Abstract
A VANET or vehicular Ad Hoc Network is known for its fast topology transition and node mobility, contributing to its attributes as an ad hoc network. The aspect of gathering the nodes, making this system extremely vigorous is known as clustering. However, in certain cases, it is not possible to keep track of the nodes which will results in network issues due to energy insufficiency during execution. Hence this will lead to primary energy management problems faced during the routing protocol which take into consideration the node lifetime. To address this discrepancy, we have proposed a novel optimization technique based on clustering. It has been observed that the proposed methodology will further improve the effectiveness of V2V communication. In this paper, clustering of the vehicle nodes is done using K-Medoid clustering model and are then used to improve energy efficiency. A metaheuristic algorithm is used to establish an energy efficient communication methodology. Based on the simulation analysis performed, it is seen that this methodology requires lesser execution time and improves the nodes' energy efficiency.
References
- Shilaja, C., & Arunprasath, T. (2019). Internet of medical things-load optimization of power flow based on hybrid enhanced grey wolf optimization and dragonfly algorithm. Future Generation Computer Systems, 98, 319-330.
- Wang, Y., Wang, M., Shen, L., Sun, X., Shi, G., Ma, W., & Yan, X. (2018). High-performance flexible surface-enhanced Raman scattering substrates fabricated by depositing Ag nanoislands on the dragonfly wing. Applied Surface Science, 436, 391-397.
- Rahman, C. M., & Rashid, T. A. (2019). Dragonfly algorithm and its applications in applied science survey. Computational Intelligence and Neuroscience, 2019.
- Stevani, C. V., de Faria, D. L., Porto, J. S., Trindade, D. J., & Bechara, E. J. (2000). Mechanism of automotive clearcoat damage by dragonfly eggs investigated by surface enhanced Raman scattering. Polymer Degradation and Stability, 68(1), 61-66.
- Kadoya, T., Suda, S. I., & Washitani, I. (2004). Dragonfly species richness on man-made ponds: effects of pond size and pond age on newly established assemblages. Ecological Research, 19(5), 461-467.
- Karunakaran, V. (2019). a stochastic development of cloud computing based task scheduling ALGORITHM. Journal of Soft Computing Paradigm (JSCP), 1(01), 41-48.
- Hu, Z., & Deng, X. Y. (2014). Aerodynamic interaction between forewing and hindwing of a hovering dragonfly. Acta Mechanica Sinica, 30(6), 787-799.
- Halberstadt, A. L., Chatha, M., Stratford, A., Grill, M., & Brandt, S. D. (2019). Comparison of the behavioral responses induced by phenylalkylamine hallucinogens and their tetrahydrobenzodifuran (“FLY”) and benzodifuran (“DragonFLY”) analogs. Neuropharmacology, 144, 368-376.
- Yıldız, B. S., & Yıldız, A. R. (2019). The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components. Materials Testing, 61(8), 744-748.
- Sureshkumar, K., & Ponnusamy, V. (2019). Power flow management in micro grid through renewable energy sources using a hybrid modified dragonfly algorithm with bat search algorithm. Energy, 181, 1166-1178.
- Manoharan, S. (2019). A smart image processing algorithm for text recognition information extraction and vocalization for the visually challenged. Journal of Innovative Image Processing (JIIP), 1(01), 31-38.
- Rahimunnisa, K. (2019). Hybrdized genetic-simulated annealing algorithm for performance optimization in wireless adhoc network. Journal of Soft Computing Paradigm (JSCP), 1(01), 1-13.
- Senthilkumar, M., Kavitha, V. R., Kumar, M. S., Raj, P. A. C., & Shirley, D. R. A. (2021, March). Routing in a Wireless Sensor Network using a Hybrid Algorithm to Improve the Lifetime of the Nodes. In IOP Conference Series: Materials Science and Engineering (Vol. 1084, No. 1, p. 012051). IOP Publishing.
