Reinforcement Learning-based Time-Dependable Modelling of Fog Connectivity for Software-Defined Vehicular Networks

Abstract

Connected vehicles are crucial in strengthening vehicular and Intelligent Transport Systems (ITS) by enabling autonomous and dynamic data sharing across the vehicular network. Extensive research has been conducted to predict connectivity, alongside thedevelopment of diverse techniques to manage this essential aspect. In recent times, learning methodologies have become increasingly popular for their ability to effec-tively handle sophisticated models adaptively. Various machine learning algorithms have been demonstrated as convincing methods for rendering any system flexible andpredictive. We thus propose a Learning based Adaptive Connectivity Estimation Model LACM. This model calculates and enhances the connectivity among differentstates and actions, monitoring their changes over time. The purpose of this model is to accurately depict the current connectivity status and predict potential fluctuations in fog connectivity. This model will utilize networking and vehicular characteristics to make the accuracy of its predictions. The design of this model aims to tackle the complexity of the problem by incorporating detailed data into a large state space representation, thereby enhancing adaptability. The second part of our work proposes a Time Dependent Connectivity Estimation Model, TDCM. Incorporating time dependency in the model helps to forecast the alterations in cluster lifestyles. It shows the progression of cluster evolution, significantly contributing towards achieving a stable and reliable network. Utilizing Long Short-Term Memory within an RL-based framework enables the system to enhance decision-making accuracy through predictions related to connectivity and network maintenance. Extensive analysis conducted through realistic simulations demonstrated that both LACM and TDCM strongly support estimating and maintaining stable connectivity over time. Our evaluation compared a previous state-of-the-art approach, showing that LACM and TDCM consistently enhanced the connectivity within the network