Abstract
Internet of things connects any object to another and communicate with them using the most performed routing protocol. But in vehicular networks, topology and communication links frequently change due to the high mobility of vehicles. So, the key challenge of our work is to choose the best routing protocol using machine learning algorithms. When choosing routing protocol, most research focuses on the improvement of the performance of specific routing protocol using one machine learning algorithm. In this paper, we propose a solution in order to find the best routing protocol in such critical condition using machine learning algorithms in order to maximize the precision of the true positive rate. After the use of a specific algorithms such as Artificial Neural Network, Random Forest and Naive Bayes, we found that the last one is the best algorithm when it have all the true positive rate with a precision equal to 0.9987 to select the best routing protocol.
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References
Suthaputchakun, C., Sun, Z.: Routing protocol in intervehicle communication systems: a survey. IEEE Commun. Mag. 49(12), 150–156 (2011)
Lin, Y.-W., Chen, Y.-S., Lee, S.-L.: Routing protocols in vehicular ad hoc networks: a survey and future perspectives. J. Inf. Sci. Eng. 26(3), 913–932 (2010)
Marzak, B., et al.: Clustering in vehicular ad-hoc network using artificial neural network. Int. Rev. Comput. Softw. (IRECOS) 11(6), 548–556 (2016)
Saha, S., Roy, U., Sinha, D.D.: AODV routing protocol modification with Dqueue (dqAODV) and optimization with neural network for VANET in City Scenario. In: MATEC Web of Conferences, vol. 57, p. 02001. EDP Sciences (2016)
Zhao, L., Li, Y., Meng, C., Gong, C., Tang, X.: A SVM based routing scheme in VANETs. In: 2016 16th International Symposium on Communications and Information Technologies (ISCIT), pp. 380–383. IEEE (2016)
Huang, B., Mo, J., Cheng, X.: Improving security and stability of AODV with fuzzy neural network in VANET. In: Chellappan, S., Cheng, W., Li, W. (eds.) WASA 2018. LNCS, vol. 10874, pp. 177–188. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94268-1_15
Prieto, A., et al.: Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214, 242–268 (2016)
Zhang, H.: The optimality of Naive Bayes. AA 1(2), 3 (2004)
Segal, M.R.: Machine learning benchmarks and random forest regression (2004)
Park, N.-U., Nam, J.-C., Cho, Y.-Z.: Impact of node speed and transmission range on the hello interval of MANET routing protocols. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 634–636. IEEE (2016)
Ahmed, B., Mohamed, R., Mohamed, O.: Queue length and mobility aware routing protocol for mobile ad hoc network. Int. J. Commun. Netw. Inf. Secur. 4(3), 207 (2012)
Bindra, P., Kaur, J., Singh, G.: Effect of TTL parameter variation on performance of AODV route discovery process. Int. J. Comput. Appl. 70(4) (2013)
Clausen, T., Jacquet, P.: RFC3626: Optimized Link State Routing Protocol (OLSR). RFC Editor (2003)
Johnson, D.B., Maltz, D.A., Broch, J., et al.: DSR: the dynamic source routing protocol for multi-hop wireless ad hoc networks. Ad hoc Netw. 5(1), 139–172 (2001)
Ksouri, C., Jemili, I., Mosbah, M., Belghith, A.: VANETs routing protocols survey: classifications, optimization methods and new trends. In: Jemili, I., Mosbah, M. (eds.) DiCES-N 2019. CCIS, vol. 1130, pp. 3–22. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40131-3_1
Ayodele, T.O.: Machine learning overview. New Adv. Mach. Learn. 9–19 (2010)
Bouckaert, R.R., et al.: WEKA-experiences with a Java open-source project. J. Mach. Learn. Res. 11, 2533–2541 (2010)
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Hadrich, A., Makhlouf, A.M., Zarai, F. (2021). Learning-Based Evaluation of Routing Protocol in Vehicular Network Using WEKA. In: He, X., Shao, E., Tan, G. (eds) Network and Parallel Computing. NPC 2020. Lecture Notes in Computer Science(), vol 12639. Springer, Cham. https://doi.org/10.1007/978-3-030-79478-1_5
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DOI: https://doi.org/10.1007/978-3-030-79478-1_5
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