# Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning

arXiv: https://arxiv.org/abs/2004.04640 · HTML: https://mlsanity.com/paper/2004.04640/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach](https://mlsanity.com/paper/2402.12260/) (2024)
- [A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks](https://mlsanity.com/paper/2405.07033/) (2024)

## Abstract (arXiv)

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.