QOS enabled data dissemination in hierarchical VANET using machine learning approach

Abstract

Vehicular ad hoc networks (VANETs) are a collection of vehicular nodes that perform as a mobile hosts form a temporary network without the aid of any centralised infrastructure, so it is a sub-class of ad hoc network. It ensures the quality of service (QoS) for different VANET applications. Although it provides the QoS services to the process, mobility and routing play an important challenge in the VANET environment. So, different researches have revealed that the hierarchical routing schemes have numerous benefits over the traditional ones. Stable cluster formation and maintenance with the guarantying QoS in intra-cluster communications has always remained as a great challenge. For overcoming this issue, this paper proposes a QoS enabled data dissemination using an improved Kruskal's algorithm to provide efficient data dissemination and QoS in hierarchical VANET. This approach constructs the minimum spanning trees using Kruskal's algorithm in every road segment, where the vehicle has been clustered using the fuzzy c-means clustering method by considering the intra-cluster QoS. Each spanning tree will have a cluster head that is responsible to collect the data from the leaf nodes and disseminates the data to other coordinator nodes and vice versa. The simulation results show that the proposed approach performs better than the existing routing approach in terms of delay, throughput and packet loss.