Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning
This addresses the problem of ensuring timely status updates for connected vehicular applications, which is incremental as it applies an existing method (DQN) to a new domain with specific data.
The paper tackles optimizing Age-of-Information (AoI) in fog computing-supported vehicular networks by proposing a Deep Q-learning algorithm to decide optimal driving routes, resulting in significant improvement in AoI confidence for various services.
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Netwrok (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.