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

Introduction With the rapid evolution of vehicular networks, delay-sensitive applications such as autonomous driving and real-time navigation have gained significant attention. However, vehicles with limited computational resources often fail to meet low-latency demands, creating a critical bottleneck in system performance. Methods To address this challenge, we propose the use of Mobile Edge Computing (MEC) for offloading time-sensitive tasks to roadside servers, reducing response times through localized processing and network intelligence. A mathematical model based on Markov Chains was developed to capture traffic dynamics and response behaviors in vehicular environments. This model enables analytical performance evaluation without relying on time-consuming simulations. Results Experimental evaluation shows that the analytical model provides predictions consistent with simulation outcomes. Specifically, for vehicle counts ranging from 20 to 120, the model estimated average response times between 0.023–0.505 s, closely matching the simulation baseline of 0.019–0.482 s. The relative error was between 3.4% and 24.0%, with a mean of 11.7%. Discussion The results validate the practicality and effectiveness of the proposed approach in meeting delay-sensitive requirements. Beyond vehicular networks, the modeling framework can also be extended to support smart campus initiatives, including mobility solutions, IoT management, and intelligent infrastructure systems, highlighting its broader applicability in advancing smart technologies.