An Enhanced Vehicle to Cloud Communication by Prediction Based Machine Learning Approaches

Vehicle utilization as mobile sensor functions like as a catalyst for most of dense vehicle sensing based application like intelligent traffic control and heterogeneous vehicular networks. Moreover, extensive rise in Machine based communication (MC) extremely stress capacities of network infra-structure. With restricted resources in systems in networks and resource availability amongst mobile users and MC, huge amount of resource effectual channel accessing approaches are needed to enhance co-existence of diverse communication entities. In this investigation, a novel Machine learning (ML) based transmission strategy for user side opportunistic transmission is presented. Considering channel prediction characteristics and measured channel state, delay tolerance of vehicles are carried out to show resource efficiency. Anticipated model is measured with MATLAB in Long Term Evolution (LTE) networks; here the mean data rate is raised with the reduction of power consumption.