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

Abstract:

To avoid connection failure, vehicular ad hoc networks need reliable clustering. A stable cluster head (CH) eliminates packet delay (PD) and maintains network throughput. This article introduces a two-step stable CH selection scheme.

In the first part of the proposed scheme, the vehicle network is a one-to-many connection network, which is realistic. New vehicular-hypergraph-based spectral clustering model generates clusters. Second, the CH is chosen to maintain a stable connection with the most neighbors.

The new rewarding/penalising relative speed and neighborhood degree meet the requirement. Eccentricity places the vehicle at the center of the cluster. CH selection uses another deep learning spectrum sensing metric. Deep learning-trained spectrum sensing calculates trust.

Layers of long short-term memory identify the primary vehicle in noisy and noiseless environments. The primary vehicle’s spectrum-vacating vehicle receives a high trust score.

These metrics select a stable CH to reduce overhead from CH switching between vehicles. Improved CH stability, CM lifetime, and CH rate of change support this. The proposed scheme also boosts PD and throughput.

Keywords: Clustering Algorithms, Vehicular Ad Hoc Networks, Stability Analysis, Measurement, Topology, Sensors, Network Topology, Deep Learning (Artificial Intelligence), Graph Theory, Mobile Radio, Radio Spectrum Management, Recurrent Neural Nets, Telecommunication Network Reliability, Telecommunication Network Topology, Vehicular Ad Hoc Networks

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