Modeling and analysis of response time in vehicular networks using Markov chains

The rapid growth of vehicular networks has led to the emergence of delay-sensitive software such as automatic driving and automatic navigation. Vehicles with limited resources cannot provide the required service quality to reduce the processing delay, which is a bottleneck in the development of vehicle networks. In this study, we utilize Mobile Edge Computing (MEC) to offload certain time-critical operations to servers located on roadsides. This approach, coupled with network intelligence, enhances the processing capability of mobile nodes and consequently, results in a decrease in response time latency. With the mathematical modeling of traffic in vehicular networks, we made it possible for the stakeholders of this field without the need to simulate their ideas, just by placing the desired values in the introduced mathematical model, a suitable picture of the changes related to getting your idea on the net. In the experiment, using the data of the state-of-the-art study, we calculated the average response time by mathematical modeling. The output of our work shows relatively similar results in the average network response time, indicating that we have achieved results close to those obtained from other expensive and time-consuming methods using the mathematical approach presented, which we consider an accomplishment in this paper.