Enhanced Stability in Vehicular Ad Hoc Networks: A Dual-Layer Approach for Cluster Head Selection Using Hypergraph Spectral Clustering and Deep Learning

The present study discuss about Hypergraph Clustering Model (HGCM) for More Stable Urban Scenario in VANET. VANETs have a highly dynamic topology, with a high relative speed of vehicles and frequent discontinuity in the networks. The topology management in VANETs can be achieved through clustering. A vehicular ad hoc network is a dynamic and constantly changing topology that requires reliable clustering to prevent connection failure. Packet delay (PD) is avoided via a stable cluster head (CH), which also keeps network throughput high. This chapter introduces a revolutionary two-step method for stable CH selection. The vehicle network is regarded in the first section of the suggested scheme as a one-to-many connection network, which is close to a realistic situation. A recently proposed spectral clustering methodology based on vehicle hypergraphs is used to handle the cluster creation. In the second part, the CH is selected considering the criteria for maintaining a stable connection with the maximum number of neighbours. The new rewarding/penalising relative speed and neighbourhood degree fulfil the condition. Eccentricity assesses that the vehicle should be at the centre of the cluster. Another metric with deep learning spectrum sensing is introduced for CH selection. Trust calculation is performed using deep learning-trained spectrum sensing as a model. The primary vehicle in noisy and noiseless environments is recognised using layers of long short-term memory. A high trust score is awarded to the vehicle that vacates the spectrum in the sensing of the primary vehicle. The stable CH selected by these metrics reduces the overhead that occurs due to the frequent shifting of the CH from one vehicle to another. The stable CH improves the routing parameters, such as PD and throughput. These parameters are distance-dependent. The minimum distance travelled by the packet leads to low PD and high throughput. This has been validated by the improved CH stability; increased cluster member (CM) lifetime and reduced rate of change of CH. The proposed scheme also demonstrates a considerable improvement in PD and throughput. The analysis of the algorithm’s computational complexity will also be a benchmark of study in the next part of this work. CH stability decreases with the increase in vehicle density in the network.