftikhar, M. S., Almogren, A., Khalid, R., Afzal, M. K., & Javaid, N. A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs. Information Processing & Management, 58(2), 102464.
  8. Shirley, D. R. A., Ranjani, K., Arunachalam, G., & Janeera, D. A. (2020). Automatic Distributed Gardening System Using Object Recognition and Visual Servoing. In Inventive Communication and Computational Technologies (pp. 359-369). Springer, Singapore.
  9. Ponmani, D. S. J., Shirley, D. R. A., Aswini, G. V., Sumathi, K., & Dally, E. C. (2016). A diagnostic study of power switches. Advances in Natural and Applied Sciences, 10(17), 271-275.
  10. Chen, J. I. Z. (2019). 5G technology and advancements in connected living-comprehensive survey. Journal of Electronics, 1(02), 71-79.
  11. Smys, S., & Ranganathan, G. (2019). Robot assisted sensing control and manufacture in automobile industry. Journal of ISMAC, 1(03), 180-187.
  12. Bindhu, V. (2019). Green cloud computing solution for operational cost efficiency and environmental impact reduction. Journal of ISMAC, 1(02), 120-128.