In VANET, a novel Hypergraph Clustering Model (HGCM) for Urban Scenarios is presented.

PROJECT TITLE :

A novelty of Hypergraph Clustering Model (HGCM) for Urban Scenario in VANET

ABSTRACT:

A vehicular ad hoc network is characterized by a dynamic topology that is in a state of constant change and calls for reliable clustering in order to avoid connection failure. A reliable cluster head (CH) eliminates the potential for packet delay (PD) and ensures that the network's throughput remains high. In this article, a novel method for stable CH selection is presented that has two parts. In the first part of the proposed scheme, the vehicle network is thought of as a one-to-many connection network, which is close to a scenario that could actually happen in the real world. A vehicular-hypergraph-based spectral clustering model has recently been proposed, and it is being used to manage the cluster generation process. In the second stage of the process, the CH is chosen with consideration given to the criteria of preserving a reliable connection with the greatest possible number of neighbors. The condition is satisfied by the newly implemented rewarding/penalising relative speed as well as the neighborhood degree. The eccentricity test determines whether or not the vehicle in question should be located in the middle of the cluster. For the purpose of CH selection, a new metric based on Deep Learning spectrum sensing has been introduced. As a model, trust calculation is carried out using spectrum sensing that has been trained with Deep Learning. Layers of long-term and short-term memory are utilized in order to identify the primary vehicle in both noisy and noiseless environments. A high trust score is given to the vehicle that moves out of the way of the primary vehicle's sensing while it is performing spectrum analysis. The consistent CH that is chosen by these metrics helps to cut down on the overhead that is incurred as a result of the frequent transfer of the CH from one vehicle to another. This has been demonstrated by a decrease in the rate of change of CH, as well as an improvement in the lifetime of cluster members (CM) and increased CH stability. A significant increase in both PD and throughput can be seen as a result of implementing the suggested plan.

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