Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments


The term "vehicular ad hoc networks," or "VANETs," is becoming more common to refer to the networks that are being developed to support a variety of applications for "smart cities" and "intelligent transportation systems." When it comes to ensuring that Communications over VANETs are reliable and stable, there are several challenging factors to consider. VANETs clustering is an essential functionality that enables reliable VANETs and serves as a foundation for routing protocols. Clustering algorithms for VANETs operate in a decentralized mode, which necessitates the incorporation of additional stages before deciding the clustering decisions. Furthermore, due to the local nature of the decentralized approach, there is a possibility that sub-optimality will be created. In addition, the complicated architecture of the road environment can result in decisions regarding clustering that are unclear. The ever-changing characteristics of clusters in VANETs, in general, and in 3D VANETs, in particular, make this problem considerably more difficult to solve. Using a centralized clustering method to create a Cauchy density model, the authors of this paper make an effort to solve the issue of VANETs in three-dimensional road environments. The model has been simulated by taking into account a variety of simulation parameters, such as traffic, mobility, driving behavior, and the curvature of the road. A adjacency list that specifies the points and segments of the road that are in a straight line are also included in the simulator. The clustering technique that the Cauchy density model employs is responsible for determining the mobility vector in order to make it possible to add vehicles to their respective clusters. The simulator has been built in MATLAB so that it can perform complex scenarios in three different locations of three-dimensional road environments. A comparison with certain benchmarks demonstrates that our model is superior to the models that are used as benchmarks, as our model achieves an improvement percentage of 1%, 10%, and 3%, respectively, for the average duration of the cluster head, the average duration of the cluster members, and the efficiency with which the clusters are formed.

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