Vehicular Ad Hoc Networks Assisted Clustering Nodular Framework for Optimal Packet Routing and Scaling

Wireless communication between moving cars and stationary structures is made possible by Vehicular Ad HocNetworks (VANETs).The goal is to communicate traffic data so that accidents can be avoided and resources can be used most effectively in current traffic conditions.There are several methods for enhancing VANETs' communicative efficacy; one is clustering in-vehicle networks.One CH assigned to each cluster and is in charge of the cluster as a whole.The CHs are responsible for all communications, both those between clusters and those within a single cluster.Vehicles in this study are organized into groups called clusters and information is relayed from one CH to another.Several different routing algorithms may be used to send data from one vehicle to another to improve the network's performance as a whole.Many reliable and safe routing systems for VANETs have been presented in the past decade.These protocols have several drawbacks, including their complexity, inability to scale to extensive networks, increased transportation costs, etc.Several bio-inspired strategies for optimal packet routing among vehicle nodes have been proposed to overcome these restrictions.Hence, this paper presents the efficient optimization of vehicular ad hoc networks assisted by a clustering nodular [EO-CN] framework to solve the abovementioned issues.The proposed method drastically reduced network overhead in settings with varying densities of nodes.Numerous experiments were conducted with various parameters, including cluster size, network area, node density, and transmission distance.These findings demonstrated that [EO-CN] performed better than competing approaches.