Volume 19, Issue 2 (9-2022)                   JSDP 2022, 19(2): 61-72 | Back to browse issues page

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saeedi N, Babaie S. A New Hybrid Routing Algorithm based on Genetic Algorithm and Simulated Annealing for Vehicular Ad hoc Networks. JSDP 2022; 19 (2) :61-72
URL: http://jsdp.rcisp.ac.ir/article-1-1133-en.html
Islamic Azad University of Tabriz
Abstract:   (281 Views)
In recent years, Vehicular Ad-hoc Networks (VANET) as an emerging technology have tried to reduce road damage and car accidents through intelligent traffic controlling. In these networks, the rapid movement of vehicles, topology dynamics, and the limitations of network resources engender critical challenges in the routing process. Therefore, providing a stable and reliable routing algorithm is a necessary requirement to maintain the Quality of Service (QoS) parameters of VANETs. In this paper, a new routing algorithm based on the clustering technique is proposed, which is called GCAR. In the proposed algorithm, the appropriate cluster heads are selected based on the genetic algorithm then two vehicles are selected between the neighboring clusters as the gateways and a vehicle chain is formed by these vehicles. Moreover, a combination of genetic algorithm and simulated annealing is applied to identify the suitable clusters. The conducted simulations in MATLAB tool indicate that, respectively, path discovery ratio, the number of clusters, throughput, and packet delivery ratio of the proposed algorithm have been improved by 18.4%, 2.55%, 3.45%, and 14.18% in comparison to the PassCAR approach. Furthermore, evaluation of the convergence, standard deviation, and standard error of the proposed algorithm prove its high convergence speed and stability.
Article number: 5
Full-Text [PDF 1322 kb]   (139 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2020/04/12 | Accepted: 2021/05/24 | Published: 2022/09/30 | ePublished: 2022/09/30

1. [1] A. Ebrahimi and S.O. Pourdanesh, "The Use of Cognitive Radio Networks Routing to Reduce Energy Consumption in Wireless Sensor Networks Dayjkstra Algorithm," JSTE, vol. 1, pp. 31-38, 2017.
2. [2] A. Shadnia and M. Romoozi, "Proposing Enhanced Strategy for Transmitting and Routing In Content-Based Vehicular Ad Hoc Networks Based On Geographical Map," Road Quarterly Journal, vol. 25, pp. 31-38, 2018.
3. [3] R. Aranbezhad and S. Babaie, "A routing algorithm based on movement direction and position of vehicles for vehicular Ad hoc networks," Journal of Soft Computing and Information Technology, vol. 9, pp. 206-213, 2020.
4. [4] S. Ebrahimi Mood, M. M. Javidi, M. R. Khosravi, "Proposing a Constrained-GSA for the Vehicle Routing Problem," Journal of Signal and Data Processing, vol. 18 (4), pp. 23-36, 2022.
5. [5] H. Hartenstein and K. P. Laberteaux, "A tutorial survey on vehicular ad hoc networks," IEEE Commun. Mag., vol. 46, pp. 164-171, Jun. 2008. [DOI:10.1109/MCOM.2008.4539481]
6. [6] M. Afrashteh and S. Babaie, "A Route Segmented Broadcast Protocol based on RFID for Emergency Message Dissemination in Vehicular Ad-hoc Networks," IEEE Transactions on Vehicular Technol.ogy, vol. 69, pp. 16017-16026, 2020. [DOI:10.1109/TVT.2020.3041754]
7. [7] A. Rasheed, S. Gillani, S. Ajmal, and A. Qayyum, "Vehicular Ad Hoc Network (VANET): A Survey, Challenges, and Applications," in Vehicular Ad-Hoc Networks for Smart Cities, A. Laouiti, A. Qayyum, and M. Mohamad Saad, Eds. Springer, Singapore, 2017, pp. 39-51. [DOI:10.1007/978-981-10-3503-6_4]
8. [8] S. Boussoufa-lahlah, F. Semchedine, and L. Bouallouche-medjkoune, "Geographic routing protocols for Vehicular Ad hoc NETworks ( VANETs ): A survey," Veh. Commun., vol. 11, pp. 20-31, 2018. [DOI:10.1016/j.vehcom.2018.01.006]
9. [9] A. Ullah, S. Yaqoob, M. Imran, and H. Ning, "Emergency Message Dissemination Schemes Based on Congestion Avoidance in VANET and Vehicular FoG Computing," IEEE Access, vol. 7, pp. 1570-1585, 2019. [DOI:10.1109/ACCESS.2018.2887075]
10. [10] C. Cooper, D. Franklin, F. Safaei, and M. Abolhasan, "A Comparative Survey of VANET Clustering Techniques," IEEE Commun. Surv. Tutorials, vol. 19, pp. 657-681, 2019. [DOI:10.1109/COMST.2016.2611524]
11. [11] X. Zeng, M. Yu, and D. Wang, "A New Probabilistic Multi-Hop Broadcast Protocol for Vehicular Networks," IEEE Trans. Veh. Technol., vol. 67, pp. 12165-12176, 2018. [DOI:10.1109/TVT.2018.2872998]
12. [12] F. Cunha et al., "Data communication in VANETs: Protocols, applications and challenges," Ad Hoc Networks, vol. 44, pp. 90-103, 2016. [DOI:10.1016/j.adhoc.2016.02.017]
13. [13] F. Li, X. Song, H. Chen, X. Li, and Y. Wang, "Hierarchical routing for vehicular Ad Hoc networks via reinforcement learning," IEEE Trans. Veh. Technol., vol. 68, pp. 1852-1865, 2019. [DOI:10.1109/TVT.2018.2887282]
14. [14] S. S. Wang and Y. S. Lin, "PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks," Comput. Commun., vol. 36, pp. 170-179, 2013. [DOI:10.1016/j.comcom.2012.08.013]
15. [15] K. C. Lin, J. C. Hung, and J. ting Wei, "Feature selection with modified lion's algorithms and support vector machine for high-dimensional data," Applied Soft Computing Journal, vol. 68. pp. 669-676, 2018. [DOI:10.1016/j.asoc.2018.01.011]
16. [16] M. Fahad et al., "Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks," Comput. Electr. Eng, vol. 70, pp. 853-870, 2018. [DOI:10.1016/j.compeleceng.2018.01.002]
17. [17] H. Bagherlou and A. Ghaffari, "A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks," J. Supercomput., vol. 74, pp. 2528-2552, Jun. 2018. [DOI:10.1007/s11227-018-2283-z]
18. [18] S. Chatterjee and S. Das, "Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network," Inf. Sci. (Ny)., vol. 295, pp. 67-90, 2015. [DOI:10.1016/j.ins.2014.09.039]
19. [19] D. Sedighizadeh and H. Mazaheripour, "Optimization of multi objective vehicle routing problem using a new hybrid algorithm based on particle swarm optimization and artificial bee colony algorithm considering Precedence constraints," Alexandria Eng. J., vol. 57, pp. 2225-2239, 2018. [DOI:10.1016/j.aej.2017.09.006]

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