Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation

Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks (VANETs) for smart transportation that results from dynamic topology, limited resources and non-centralized architecture. The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation. To design a robust clustering algorithm, careful attention must be paid to components like mobility models and performance objectives. A clustering algorithm may not perform well with every mobility pattern. Therefore, we propose a supervisory protocol (SP) that observes the mobility pattern of vehicles and identifies the realistic Mobility model through microscopic features. An analytical model can be used to determine an efficient clustering algorithm for a specific mobility model (MM). SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations. The simulation has performed in three parts that is the central part simulation for setting up the clustering environment, In the second part the clustering algorithms are tested for efficiency in a constrained atmosphere for some time and the third part represents the proposed scheme. The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count, re-affiliation rate, control overhead and cluster lifetime.