Skip to main content
Log in

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

An accurate localization technique is considered as the significant entity in vehicular ad hoc networks (VANETs) for facilitating emergency message data transmission in diversified critical safety applications. In VANETs, the system of global positioing is generally used for estimating the position of the vehicles in the network for attaining neighborhood awareness in the event of warning message dissemination. However, the existence of green foliages, buildings, indoor parking lots and urban streen canyons introduces NLOS situation that introduces unwanted errors that crumbles the degree of data dissemination in emergency situations. In this paper, spotted hyena and simulated annealing optimization algorithm (SHSAOA)-based positioning scheme was proposed for precise estimation of NLOS nodes. İt included the advantages of improved simulated annealing (SA) integrated into SHOA for establishing better balance between the process of exploitation and exploration in the search space. This positioning approach generated candidate solutions by deriving the merits of the trajectory-based charateristics of SA throughout the algorithmic development process in order to improve the local optimization process. This proposed SHSAOA utilized the distance infotmation that are associated with the vehicle trajectory, number of vehicles and error in distance information for assessing the precise location of the NLOS nodes in the network. The simulation results of the proposed SHSAOA scheme confirmed minimized localization error with maximized accuracy in transmission, warning message transmission rate, channel utilization degree and neighborhood awareness degree with different vehicular density and NLOS nodes.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

Data sharing not applicable—no new data generated, Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  1. Cruz, S. B., Abrudan, T. E., Xiao, Z., Trigoni, N., & Barros, J. (2017). Neighbor-aided localization in vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2693–2702.

    Article  Google Scholar 

  2. Tripathi, R., Prakash, A., & Gupta, N. (2017). Clustering-based enhanced safety message dissemination medium access control protocol for vehicular ad hoc network. International Journal of Ad Hoc and Ubiquitous Computing, 24(1/2), 76.

    Article  Google Scholar 

  3. Singh, B., Kavitha, P., Regin, R., Praghash, D., Sujatha, S., & Rajest, D. S. (2020). Optimized node clustering based on received signal strength with particle ordered-filter routing used in VANET. Webology, 17(2), 262–277.

    Article  Google Scholar 

  4. Soleymani, S. A., Goudarzi, S., Anisi, M. H., Kama, N., Adli Ismail, S., Azmi, A., Zareei, M., & Hanan Abdullah, A. (2020). A trust model using edge nodes and a cuckoo filter for securing VANET under the NLoS condition. Symmetry, 12(4), 609.

    Article  Google Scholar 

  5. Zhao, J., Zhang, Y., Ni, S., & Li, Q. (2020). Bayesian cooperative localization with NLOS and malicious vehicle detection in GNSS-challenged environments. IEEE Access, 8(3), 85686–85697.

    Article  Google Scholar 

  6. Ansari, A. R., Saeed, N., Ul Haq, M. I., & Cho, S. (2018). Accurate 3D localization method for public safety applications in vehicular ad-hoc networks. IEEE Access, 6(2), 20756–20763.

    Article  Google Scholar 

  7. Hu, N., Wu, C., Liu, P., Wu, H., Wu, B., & Cheng, L. (2015). Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments. Telecommunication Systems, 62(4), 641–655.

    Article  Google Scholar 

  8. Amuthan, A., & Kaviarasan, R. (2018). Weighted distance hyperbolic prediction-based detection scheme for non line of sight nodes in VANETs. Journal of King Saud University—Computer and Information Sciences, 2(1), 45–56.

    Google Scholar 

  9. Nascimento, P., Kimura, B., Guidoni, D., & Villas, L. (2018). An integrated dead reckoning with cooperative positioning solution to assist GPS NLOS using vehicular communications. Sensors, 18(9), 2895.

    Article  Google Scholar 

  10. Janakiraman, S. (2020). An improved rank criterion-based NLOS node detection mechanism in VANETs. International Journal of Intelligent Unmanned Systems, 9(1), 1–15.

    Article  Google Scholar 

  11. Min, H., Wu, X., Cheng, C., & Zhao, X. (2019). Kinematic and dynamic vehicle model-assisted global positioning method for autonomous vehicles with low-cost GPS/Camera/in-vehicle sensors. Sensors, 19(24), 5430.

    Article  Google Scholar 

  12. Zubairu, B. (2017). Novel approach of spoofing attack in VANET location verification for non-line-of-Sight (NLOS). Innovations in Computational Intelligence, 2(1), 45–59.

    Google Scholar 

  13. Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114(2), 48–70.

    Article  Google Scholar 

  14. Huang, Z., Lin, Z., Zhu, Z., & Chen, J. (2020). An improved simulated annealing algorithm with excessive length penalty for fixed-outline Floorplanning. IEEE Access, 8(1), 50911–50920.

    Article  Google Scholar 

  15. Jia, H., Li, J., Song, W., Peng, X., Lang, C., & Li, Y. (2019). Spotted hyena optimization algorithm with simulated annealing for feature selection. IEEE Access, 7(2), 71943–71962.

    Article  Google Scholar 

  16. Soleymani, S. A., Abdullah, A. H., Zareei, M., Anisi, M. H., Vargas-Rosales, C., Khurram Khan, M., & Goudarzi, S. (2017). A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access, 5(3), 15619–15629.

    Article  Google Scholar 

  17. Lobo, F., Grael, D., Oliveira, H., Villas, L., Almehmadi, A., & El-Khatib, K. (2019). Cooperative localization improvement using distance information in vehicular ad hoc networks. Sensors, 19(23), 5231.

    Article  Google Scholar 

  18. Alodadi, K., Al-Bayatti, A. H., & Alalwan, N. (2017). Cooperative volunteer protocol to detect non-line of sight nodes in vehicular ad hoc networks. Vehicular Communications, 9(2), 72–82.

    Article  Google Scholar 

  19. Amuthan, A., & Kaviarasan, R. (2018). Weighted inertia-based dynamic virtual bat algorithm to detect NLOS nodes for reliable data dissemination in VANETs. Journal of Ambient Intelligence and Humanized Computing, 10(11), 4603–4613.

    Article  Google Scholar 

  20. Soleymani, S. A., Anisi, M. H., Abdullah, A. H., Ngadi, M. A., Goudarzi, S., Khan, M. K., & Kama, M. N. (2020). An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET. Science China Information Sciences, 63(12), 45–58.

    Article  MathSciNet  Google Scholar 

  21. Malar, C. J., Priya, D. M., & Janakiraman, S. (2020). Harris Hawk optimization algorithm-based effective localization of non-line-of-sight nodes for reliable data dissemination in vehicular ad hoc networks. International Journal of Communication Systems, 32(2), 65–78.

    Google Scholar 

  22. Balamurugan, A., Priya, M. D., Malar, A. C., & Janakiraman, S. (2021). Raccoon optimization algorithm-based accurate positioning scheme for reliable emergency data dissemination under NLOS situations in VANETs. Journal of Ambient Intelligence and Humanized Computing, 2(1), 56–72.

    Google Scholar 

  23. Kaviarasan, R., & Harikrishna, P. (2020). Localizing non-line-of-sight nodes in vehicluar adhoc networks using gray wolf methodology. International Journal of Communication Systems, 34(3), 88–99.

    Google Scholar 

  24. Malar, C. J., Priya, D. M., & Janakiraman, S. (2020). A hybrid crow search and gray wolf optimization algorithm-based reliable non-line-of-Sight node positioning scheme for vehicular ad hoc networks. International Journal of Communication Systems, 34(3), 23–35.

    Google Scholar 

  25. Mani, R., Jayaraman, S., & Ellappan, M. (2020). Hybrid seagull and thermal exchange optimization algorithm-based NLOS nodes detection technique for enhancing reliability under data dissemination in VANETs. International Journal of Communication Systems, 33(14), e4519.

    Article  Google Scholar 

  26. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Funding

There is no funding received for this research work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Tamilarasan.

Ethics declarations

Conflict of interest

The authors declare that there is no competing interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lenin, S.B., Tamilarasan, N. Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs. Wireless Pers Commun 128, 415–445 (2023). https://doi.org/10.1007/s11277-022-09961-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09961-y

Keywords

Navigation