Efficient Leader Head Selection In Vanet By Minimizing Message Overhead

Abstract

Vehicular Ad-hoc Networks (VANETs) require efficient leader head selection algorithms to optimize network management tasks and communication overhead In this paper, we offer a proactive approach technique to reduce message overhead while retaining effective network performance for leader head selection in VANETs. To create clusters and choose leader heads, the system makes use of predictive models of vehicle motion and communication patterns. The proposed technique achieves better scalability, decreased communication costs, and improved network performance by minimizing the number of messages exchanged and optimizing cluster formation. The algorithm's success in decreasing message overhead and ensuring effective leader head selection in VANETs is demonstrated by simulation results. Vehicle-to-vehicle (V2V) communications have rapidly advanced in recent years, opening the door for new applications tackling concerns like autonomous driving, traffic efficiency, and vehicle safety. These applications may greatly enhance the driving experience, travel time, fuel efficiency, traffic safety, and other elements directly connected to automobiles. In many of these circumstances, a coordinator vehicle is necessary to coordinate the right-of-way among many cars. Such a coordinator or leader vehicle is essential in many situations where a cooperative goal is desired for all cars in the group. Effective leader head selection algorithms are crucial in the context of vehicular ad hoc networks (VANETs) for optimizing network management activities and reducing communication overhead. The proactive approach strategy that is suggested in this study attempts to lower message overhead while maintaining efficient network performance in VANETs. To generate clusters and choose leader heads, the method makes use of predictive models of vehicle movements and communication patterns. The suggested method delivers higher scalability, lower communication costs, and increased network performance by utilizing predictive models. This is accomplished by optimizing cluster formation and reducing the volume of communications transferred during the leader-head selection process. To choose the best leader, the system considers a number of variables, including vehicle velocity, proximity, connection, and communication patterns. The algorithm can efficiently divide the leadership position among cars in a way that improves network performance and lowers communication costs by taking these aspects into account. Results from simulations show how the suggested technique is successful in lowering message overhead and guaranteeing efficient leader head selection. The simulations illustrate how the algorithm can build effective clusters and choose appropriate leader heads, improving throughput, latency, and reliability across the network. The algorithm's proactive strategy and implementation of predictive models let it adapt effectively to VANETs' dynamic nature, where cars are continually moving and entering/leaving the network. The development of vehicle-to-vehicle (V2V) communications has created new possibilities for applications relating to autonomous driving, traffic efficiency, and vehicle safety in a more general sense. The existence of a coordinator or leader vehicle is essential in many of these scenarios in order to promote coordination and collaboration among several vehicles. In order to enable smooth coordination and collaboration inside VANETs, the suggested leader head selection algorithm solves this need by effectively recognizing and choosing leaders' heads. Overall, the proactive strategy and technique based on predictive modeling described in this study provide solutions that have promise for lowering message overhead, improving cluster formation, and ensuring efficient leader head selection in VANETs. These developments have the potential to improve a number of directly linked features of vehicles and their interactions in V2V communication scenarios, including the driving experience, journey duration, fuel efficiency, traffic safety, and others.