RETRACTED ARTICLE: Improved AODV Based on TOPSIS and Fuzzy Algorithms in Vehicular Ad-hoc Networks

A Correction to this article is available

This article has been updated

Abstract

The ad-hoc on-demand distance vector (AODV) routing protocol is one of the most widely used routing protocols in VANETs. The AODV finds the shortest path that is not desirable in networks with high mobility. In addition, in the AODV, path request messages are broadcasted by the source and middle vehicles, which increases the routing overhead. However, in this paper, each vehicle selects the most reliable neighbors in order to send path request. This selection is based on the technique for order of preference by similarity to ideal solution algorithm. As a result, the destination vehicle receives the most reliable paths and uses the fuzzy algorithm to select the best route from the perspective of failure among all received routes. Simulation results show that the proposed method has lower end-to-end latency and higher throughput than the AODV.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Change history

  • 22 December 2020

    The Editor-in-Chief has retracted this article [1] because it contains material that substantially overlaps with the following article [2]. All authors do not agree to this retraction.

References

  1. 1.

    Eze, E. C., Zhang, S. J., Liu, E. J., et al. (2016). Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development. International Journal of Automation and Computing, 13(1), 1–18.

    Article  Google Scholar 

  2. 2.

    Ghori, M. R., Zamli, K. Z., Quosthoni, N., et al. (2018). Vehicular ad-hoc network (VANET). In IEEE international conference on innovative research and development (ICIRD) (pp. 1–6).

  3. 3.

    Kolluri, R. (2017). Reduction of routing overhead in vehicle-to-vehicle communication using clustering in vehicular ad hoc networks. Ph.D. thesis.

  4. 4.

    Liu, J., Wan, J., Wang, Q., et al. (2016). A survey on position-based routing for vehicular ad hoc networks. Telecommunication Systems, 62(1), 15–30.

    Article  Google Scholar 

  5. 5.

    Jiang, D., & Delgrossi, L. (2008). IEEE 802.11 p: Towards an international standard for wireless access in vehicular environments. In International conference in vehicle technology (pp 2036–2040).

  6. 6.

    Sharef, B. T., Alsaqour, R. A., & Ismail, M. (2014). Vehicular communication ad hoc routing protocols: A survey. Journal of Network and Computer Applications, 40, 363–396.

    Article  Google Scholar 

  7. 7.

    Mammeri, A., & Boukerche, A. (2017). Inter-vehicle communication of warning information: An experimental study. Wireless Networks, 23, 1837–1848.

    Article  Google Scholar 

  8. 8.

    Dunning, G. J., Hsu, T. Y., Pepper, D. M., et al (2018). Inter vehicle communication system. https://patents.google.com/patent/US8307037B2/en. Redrived data August 10, 2018.

  9. 9.

    Suzuki, T., & Fujii, T. (2017). Joint routing and spectrum allocation for multi-hop inter-vehicle communication in cognitive radio networks. Intelligent Transportation Systems Research, 15, 39–49.

    Article  Google Scholar 

  10. 10.

    Rosati, L., Berioli, M., & Reali, G. (2008). On ant routing algorithms in ad hoc networks with critical connectivity’. Ad Hoc Networks, 6, 827–859.

    Article  Google Scholar 

  11. 11.

    Samara, G., & Al-Raba’nah, Y. (2017). Security issues in vehicular Ad Hoc networks (VANET): A survey. arXiv preprint arXiv:1712.04263.

  12. 12.

    Reddy, T. B., Karthigeyan, I., Manoj, B. S., et al. (2006). Quality of service provisioning in ad hoc wireless networks: A survey of issues and solutions. Ad Hoc Networks, 4, 83–124.

    Article  Google Scholar 

  13. 13.

    Perkins, C., Belding-Royer, E., Das, S. (2003). RFC3561: Ad hoc on-demand distance vector (AODV) routing, Network Working Group, July 2003.

  14. 14.

    Yoon, K., & Hwang, C. L. (1981). TOPSIS (technique for order preference by similarity to ideal solution)—A multiple attribute decision making, w: Multiple attribute decision making–methods and applications, a state-of-the-at survey. Berlin: Springer.

    Google Scholar 

  15. 15.

    Kayacan, E., & Khanesar, M. A. (2015). Fuzzy neural networks for real time control applications. Amsterdam: Elsevier.

    Google Scholar 

  16. 16.

    Zadeh, L. A. (1965). Fuzzy sets. Journal of Information and Control, 8, 338–353.

    Article  Google Scholar 

  17. 17.

    Lenders, V., Wagner, J., May, M. (2006). Analyzing the Impact of Mobility in Ad Hoc Networks. In 2nd Int. workshop on Multi-hop ad hoc networks (pp. 39–46). Florence, Italy, May 26–26, 2006.

  18. 18.

    Mullen, J., & Huang, H. (2005). Impact of multipath fading in wireless ad hoc networks. In 2nd ACM workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (pp. 181–188).

  19. 19.

    Aoki, M., Saito, M., Aida, H., & Tokuda, H. (2003). ANARCH: A name resolution scheme for mobile ad hoc networks. In International Conference on advanced information networking and applications.

  20. 20.

    Djenouri, D., & Badache, N. (2003). An energy efficient routing protocol for mobile ad hoc network. In The 2nd proceeding of the Mediterranean workshop on ad-hoc networks.

  21. 21.

    Perkins, C. E., Bhagwat, P. (1994). Highly Dynamic Destination Sequenced Distance-Vector Routing (DSDV) for mobile computers. Proceedings of the conference on communications architectures, protocols and applications (pp. 234–244). London, United Kingdom, August 31–September 02 1994.

  22. 22.

    Clausen, T., Jacquet, P. (2003). RFC 3626: Optimized link state routing protocol (OLSR), Network Working Group, October 2003.

  23. 23.

    Hanzo, L., & Tafazolli, R. (2007). A survey of QoS routing solutions for mobile ad hoc networks’. IEEE Communications Surveys & Tutorials, 9, 50–70.

    Article  Google Scholar 

  24. 24.

    Eiza, M. H., Owens, T., Ni, Q., & Shi, Q. (2015). Situation-aware QoS routing algorithm for vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 64, 5520–5535.

    Article  Google Scholar 

  25. 25.

    Jaffe, J. M. (1984). Algorithms for finding paths with multiple constraints. Networks, 14, 95–116.

    MathSciNet  Article  Google Scholar 

  26. 26.

    Korkmaz, T., & Krunz, M. (2001) Multi-constrained optimal path selection. INFOCOM (pp. 834–843).

  27. 27.

    Chatterjee, S., & Das, S. (2015). Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad hoc network. Journal of Information Science, 295, 67–90.

    MathSciNet  Article  Google Scholar 

  28. 28.

    NS2 Software (2018). https://github.com/hbatmit/ns2.35, Redrived data August 10, 2018.

  29. 29.

    Krajzewicz, D., Erdmann, J., Behrisch, M., et al. (2012). Recent development and applications of SUMO—Simulation of urban mobility. International Journal on Advances in Systems and Measurements, 5, 128–138.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Reza Hooshmand.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11277-020-08026-2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Amiri, E., Hooshmand, R. RETRACTED ARTICLE: Improved AODV Based on TOPSIS and Fuzzy Algorithms in Vehicular Ad-hoc Networks. Wireless Pers Commun 111, 947–961 (2020). https://doi.org/10.1007/s11277-019-06894-x

Download citation

Keywords

  • Vehicular ad-hoc network (VANET)
  • TOPSIS algorithm
  • Fuzzy algorithm
  • AODV