Abstract
In vehicular ad hoc networks, the vehicle speed can exceed 120 kmph. Therefore, any node can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To overcome these problems, many routing protocols based clustering technique have been proposed. Indeed, the poor assignment of vehicles to clusters is the most important shortcomings where an inaccurate affiliation may reduce the algorithm’s effectiveness and disrupt all results and analyzes. Therefore, in this paper, we used a hybrid genetic algorithm to improve the cluster maintenance phase in our Weighted K-medoid Clustering Algorithm (WKCA) proposed recently. The proposed model incorporated the tabu search within genetic algorithm to allow the scan of all search space and to reach the best solution without falling into the local optima. This model improves the assignment of nodes to clusters, which in turn achieves efficient vehicle communication and ensures more stability in clustered architecture. Based on relevant metrics, the results obtained in a simulation game show that the enhanced model (E-WKCA) achieves more stability and robustness when compared to the original algorithm (WKCA) and other approaches designed for the same objective.
Similar content being viewed by others
References
Hajlaoui, R., Moulahi, T., & Guyennet, H. (2018). Vehicular ad hoc networks: from simulations to real-life scenarios. Journal of Fundamental and Applied Sciences, 10(4S), 632–637.
da Cunha, F. D., Boukerche, A., Villas, L., Viana, A. C., & Loureiro, A. A. F. (2014). Data communication in VANETs: A survey, challenges and applications. Research Report No 8498.
Singh, P. K., Lego, K., & Tuithung, T. (2011). Simulation based analysis of adhoc routing protocol in urban and highway scenario of VANET. International Journal of Computer Applications (0975–8887) 12(10).
Toutouh, J., Garcia-Nieto, J., & Alba, E. (2012). Intelligent OLSR routing protocol optimization for VANETs. IEEE Transactions on Vehicular Technology, 61(4), 1884–1894.
Weea, H. M., & Yang, P. C. (2004). The optimal and heuristic solutions of a distribution network. European Journal of Operational Research, 158(3), 626–632.
Hajlaoui, R., Guyennet, H., & Moulahi, T. (2016). A survey on heuristic-based routing methods in vehicular ad-hoc network: Technical challenges and future trends. IEEE Sensors Journal, 16(17), 6782–6792.
Baghel, M., Agrawal, S., & Silakari, S. (2012). Survey of metaheuristic algorithms for combinatorial optimization. International Journal of Computer Applications, 58(19), 21–31.
Yang, X.-S. (2011). Metaheuristic optimization. Scholarpedia, 6(8), 11472.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308.
Hakima, A. (2012). “sur l’hybridation des métaheuristiques”, mémoire de magistère.
El-Mihoub, T. A., Hopgood, A. A., Nolle, L., & Battersby, A. (2006). Hybrid genetic algorithms: A review. Engineering Letters, 13(2), 124–137.
Hajlaoui, R., Moulahi, T., & Guyennet, H. (2018). A weighted k-medoids clustering algorithm for effective stability in vehicular ad hoc networks. IEEE Journal of Communications and Networks.
Kumar, S. N., & Panneerselvam, R. (2012). A survey on the vehicle routing problem and its variants. Intelligent Information Management Journal, 4(03), 66.
Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., et al. (2011). Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions. IEEE Communications Surveys & Tutorials, 13(4), 584–616.
Balaji, S., Sureshkumar, S., & Saravanan, G. (2013). Cluster based ant colony optimization routing for vehicular ad hoc networks. International Journal of Scientific & Engineering Research, 4(6), 26–30.
Sahoo, A., Swain, S. K., Pattanayak, B. K., & Mohanty, M. N. (2016). An optimized cluster based routing technique in VANET for next generation network. In S. Satapathy, J. Mandal, S. Udgata, & V. Bhateja (Eds.), Information systems design and intelligent applications: Advances in intelligent systems and computing (Vol. 433). New Delhi: Springer.
Aadil, F., Bajwa, K. B., Khan, S., Chaudary, N. M., & Akram, A. (2016). CACONET: Ant Colony Optimization (ACO) based clustering algorithm for VANET. PLoS ONE, 11(5), e0154080.
Fathian, M., & Jafarian-Moghaddam, A. R. (2015). New clustering algorithms for vehicular ad-hoc network in a highway communication environment. Wireless Networks Journal, 21(8), 2765–2780.
Kumar, S. S., Rajaguru, D., Vengattaraman, T., Dhavachelvan, P., Jesline, A. J., & Amudhavel, J. (2016). Intelligent collision avoidance approach in VANET using artificial bee colony algorithm. In L. Suresh & B. Panigrahi (Eds.), Proceedings of the international conference on soft computing systems. Advances in intelligent systems and computing, vol. 398. New Delhi: Springer.
Harrabi, S., Jaafar, I. B., & Ghedira, K. (2016). A novel clustering algorithm based on agent technology for VANET. Network Protocols and Algorithms Journal, 8(2), 1–19.
Harrabi, S., Jaafar, I. B., & Ghedira, K. (2016). Novel optimized routing scheme for VANETs. In 7th International conference on emerging ubiquitous systems and pervasive networks (EUSPN), Berlin: Springer.
Hadded, M., Zagrouba, R., Laouiti, A., Muhlethaler, P. & Saidane, L. A. (2015). A multi-objective genetic algorithm-based adaptive weighted clustering protocol in VANET. In IEEE congress on evolutionary computation, Sendai, Japan, pp. 994–1002.
Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5(2), 193–204.
Moridi, E., & Barati, H. (2016). RMRPTS: A reliable multi-level routing protocol with tabu search in VANET. Telecommunication Systems, 65, 127–137.
M. Ren, L. Khoukhi, H. Labiod, J. Zhang and V. Veque, “A mobility-based scheme for dynamic clustering in vehicular ad-hoc networks (VANETs)”, IEEE/IFIP NOMS Workshop: International Workshop on Urban Mobility & Intelligent Transportation Systems (UMITS), 2016.
Kakkasageri, M. S., & Manvi, S. S. (2014). Multiagent driven dynamic clustering in VANETs. In Elsevier JNCA.
Mitchell, M. (1999). An introduction to genetic algorithms. A Bradford book.
http://www.obitko.com/tutorials/genetic-algorithms/selection.php
Bhattacharjya, R. K. (2015). Introduction to genetic algorithms. Department of Civil Engineering IIT Guwahati.
Hertz, A., & de Werra, D. (1990). The tabu search metaheuristic: How we used it. Annals of Mathematics and Artificial Intelligence, 1(1), 111–121.
Gendreau, M., & Potvin, J.-Y. (2010). Tabu Search. In Handbook of metaheuristics, international series in operations research & management science 146, Springer.
Hajlaoui, R., Gzara, M., Dammak, A. (2011). Hybrid model for solving multi-objective problems using evolutionary algorithm and tabu search. In World of computer science and information technology journal (WCSIT).
Bilge, U., Kurtulan, M., & Kırac, F. (2007). A tabu search algorithm for the single machine total weighted tardiness problem. European Journal of Operational Research, 176(3), 1423–1435.
Bhat, A. (2014). K-medoids clustering using partitioning around medoids for performing face recognition. International Journal of Soft Computing, Mathematics and Control, 3(3), 1–12.
Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of Medoids, in statistical data analysis based on the L1-norm and related methods. edited by Y. Dodge, North- Holland, pp. 405–416.
Park, H.-S., Lee, J.-S., & Jun, C.-H. (2006). A K-means-like algorithm for K-medoids clustering and its performance. Department of Industrial and Management Engineering, POSTECH, South Korea.
Bottaci, L. (2001). A genetic algorithm fitness function for mutation testing. Department of Computer Science, University of Hull, UK.
Najeeb, A. R., Aibinu, A. M., Nwohu, M. N., Salami, M. J. E., & Salau, H. B. (2016). Performance analysis of clustering based genetic algorithm. In IEEE international conference on computer and communication engineering (ICCCE), pp. 327–331.
Man, K. F., Tang, K. S., & Kwong, S. (1996). Genetic algorithms: Concepts and applications. IEEE Transactions on Industrial Electronics, 43(5), 519–534.
http://www.obitko.com/tutorials/genetic-algorithms/crossover-mutation.php.
https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html#top.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
The Network Simulator—https://www.nsnam.org/.
Fogue, M., Garrido, P., Martinez, F. J., Cano, J-C., Calafate, C. T., & Manzoni, P. (2012). A realistic simulation framework for vehicular networks. In SIMUTOOLS ‘12 Proceedings of the 5th international ICST conference on simulation tools and techniques, pp. 37–46.
Adhvaryu, K. U., & Kamboj, P. (2015). Efficient multicast ad hoc on-demand distance vector routing protocol. Journal of Network Communications and Emerging Technologies, 5(2).
Krishna, M. P. V., & Sebastain, M. P. (2006). HMAODV: History aware on multicast ad hoc on demand distance vector routing. In IEEE international symposium on ad hoc and ubiquitous computing.
Vidhale, B., & Dorle, S. S. (2011). Performance analysis of routing protocols in realistic environment for vehicular ad hoc networks. In IEEE 21st international conference on systems engineering.
Gulati, V., Tiwari, R., & Dumka, A. (2015). Evaluation of routing protocols in congested VANET environment. In IEEE, 2nd international conference on computing for sustainable global development (INDIACom).
Shahidi, R., & Ahmed, M. H. (2014). Probability distribution of end-to-end delay in a highway VANET. IEEE Communications Letters, 18(3), 443–446.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hajlaoui, R., Alsolami, E., Moulahi, T. et al. Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm. Telecommun Syst 71, 433–445 (2019). https://doi.org/10.1007/s11235-018-0513-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11235-018-0513-6