Position-Based Adaptive Clustering Model (PACM) for Efficient Data Caching in Vehicular Named Data Networks (VNDN)

Abstract

The seamless data delivery is essential in VANET for application such as autonomous vehicle, intelligent traffic management and for the road safety and emergency applications. The incorporation of named data networking (NDN) with VANET, intended to frame intelligent traffic flow and seamless data delivery. Such integration of vehicular ad hoc networks (VANET) with NDN is termed as vehicular named data networks (VNDN). Because of the continuous node/vehicle mobility, it is a tedious process to build constant and consistent communication between vehicles. With that concern, for enhancing the performance of VNDN and solving the issues such as frequent cluster formation on heavy loaded data transmissions, position-based adaptive clustering model (PACM) is developed. The major intention of PACM is to form clusters based on trajectory. Besides, PACM performs efficient data caching by collecting significant data from vehicles to establish consistent data communication with all nodes in the network. Efficient data caching is done with the elected cluster heads among the framed clusters based on its positions and mobility models. For handling the vehicles at higher mobility speed, mutual data caching process is also designed that makes vehicles to perform on-demand data gathering from cluster heads. Further, the model is simulated and the obtained results are compared with the existing models based on the metrics such as packet delivery ratio, mean delay, cache hit rate and mean hop distance. The comparative analysis shows that the proposed model outperforms the available techniques.