Abstract

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.

Highlights

  • Vehicular ad-hoc network (VANET) is a mobile ad-hoc network (MANET) in which communication is done among vehicles

  • VANET is an important component of the intelligent transportation system (ITS) [1], where vehicles contain wireless transceivers having different communication modes, namely, vehicle to vehicle (V2V) [2], vehicle to infrastructure (V2I) [3], and vehicle to anything (V2X) [4]

  • Results and Discussion equation shows how to calculate Gf function of gravitational force: The results obtained from the preliminary analysis of all techniques with network size equal

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Summary

Introduction

Vehicular ad-hoc network (VANET) is a mobile ad-hoc network (MANET) in which communication is done among vehicles. Clustering a network is a process of dividing it into small logical groups This process is based on different parameters, for example, internode distance and communication link capacity, to optimize overall network performance. In passive clustering aided routing protocol for vehicular ad-hoc networks (PassCAR) [16] proposed by Wang, this of method utilizes a passive mechanism, a technique where information of control. This studyproposed proposedan analgorithm algorithm for for vehicular vehicular node optimized thethe process of of clustering based onon several parameters (nodes direction, network area, communication process clustering based several parameters (nodes direction, network area, communicationlink capacity, node density, and transmission, range, etc.).

Literature
Intelligent Clustering via GOA
GOA Pseudo Code
Fitness Function used in GOA
The number of of clusters for11Km
The of Clusters
The number of clusters
Conclusion
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