Abstract
The vehicular Ad Hoc Network is a very dynamic network with uncertain presence of vehicles on the road and data transmission between vehicles and road-side units. Centralizing and distribution management of this network is stimulating. The management of the vehicles’ network starts with the Cluster Head selection and regulating its stability to be Cluster Head, for the maximum time possible. The clusters thus formed are further updated by Software-Defined Network. In the update of the cluster head, the Software-Defined Network selects that vehicle as Cluster Head, which has a higher probability of selection as a Primary User for available spectrum usage. It makes the vehicle more trustworthy as no malfunctioned vehicle is selected as a Primary User. In this paper, a 2-level approach for Primary User detection in the network has been proposed. The energy of the received signal is sensed and Primary User is detected by fuzzy logic and this decision is further used as a threshold for final Primary User selection. Other parameters like network connectivity level, lane weight, average velocity, and average distance along with trustworthiness by cognitive radio sensing are used to elect the Cluster Head by the SDN. The proposed approach shows higher stability in cluster head selection as compared to state-of-the-art schemes. Primary Users are also detected with zero percent probability of false detection in the proposed method.
Similar content being viewed by others
References
Liu, L., Chen, C., Ren, Z., & Yu, F. R. (2018). An intersection-based geographic routing with transmission quality guaranteed in urban VANETs. In IEEE International Conference on Communications (pp. 1–6).
Mahmood, A., Zhang, W. E., & Sheng, Q. Z. (2019). Software-defined heterogeneous vehicular networking: The arch. Design and open challenges. Future Internet, 11(3), 70.
Ozera, K., Bylykbashi, K., Liu, Y., & Barolli, L. (2018). A security-aware fuzzy-based cluster head selection system for VANETs. In International conference on innovative mobile and internet services in ubiquitous computing (pp. 505–516).
Bevish Jinila, K. (2014). Rough set based fuzzy scheme for clustering and cluster head selection in VANET. Elektronika Ir Elektrotechnika, 21(1), 54–59.
Rasheed, T., Rashdi, A., & Akhtar, A. N. (2018). Cooperative spectrum sensing using fuzzy logic for cognitive radio network. In International conference on advances in science and engineering technology (pp. 1–6).
Hafeez, K. A., Zhao, L., Liao, Z., & Ma, B. N. W. (2012). A fuzzy-logic-based cluster head selection algorithm in VANETs. In IEEE international conference on communications (pp. 203–207).
Bylykbashi, K., Elmazi, D., Matsuo, K., Ikeda, M., & Barolli, L. (2019). Effect of security and trustworthiness for a fuzzy cluster management system in VANETs. Cognitive Systems Research, 55, 153–163.
Alsuhli, G. H., Khattab, A., & Fahmy, Y. A. (2019). Double-head clustering for resilient VANETs. Wireless Communications and Mobile Computing, 2019, 1–17.
Ji, X., Yu, H., Fan, G., Sun, H., & Chen, L. (2018). Efficient and reliable cluster-based data transmission for vehicular ad hoc networks. Mobile Information Systems, 2018, 1–15.
Sowmya, M., Ramachandiran, K., & Senthilkumaran, R. (2018). An optimal and stable route selection in cluster based routing in VANET with reduced overhead. International Journal of Pure and Applied Mathematics, 119(14), 1121–1129.
Eze, J., Zhang, S., Liu, E., & Eze, E. (2017). Cognitive radio technology assisted vehicular ad-hoc networks (VANETs): Current status, challenges, and research trends. Proceedings of 23rd international conference on automation and computing, pp. 1–6
Elgaml, N., Khattab, A., & Mourad, H. A. (2017). Towards low-delay and high-throughput cognitive radio vehicular networks. ICT Express, 3(4), 183–187.
Lim, J. M. Y., Chang, Y. C., Alias, M. Y., & Loo, J. (2016). Cognitive radio network in vehicular ad hoc network: A survey. Cogent Engineering, 3(1), 1191–1194.
Huang, J., Zeng, X., Tan, X., Jian, X., & He, Y. (2017). Spectrum allocation for cognitive radio networks with non-deterministic bandwidth of spectrum hole. China Communications, 14(3), 87–96.
Kumar, K., Prakash, A., & Tripathi, R. (2017). A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game-theoretic auction theory approach. Physical Communication, 24, 19–33.
Khan, Z., Fan, P., Fang, S., & Abbas, F. (2019). An unsupervised cluster-based VANET-oriented evolving graph (CVoEG) model and associated reliable routing scheme. IEEE Transactions on Intelligent Transportation Systems, 20(10), C1–C4.
Goli-Bidgoli, S., & Movahhedinia, N. (2017). Determining vehicles’ radio transmission range for increasing cognitive radio VANET (CR-VANET) reliability using a trust management system. Computer Networks, 127, 340–351.
He, Y., Yu, F. R., Wei, Z., & Leung, V. (2019). Trust management for secure cognitive radio vehicular ad hoc networks. Ad Hoc Networks, 86, 154–165.
Afzal, H., Mufti, M. R., Awan, I., & Yousaf, M. (2019). Performance analysis of radio spectrum for cognitive radio wireless networks using discrete-time Markov chain. Journal of Systems and Software, 151, 1–7.
Elghamrawy (2018). Security in cognitive radio network: Defense against primary user emulation attacks using genetic artificial bee colony (GABC) algorithm. Future Generation Computer Systems, pp. 1–19.
Chen, M., Tian, Y., Fortino, G., Zhang, J., & Humar, I. (2018). Cognitive internet of vehicles. Computer Communications, 120, 58–70.
Lekomtcev, D., Kasem, E., & Marsalek, R. (2015). Matlab-based simulator of cooperative spectrum sensing in real channel conditions. In 25th International Conference Radioelektronika (pp. 209–212).
Liang, Y. C., Zeng, Y., Peh, E. C., & Hoang, A. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.
Dong, Q., Chen, Y., Li, X., Zeng, K., & Zimmermann, R. (2018). An adaptive primary user emulation attack detection mechanism for cognitive radio networks. In International conference on security and privacy in communication systems (pp. 297–317).
Oubabas, S., Aoudjit, R., Rodrigues, J. J., & Talbi, S. (2018). Secure and stable vehicular ad hoc network clustering algorithm based on hybrid mobility similarities and trust management scheme. Vehicular Communication, 13, 128–138.
Cheng, X., & Huang, B. (2019). A center-based secure and stable clustering algorithm for VANETs on highways. Wireless Communications and Mobile Computing, 1–10.
Alsuhli, G. H., Khattab, A., & Fahmy, Y. A. (2018). A mobility-based double-head clustering algorithm for dynamic VANET. In International Japan-Africa conference on electronics, communications, and computations (pp. 91–94).
Qi, W., Song, Q., Wang, X., Guo, L., & Ning, Z. (2018). SDN-enabled social-aware clustering in 5G-VANET systems. IEEE Access, 6, 28213–28224.
Mehmood, A., Khanan, A., Mohamed, A. H. H., Mahfooz, S., Song, H., & Abdullah, S. (2017). ANTSC: An intelligent Naïve Bayesian probabilistic estimation practice for traffic flow to form stable clustering in VANET. IEEE Access, 6, 4452–4461.
Khan, A. A., Abolhasan, M., & Ni, W. (2018). 5G next-generation VANETs using SDN and fog computing framework. In 15th IEEE Annual Consumer Comm. & Networking Conference (pp. 1–6).
Duan, X., Wang, X., Liu, Y., & Zheng, K. (2016). SDN enabled dual cluster head selection and adaptive clustering in 5G-VANET. In IEEE 84th vehicular technology conference (pp. 1–5).
Alioua, A., Senouci, S. M., & Moussaoui, S. (2017). dSDiVN: A distributed software-defined networking architecture for infrastructure-less vehicular networks. In International conference on innovations for community services (pp. 56–67).
Ghafoor, H., & Koo, I. (2017). CR-SDVN: A cognitive routing protocol for software-defined vehicular networks. IEEE Sensors Journal, 18(4), 1761–1772.
Jang, I., Choo, S., Kim, M., Pack, S., & Dan, G. (2017). The software-defined vehicular cloud: A new level of sharing the road. IEEE Vehicular Technology Magazine, 12(2), 78–88.
Correia, S., Boukerche, A., & Meneguette, R. I. (2017). An architecture for hierarchical software-defined vehicular networks. IEEE Communications Magazine, 55(7), 80–86.
Pathak, S., & Jain, S. (2019). A priority-based weighted clustering algorithm for mobile ad hoc network. International Journal of Communication Networks and Distributed Systems, 22(3), 313–328.
Saeed, Y., Ahmed, K., Zareei, M., Zeb, A., Vargas-Rosales, C., & Awan, K. M. (2019). In-vehicle cognitive route decision using fuzzy modeling and artificial neural network. IEEE Access, 7, 20262–20272.
Ku, I., Lu, Y., Gerla, M., Gomes, R. L., Ongaro, F., & Cerqueira, E. (2014). Towards software-defined VANET: Architecture and services. Med-Hoc-Net, 103–110.
Li, G., Gao, T., Zhang, Z., & Chen, Y. (2017). Fuzzy logic load-balancing strategy based on software-defined networking. In International wireless internet conference (pp. 471–482).
Budugutta, S., & Sampath, N. (2017). Intrusion detection using fuzzy logic in software defined networking. In International conference on intelligent computing systems (pp. 102–108).
Daeinabi, A., Rahbar, A. G. P., & Khademzadeh, A. (2011). VWCA: An efficient clustering algorithm in vehicular ad hoc networks. Journal of Network and Computer Applications, 34(1), 207–222.
Ren, M., Khoukhi, L., Labiod, H., Zhang, J., & Veque, V. (2017). A mobility-based scheme for dynamic clustering in vehicular ad-hoc networks (VANETs). Vehicular Communications, 9, 233–241.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Maan, U., Chaba, Y. Accurate Cluster Head Selection Technique for Software Defined Network in 5G VANET. Wireless Pers Commun 118, 1271–1293 (2021). https://doi.org/10.1007/s11277-021-08072-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08072-4