A Headway to QoS on Traffic Prediction over VANETs using RRSCM Statistical Classifier

Abstract

In this paper, a novel throughput measurement forecast model is recommended for VANETs. The model is based on a statistical technique adopted and deployed over a high speed IP network traffic. Network traffic would always experience more QoS (Quality of Service) issues such as jitter, delay, packet loss and degradation due to very low bit rate codification too. Despite of all such dictated issues the traffic throughput is to be predicted with at most accuracy using a proposed multivariate analysis scheme represented as a RRSCM (Refined Regression Statistical Classifier Model) that optimizes parting parameters. Henceforth, the focus is towards the measurement methodology that estimates the traffic parameters that triggers to predict the accurate traffic and extemporize the QoS for the end-users. Finally, the proposed RRSCM classification model’s end-results are compared with the ANN (Artificial Neural Network) classification model to showcase its better act on the projected model