SARSA RL for Edge Connectivity Management in Vehicular Edge Networks

Vehicular network connectivity within Intelligent Transport Systems (ITS) is essential for enabling seamless data and resource sharing, including transmitting critical safety messages, traffic management information, entertainment, and comfort services. This connectivity enhances the user experience by supporting complex interactions between vehicles and infrastructure in dynamic network environments. Edge connectivity management has recently gained attention for maintaining connection stability while managing complex models effectively. In this context, connectivity refers to vehicles' ability to maintain a stable and robust link with network resources and other vehicles for optimal data exchange. In this paper, we propose an edge connectivity management approach, the Edge Connectivity Estimation Model ECEM, aimed at ensuring connection stability and strength. We design and implement the SARSA Reinforcement Learning (RL) algorithm to assess and estimate the overall connection reliability, determining the optimal vehicular edge - a selective combination of vehicles within groups - to ensure superior connection strength for data and resource sharing, even in high-mobility scenarios. This estimation process helps identify the most suitable edge to meet the data-sharing requirements for each vehicle. Our approach considers multiple parameters, including mobility, application parameters, and network density. Extensive realistic simulations have demonstrated that our proposed approach outperforms existing methods by reducing packet loss and delay while increasing throughput.