An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs. (7th October 2022)

• Record Type: Journal Article
• Authors: Husnain, Ghassan; Anwar, Shahzad; Shahzad, Fahim; Sikander, Gulbadan; Tariq, Rehan; Bakhtyar, Maheen; Lim, Sangsoon
• Other Names: Pathak Akhilesh Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-10-07
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/6790082
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : In recent years, intelligent vehicles with cutting-edge vehicular applications have grown in popularity, enabling the growth of Vehicular Ad hoc Networks (VANETs). Vehicular Ad hoc Networks (VANETs) are a network of vehicles that share and analyze real-time data and require a well-organized and efficient data delivery method. The stability of clusters and dynamic topology change in VANETs are the major issues in finding an optimal route amongst the vehicles. The cooperative approach and surprise pounce chasing technique of Harris Hawks in nature serve as the main sources of inspiration for Harris Hawks Optimization. In this technique, several hawks work together to attack a victim from various angles to surprise it. Due to the unpredictable nature of situations and the prey's fleeing movements, Harris Hawks can exhibit a variety of intelligent strategies. This study proposes a novel route clustering optimization technique that takes into account communication range, the number of nodes, velocity, orientations, and grid size. To create and evaluate ideal cluster head (CH), the proposed method is based on Harris Hawks Intelligent Optimization Algorithm for route Clustering (iCHHO) which finds optimal and reliable routes amongst the vehicles. Other state-of-the-art methods, such as the Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimization (GWO), and Whale Optimization Algorithm (WOACNET), are utilized to evaluate and validate the proposed method. Our findings show that the developed method outperforms other current methods in terms of number of clusters, variable communication ranges, network size, and the number of vehicles. Furthermore, the statistical analysis concludes that the proposed method improves cluster optimization by 79% and increases cluster stability by an adjusted R -squared of 91.22.